< Dongguk Open Databases & CNN Model
>
---------------------------------------------
63. Dongguk Computer-Aided
Framework to Diagnose Tuberculosis from Chest X-Ray Images
62. Dongguk RPS-Net based
retinal pigment sign detection model (DRPM) with Algorithms
61. Dongguk
DenseNet-based Finger-vein Recognition Model (DDFRM) with Algorithms
60. CNN model for Thermal
Reflection Removal
59. Synthesized Low Light
Cambridge-driving Labeled Video Database (Syn-CamVid), Synthesized Low Light
Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago
(Syn-KITTI) database, and Algorithm Including CNN Models
58. Dongguk Drone Motion Blur
Dataset - Versions 1 and 2 (DDBD-DB1 and DDBD-DB2) & Pretrained Models
57. Dongguk X-RayNet Model with Algorithms (DXM)
56. Dongguk Mitotic Cell
Detection Models (DMM)
55. Dongguk CNN Models for Fake Banknote Image Classification Using
Visible-Light Images Captured by Smartphone Camera
54. Dongguk mobile finger
wrinkle database versions 1 and 2 (DMFW-DB1 and DMFW-DB2), and GAN with CNN
models for motion deblurring
53. Enhanced Ultrasound Thyroid Nodule Classification (US-TNC-V2)
Algorithm
52. Dongguk generation model of presentation attack face images
(DG_FACE_PAD_GEN)
51. Dongguk Spatiotemporal
Features-Based Classification Network (DenseNet+LSTM) to Classify the Multiple
Gastrointestinal Diseases with Including the Video Indices of Experimental
Endoscopy Videos
50. Dongguk Modified Conditional GAN &
Deep CNN Models, and Generated Images
49. Dongguk Super-resolution
Reconstruction & Age Estimation CNN Model (DSR&AE-CNN)
48. Dongguk ESSN models and
algorithm for Semantic Segmentation
47. Dongguk Mask R-CNN Model for Elimination of
Thermal Reflections, Generated Data, Dongguk Thermal Image Database (DTh-DB),
and Items and Vehicles Database (DI&V-DB)
46. Dongguk
Ultrasound Thyroid Nodule Classification (DUS-TNC) algorithm
45. Dongguk Modified CycleGAN for Age
Estimation (DMC4AE) and Generated Images
44. Dongguk Vess-Net Models with Algorithm
43. Dongguk CNN for Detecting Road Markings Based on Adaptive ROI with
Algorithms
42. Dongguk CNN stacked LSTM and CycleGAN for Action
Recognition, Generated Data, and Dongguk Activities & Actions Database
(DA&A-DB2)
41. Label Information of Sun Yat-sen University Multiple
Modality Re-ID (SYSU-MM01) Database and Dongguk Gender Recognition CNN Models
(DGR-CNN).
40. Dongguk cGAN-based Iris Image Generation Model and
Generated Images (DGIM&GI)
39. Dongguk CNN and LSTM models for the classification of
multiple gastrointestinal (GI) diseases, and video indices of experimental
endoscopic videos
38. Dongguk Dual-Camera-based
Gaze Database (DDCG-DB1) and CNN models with Algorithms
37. Dongguk Mobile
Finger-Wrinkle Database (DMFW-DB1) and CNN models with Algorithms
36. Dongguk low-resolution
drone camera dataset & CNN models
35. Dongguk CNN Model for
CBMIR
34. Dongguk Person ReID CNN
Models (DPRID-CNN)
33. Dongguk DenseNet-based
Finger-vein Recognition Model (DDFRM) with algorithms
32. Dongguk OR-Skip-Net Model
for Image Segmentation with Algorithm and Black Skin People (BSP) Label
Information
31. Dongguk Banknote Type and
Fitness Database (DF-DB3) & CNN Model with algorithms
30. Dongguk RetinaNet for
Detecting Road Marking Objects with Algorithms and Annotated Files for Open
Databases
29. Dongguk CNN Model for NIR
Ocular Recognition (DC4NO) with algorithm
28. Dongguk Face Presentation
Attack Detection Algorithms by Spatial and Temporal Information (DFPAD-STI)
27. Dongguk Dual Camera-based
Driver Database (DDCD-DB1) and Trained Faster R-CNN Model with Algorithm
26. Dongguk FRED-Net with Algorithm
25. Dongguk Face and Body
Database (DFB-DB1) with CNN models and algorithms
24. Dongguk Night-Time Face
Detection database (DNFD-DB1) and algorithm including CNN model
23. Dongguk Iris Spoof
Detection CNN Model version 2 (DFSD-CNN-2) with Algorithm
22. Dongguk Fitness Database (DF-DB2) & CNN Model
21. Dongguk-body-movement-based human identification
database version 2
(DBMHI-DB2) & CNN Model
20. Dongguk Multimodal Recognition
CNN of Finger-vein and Finger shape (DMR-CNN) with Algorithm
63. Dongguk Computer-Aided Framework to Diagnose Tuberculosis from
Chest X-Ray Images
(1) Introduction
We proposed a
novel deep learning-based computer-aided framework to diagnose Tuberculosis
from a given CXR image and provide the appropriate visual and descriptive
information from a previous database. Such information can further assist
radiologists to subjectively validate the computer decision. Thus, both
subjective and computer decisions will validate each other and ultimately
result in effective diagnosis and treatment.
(2) Request for Our
Model and Dataset Indices
To obtain our trained
model and the training and testing data splitting information, please fill the
request form below and send an email to Mr. Muhammad Owais at
malikowais266@gmail.com. Any work that uses the provided pretrained network
must acknowledge the authors by including the following reference.
Muhammad
Owais, Muhammad Arslan, Tahir Mahmood, Yu Hwan Kim, and Kang Ryoung Park, “Interpretable
Computer-Aided Framework to Diagnose Tuberculosis from Chest X-Ray Images with
Retrieval of Descriptive Evidence,” IEEE Transactions
on Medical Imaging, in submission.
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indices of experimental endoscopy videos. This model should not be used for
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62. Dongguk RPS-Net
based retinal pigment sign detection model (DRPM) with Algorithms
(1) Introduction
In this study, we proposed an accurate retinal pigment segmentation network (RPS-Net) that segment the pigment signs for diagnostic purposes. RPS-Net is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background.
(2) Request for Models
To gain access to our
databases and pretrained models with algorithm, Please sign and scan the
request form and email to Mr. Muhammad Arsalan at arsal@dongguk.edu. Any work
that uses our models, algorithm, and databases must acknowledge the authors by
including the following reference.
Muhammad
Arsalan, Na Rae Baek, Muhammad Owais, Tahir Mahmood, and Kang Ryoung Park,
"Deep Learning-based Detection of Pigment Signs for Analysis and Diagnosis
of Retinitis Pigmentosa ", Sensors, in submission.
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61. Dongguk
DenseNet-based Finger-vein Recognition Model (DDFRM) with Algorithms
(1) Introduction
In this study, we proposed a finger-vein recognition system based on score-level fusion method with shape and texture images. For extracting the matching score of each shape image and texture image, revised DenseNet-161 with composite image input is used. Finger-vein recognition models trained with our experimental databases in this study are made available to other researchers for a fair judgment on the performance.
(2) Request for Models
To get access to our pretrained models with algorithms please sign and scan the request form and send an email to Mr. Kyoung Jun Noh at nohkyungjun@dongguk.edu. Any work that uses our models and algorithm must acknowledge the authors by including the following reference.
Kyoung Jun Noh, Jiho Choi, Jin Seong Hong and Kang Ryoung Park, “Finger-vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion with Shape and Texture Images”, IEEE Access, in submission.
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60. CNN model for
Thermal Reflection Removal
(1) Introduction
We trained the CNN model with our thermal image database and an open database for the purpose of thermal reflection removal. In the proposed method, a region image and an original image are used as inputs to the CNN models. We made the models (pruned fully convolutional network (PFCN)) open to other researchers.
(2) Request for Models
To obtain our pretrained models please fill the request form below and send an email to Prof. Batchuluun at ganabata87@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.
Ganbayar Batchuluun, Na Rae Baek, Dat Tien Nguyen, Tuyen Danh Pham, and Kang Ryoung Park, “Region-based Removal of Thermal Reflection using Pruned Fully Convolutional Network”, IEEE Access, in submission.
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59. Synthesized Low
Light Cambridge-driving Labeled Video Database (Syn-CamVid), Synthesized Low
Light Karlsruhe Institute of Technology and Toyota Technological Institute at
Chicago (Syn-KITTI) database, and Algorithm Including CNN Models
(1) Introduction
We used synthesized databases that are similar to real low light environments to perform multi-class segmentation in low light environments. Images taken in real low light or nighttime environments have poor image quality and visibility due to low brightness, blur, and noise, making it difficult for humans to create segmentation labels for all the objects in the image and the labels are not accurate. Therefore, to utilize accurate segmentation labels and paired images, experiments were performed using the Syn-CamVid and Syn-KITTI databases, which are the results of converting the daytime CamVid and KITTI databases into low light images, respectively. To create extremely low light images similar to an actual low light environment with little external light, we have used the existing low light image generation methods in combination. In a real low light environment with little external light, the brightness value does not decrease linearly. When comparing the daytime image with the nighttime image, the brightness of highly bright pixels will decrease more, whereas that of the pixels with lower brightness will decrease less. We used gamma correction to produce this nonlinear brightness change. In a low light environment, blurry images are captured due to the amount of light and the camera’s exposure time, and we used the Gaussian blur kernel to implement this effect. Finally, the noise in the low light image is generated by the camera sensor, which is added in this experiment using the Gaussian and Poisson noise functions.
(2) Request for Our
Models and Algorithms
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datasets and pretrained models with algorithm, please sign and scan the request
form and email to Mr. Se Woon Cho at jsu319@dongguk.edu. Any work that uses our
models, algorithm, and databases must acknowledge the authors by including the
following reference.
Se Woon Cho, Na Rae Baek, Ja
Hyung Koo, Muhammad Arsalan, and Kang Ryoung Park, "Semantic Segmentation
with Low Light Images by Modified CycleGAN-based Image Enhancement", IEEE
Access, in submission.
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58. Dongguk Drone
Motion Blur Dataset - Versions 1 and 2 (DDBD-DB1 and DDBD-DB2) & Pretrained
Models
(1) Introduction
We used the Dongguk drone camera dataset ver.2 (DDroneC-DB2) open dataset to generate two datasets by two different methods, denoted as the synthesized motion blur drone database 1 (SMBD-DB1) and synthesized motion blur drone database 2 (SMBD-DB2). For the first dataset, the motion-blurred images were generated by applying the motion-blurring kernels, which are created by applying subpixel interpolation to the trajectory vector. Each trajectory vector, which is a complex-valued vector, corresponds to the discrete positions of an object undergoing 2D random motion in a continuous domain. For the second dataset, we synthesized dataset that contains realistic motion blur close to the motion blur in the wild. Specifically, we used a video frame interpolation model to increase the frame rate of DDroneC-DB2 videos from 30 to 120 FPS. Then, we generated blurred images by averaging consecutive frames on the generated high-frame-rate videos. With these two datasets, we performed proposed deblur CNN and marker detection CNN. We made our synthesized datasets and CNN models publicly available for fair comparison and result regeneration.
(2) Request for Our
Models and Algorithms
To gain access to our
datasets and pretrained models with algorithm, please sign and scan the request
form and email to Mr. Noi Quang Truong at noitq@dongguk.edu. Any work that uses
our models, algorithm, and databases must acknowledge the authors by including
the following reference.
Noi Quang Truong, Muhammad
Owais, Young Won Lee, and Kang Ryoung Park, "Deep Learning-based Motion
Deblurring and Marker Detection for Drone Landing", Sensors, in
submission.
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57. Dongguk X-RayNet Model with Algorithms (DXM)
(1) Introduction
In
this study, semantic segmentation based automatic cardiothoracic ratio (CTR)
estimation is proposed. CTR is important to diagnose cardiac and other related
diseases. The proposed method consists of two multiclass segmentation networks
(X-RayNet1 and X-RayNet2) to provide accurate boundary of chest anatomical
structures such as, lungs, heart and clavicle bones. The accurate boundary
segmentation of these anatomies helps to compute the CTR automatically, where
the CTR is considered as biomarker for cardiomegaly and other diseases. Three
publicly available datasets Japanese Society of Radiological Technology (JSRT),
Montgomery County (MC) and Shenzhen X-ray sets (SC) where used to evaluate the
performance of proposed network. The experimental results show that our method
outperforms the existing approaches and provide accurate boundary for CTR
computation. We made our models publicly available for fair comparison and
result regeneration. All the experiments are implemented in MATLAB R2019a.
(2) Request for Our
Models and Algorithms
To gain access to our
databases and pretrained models with algorithm, Please sign and scan the
request form and email to Mr. Muhammad Arsalan at arsal@dongguk.edu. Any work
that uses our models, algorithm, and databases must acknowledge the authors by
including the following reference.
Muhammad Arsalan,
Muhammad Owais, Tahir Mahmood, Jiho Choi, and Kang Ryoung Park,
"Artificial Intelligence-based Diagnosis of Cardiac and Related Diseases
", Journal of Clinical Medicine, in submission.
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algorithms. These should not be used for commercial use.
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56. Dongguk Mitotic Cell Detection Models (DMM)
(1) Introduction
In
this study, we proposed a multistage mitosis detection method based on Faster
region convolutional neural network (Faster R-CNN) and deep CNNs. Two open
datasets of breast cancer known as ICPR 2012 and MITOS-ATYPIA-14 are used. Our
proposed technique outperforms over the existing techniques. We made our models
publicly available to allow other researchers to regenerate our results and do
fair comparisons. All the experiments are implemented in MATLAB R2019a.
(2) Request for Our
Models and Algorithms
To gain access to our
databases and pretrained models with algorithm, Please sign and scan the
request form and email to Mr. Tahir Mahmood at tahirmahmood@dongguk.edu. Any
work that uses our models, algorithm, and databases must acknowledge the
authors by including the following reference.
Tahir Mahmood, Muhammad Arsalan,
Muhammad Owais, Min Beom Lee, and Kang Ryoung Park, "Artificial
Intelligence-based Mitosis Detection in Breast Cancer Histopathology Images
Using Faster R-CNN and Deep CNNs", Journal of Clinical Medicine, in
submission.
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55. Dongguk CNN Models for Fake Banknote Image
Classification Using Visible-Light Images Captured by Smartphone Camera
(1) Introduction
In
this study, we proposed a fake banknote classification method using CNN on
banknote images captured by smartphone cameras under visible-light conditions.
The fake banknote dataset used for the experiments in this study consists of
images of banknotes of three national currencies: EUR (EUR 5, EUR 10, EUR 20,
EUR 50, and EUR 100), USD (USD 1, USD 5, USD 10, USD 20, USD 50, and USD 100),
and KRW (KRW 1000, KRW 5000, KRW 10,000, and KRW 50,000). The fake banknotes were
created by capturing the original banknotes by scanner and smartphone cameras,
and printed out by color printer to make the reproduced banknote. We
subsequently captured banknote images by the same abovementioned smartphones
while holding the fake and genuine banknotes in front of cameras or placing
them on tables. The training process were conducted using the MATLAB
implementation of CNN with AlexNet, ResNet-18, and GoogleNet architectures.
(2) Request for Our
Models and Algorithm
To
gain access to these files, download the following request form. Please scan
the request form and email to Dr. Tuyen Danh Pham (phamdanhtuyen@dongguk.edu).
Any work that uses these files with algorithm must acknowledge the authors by
including the following reference.
Tuyen
Danh Pham, Chanhum Park, Dat Tien Nguyen, Ganbayar Batchuluun, and Kang Ryoung
Park, “Deep Learning-Based Fake-Banknote Detection Using
Visible-Light Images Captured by Smartphone Cameras,” IEEE
Access, in submission.
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54. Dongguk mobile finger wrinkle database versions 1 and 2
(DMFW-DB1 and DMFW-DB2), and GAN with CNN models for motion deblurring
(1) Introduction
To evaluate performance
using images captured by a variety of smartphone cameras, DMFW-DB2 used the
rear camera of a Samsung Galaxy S8+. Later, images were extracted from the
captured images at 30 fps, and the motion blurred images were captured by obtaining
the average images of the captured images. In addition, DMFW-DB1 (refer to “37”) is artificial blurred by motion blurring kernel.
This study used DeblurGAN to restore motion blurred images of DMFW-DB1 and
DMFW-DB2. The restored images obtained by DeblurGAN are
used as the input for a
ResNet-101 to perform the finger wrinkle recognition.
(2) Request for Our
Models, Algorithm, and Databases
To gain access to our
databases and pretrained models with algorithm, Please sign and scan the
request form and email to Mr. Nam Sun Cho at diko93@dongguk.edu. Any work that
uses our models, algorithm, and databases must acknowledge the authors by
including the following reference.
Nam Sun Cho, Chan Sik Kim, Chanhum Park, and Kang Ryoung Park, "GAN-based Blur Restoration for Finger Wrinkle Biometrics System", IEEE Access, in submission.
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53. Enhanced Ultrasound Thyroid Nodule Classification (US-TNC-V2)
Algorithm
(1) Introduction
In this study, we
enhance the classification performance of ultrasound image-based thyroid nodule
classification system. The pretrained model was successfully trained using TDID
dataset [1]
[1] Pedraza, L.;
Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero, E. An open access
thyroid ultrasound-image database. In Proceedings of the 10th International
Symposium on Medical Information Processing and Analysis, Colombia, 28 January,
2015 (in SPIE Proceedings, Vol. 9287, pp. 1-6).
(2) Request for Our
Models and Algorithm
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algorithm and pretrained models, Please sign and scan the request form and
email to Prof. D. T. Nguyen at nguyentiendat@dongguk.edu. Any work that uses
our algorithm must acknowledge the authors by including the following
reference.
D. T. Nguyen, et al.
"Ultrasound Image-based Diagnosis of Malignant Thyroid Nodule Using
Artificial Intelligence", Sensors, in submission.
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52. Dongguk
generation model of presentation attack face images (DG_FACE_PAD_GEN)
(1)
Introduction
We trained our generative adversarial network
(GAN)-based model to artificially generate presentation attack (PA) face images
to reduce the efforts of PA image acquisition.
(2) Request
for obtaining DG_FACE_PAD_GEN
To obtain our pretrained model, please fill
the request form bellow and send an email to Mr. Nguyen at
nguyentiendat@dongguk.edu. Any work that uses the provided pretrained network
must acknowledge the authors by including the following reference.
Dat Tien Nguyen, Tuyen Danh Pham, Ganbayar Batchuluun, Kyoung Jun Noh, and Kang Ryoung Park, “Presentation Attack Face Image Generation Based on Deep Generative Adversarial Network,” Sensors, in submission.
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51. Dongguk Spatiotemporal Features-Based Classification Network
(DenseNet+LSTM) to Classify the Multiple Gastrointestinal Diseases with
Including the Video Indices of Experimental Endoscopy Videos
(1) Introduction
We
trained a spatiotemporal features-based classification model (named
as DenseNet+LSTM) to classify the multiple gastrointestinal
diseases using endoscopic videos. Moreover, after performing the
classification, the extracted features were further used to retrieve images of
similar medical conditions, such as normal and abnormal cases, from a large
endoscopic database.
(2) Request for Our
Algorithm and Dataset Indicies
To obtain our trained
model with including the video indices of experimental endoscopy videos, please
fill the request form below and send an email to Mr. Muhammad Owais at
malikowais266@gmail.com. Any work that uses the provided pretrained network
must acknowledge the authors by including the following reference.
Muhammad
Owais, Muhammad Arsalan, Tahir Mahmood, Jin Kyu Kang and
Kang Ryoung Park, “Automated Diagnosis of Various
Gastrointestinal Lesions Using Deep Learning-Based Classification and Retrieval
Framework with Large Endoscopic Database,” Journal
of Medical Internet Research, In Submission.
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50.
Dongguk Modified Conditional GAN & Deep CNN Models, and Generated Images
(1) Introduction
We trained our modified conditional GAN & Deep CNN
Models for finger-vein optical blur restoration and finger-vein recognition by
databases of PolyU-DB [1] and SDU-DB [2]. We made our
trained models and generated images open to other
researchers.
[1] Kumar, A.; Zhou, Y. Human
identification using finger images. IEEE Trans. Image Process. 2012, 21, 2228–2244.
[2] SDUMLA-HMT Finger Vein Database.
Available online: http://mla.sdu.edu.cn/info/1006/1195.htm
(2) Request for Models
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images, download the following request form. Please fill the request form below
and send an email to Mr. Jiho Choi (choijh1027@dongguk.edu). Any work that uses our algorithm and models must
acknowledge the authors by including the following reference.
Jiho Choi, Kyoung Jun Noh, Se Woon Cho, Se Hyun
Nam, Muhammad Owais, and Kang Ryoung Park, “Modified Conditional
Generative Adversarial Network-based Optical Blur Restoration for Finger-vein
Recognition,” IEEE Access, in submission.
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49. Dongguk
Super-resolution Reconstruction & Age Estimation CNN Model (DSR&AE-CNN)
(1) Introduction
We trained our models (DSR&AE-CNN) for facial image super-resolution reconstruction and age estimation by databases of PAL [1] and MORPH databases [2].We made our trained models and generated images open to other researchers.
[1] PAL database Available online:
http://agingmind.utdallas.edu/download-stimuli/face-database/ (accessed on 17
May 2019).
[2] MORPH database Available
online: https://ebill.uncw.edu/C20231_ustores/web/store_main.jsp?STOREID=4
(accessed on 17 May 2019).
(2) Request for Models
To gain access to our models
and images, download the following request form. Please fill the request form
below and send an email to Mr. Se Hyun Nam (nsh6473@dongguk.edu).
Any work that uses our algorithm and models must acknowledge the authors
by including the following reference.
Se Hyun Nam, Yu Hwan Kim, Noi
Quang Truong, jiho Choi, and Kang Ryoung Park, “Age Estimation by Super-Resolution
Reconstruction Based on Adversarial Networks,” IEEE Access, in submission.
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48. Dongguk ESSN
models and algorithm for Semantic Segmentation
(1) Introduction
We propose new models (ESSN) for semantic segmentation. That proposed model was trained with open dataset, SBD [1] and CamVid [2]. We made our trained model and algorithm open to other researchers.
[1] S. Gould, R. Fulton, and D. Koller, “Decomposing a Scene into
Geometric and Semantically Consistent Regions,” in
Proc. IEEE Int. Conf. Comput. Vis., Kyoto, Japan, 29 Sep.-2 Oct. 2009, pp. 1-8.
[2] G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, “Segmentation and Recognition Using Structure from Motion Point
Clouds,” in Proc. European Conf. Comput. Vis.,
Marseille, France, 12-18 Oct. 2008, pp. 44-57.
(2) Request for Models
To obtain our pretrained
model, please fill the request form below and send an email to Mr. Dong Seop
Kim (seob2@dongguk.edu). Any work
that uses our algorithm and models must acknowledge the authors by including
the following reference.
DONG SEOP KIM, MUHAMMAD ARSALAN,
MUHAMMAD OWAIS, and KANG RYOUNG PARK, “ESSN: Enhanced Semantic Segmentation Network by
Residual Concatenation of Feature Maps,” IEEE Access, in submission.
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47. Dongguk Mask R-CNN Model for Elimination of Thermal Reflections,
Generated Data, Dongguk Thermal Image Database (DTh-DB), and Items and Vehicles
Database (DI&V-DB)
(1) Introduction
We trained the Mask R-CNN model with our thermal image database for the purpose of elimination of thermal reflections. We made the models, generated data with Dongguk thermal image database (DTh-DB), and Dongguk items & vehicles database (DI&V-DB) open to other researchers.
(2) Request for Models, Generated Data, and databases
To obtain our pretrained model, generated data, and databases, please fill the request form below and send an email to Prof. Batchuluun at ganabata87@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.
Ganbayar Batchuluun, Hyo Sik Yoon, Dat Tien Nguyen, Tuyen Danh Pham, and Kang Ryoung Park, “A Study on the Elimination of Thermal Reflections,” IEEE Access, Vol. 7, pp. 174597-174611, December 2019.
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46. Dongguk Ultrasound Thyroid Nodule
Classification (DUS-TNC) algorithm
(1) Introduction
In this study, we enhance the classification performance of ultrasound image based thyroid nodule classification system by cascading classifiers using FFT-based and CNN-based methods. The pretrained model was successfully trained using TDID dataset [1]. We made our trained model and algorithm open to other researchers
[1] Pedraza, L.; Vargas, C.; Narvaez, F.; Duran, O.; Munoz, E.; Romero,
E. An open access thyroid ultrasound-image database. In Proceedings of the 10th
International Symposium on Medical Information Processing and Analysis,
Colombia, 28 January, 2015 (in SPIE Proceedings, Vol. 9287, pp. 1-6).
(2) Request for our algorithm
To gain access to our algorithm (code and pretrained models), Please
sign and scan the request form and email to Prof. D. T. Nguyen at
nguyentiendat@dongguk.edu. Any work that uses our algorithm must acknowledge
the authors by including the following reference.
Dat Tien Nguyen, Tuyen Danh Pham, Ganbayar Batchuluun, Hyo Sik Yoon, and
Kang Ryoung Park, “Artificial
Intelligence-based Thyroid Nodule Classification Using Information from Spatial
and Frequency Domains,” Journal of Clinical
Medicine, Vol. 8, Issue 11(1976), pp. 1-24, November 2019.
< Request Form for DUS-TNC algorithm >
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contents must be completed). These models should not be used for commercial
use.
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45. Dongguk Modified CycleGAN for Age Estimation
(DMC4AE) and Generated Images
(1) Introduction
We trained our modified CycleGAN models for age estimation with heterogeneous databases of MegaAge and MORPH databases [1,2]. We made our trained models and generated images by modified CycleGAN open to other researchers.
1.
Y. Zhang, L. Liu, C. Li, and C.C. Loy,
Quantifying facial age by posterior of age comparisons, In Proceedings of
British Machine Vision Conference, London, UK, 4-7 September 2017; pp. 1-14.
2. K.
Ricanek and T. Tesafaye, Morph: A longitudinal image database of normal adult
age-progression, In Proceedings of 7th International Conference on
Automatic Face and Gesture Recognition, Southampton, UK, 10-12 April 2006; pp
341–345.
(2) Request for our models and images
To gain access to our models and images, download the following request form. Please sign and scan the request form and email to Mr. Yu Hwan Kim (taekkuon@dongguk.edu).
Any work that uses these models and images must acknowledge the authors by including the following reference.
Yu Hwan
Kim, Min Beom Lee, Se Hyun Nam, and Kang Ryoung Park, “Enhancing the Accuracies of Age Estimation with
Heterogeneous Databases Using Modified CycleGAN,” IEEE
Access, Vol. 7, pp. 163461-163477, November 2019.
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44. Dongguk Vess-Net Models with Algorithm
(1) Introduction
We trained our Vess-Net model based on dual stream feature empowerment scheme for retinal vessel segmentation to aid the process of diagnosing diseases like diabetic and hypertensive retinopathy. In our experiments we validated the performance of our method with three different publicly available fundus image databases including DRIVE [1] CHASE-DB1 [2] and STARE [3]. We made our trained models open to other researchers.
3.
Gastrolab Staal, J.; Abràmoff, M.D.; Niemeijer, M.;
Viergever, M.A.; van Ginneken, B. Ridge-based vessel segmentation in color
images of the retina. IEEE Trans. Med. Imaging, 2004, 23, 501–509.
4.
Fraz, M.M.; Remagnino, P.;
Hoppe, A.; Uyyanonvara, B.; Rudnicka, A.R.; Owen, C.G.; Barman, S.A. An
ensemble classification-based approach applied to retinal blood vessel
segmentation. IEEE Trans. Biomed. Eng. 2012, 59, 2538–2548
5. Hoover, A.; Kouznetsova, V.; Goldbaum, M. Locating blood vessels in
retinal images by piecewise threshold probing of a matched filter response.
IEEE Trans. Med. Image, 2000, 19, 203-210.
(2) Request for our Vess-Net models
To gain access to the Vess-Net trained models, download the following request form. Please sign and scan the request form and email to Mr. Muhammad Arsalan (arsal@dongguk.edu).
Any work that uses these Vess-Net models must acknowledge the authors by including the following reference.
Muhammad
Arsalan, Muhammad Owais, Tahir Mahmood, Se Woon Cho and Kang Ryoung Park, “Aiding the Diagnosis of Diabetic
and Hypertensive Retinopathy Using Artificial Intelligence-based Semantic
Segmentation,” Journal of Clinical Medicine, Vol. 8, Issue 9(1446), pp. 1-27, September 2019.
<
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43. Dongguk CNN for Detecting Road Markings Based on Adaptive ROI with
Algorithms
(1)
Introduction
We created adaptive ROI images before using
them to train our convolutional neural network (CNN). In the first stage, a vanishing point is detected in
order to create the ROI image. The ROI image that covers the majority of the
road region is then used as the input to train the CNN-based detector and
classifier in the second stage. We made the models, generated data, and
labeled information of database open to other researchers. Our CNN model was
trained with Malaga urban dataset
[1], the Daimler dataset [2], and the Cambridge dataset [3].
1. The Málaga Stereo and Laser
Urban Data Set –
MRPT. Available online: https://www.mrpt.org/MalagaUrbanDataset (accessed on 1
October 2018).
2. Daimler Urban Segmentation
Dataset.
Available online:
http://www.6d-vision.com/scene-labeling (accessed on 2 January 2019).
3. Cambridge-driving Labeled Video Database (CamVid). Available online: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/ (accessed on 1 October 2018).
(2) Request for models, generated data, and labeled information
To obtain our pretrained model, generated data, and labeled information, please fill the request form bellow and send an email to Dr. Toan Minh Hoang at hoangminhtoan@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.
Toan Minh Hoang, Se Hyun Nam,
and Kang Ryoung Park, “Enhanced
Detection and Recognition of Road Markings Based on Adaptive Region of Interest
and Deep Learning,”IEEE Access, Vol. 7, pp. 109817- 109832, August 2019.
< Request Form
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42. Dongguk CNN stacked LSTM and CycleGAN for Action Recognition,
Generated Data, and Dongguk Activities & Actions Database (DA&A-DB2)
(1) Introduction
We trained our convolutional neural network (CNN), CNN stacked with long short-term memory (CNN-LSTM), cycle-consistent adversarial network (CycleGAN) models with our action database. We made the models, generated data, and database open to other researchers.
(2) Request for Models, Generated Data, and DA&A-DB2
To obtain our pretrained model, generated data, and database, please fill the request form bellow and send an email to Dr. Batchuluun at ganabata87@dongguk.edu. Any work that uses the provided pretrained network must acknowledge the authors by including the following reference.
Ganbayar Batchuluun, Dat Tien Nguyen, Tuyen Danh Pham, Chanhum Park, and Kang Ryoung Park, “Action Recognition from Thermal Videos,” IEEE Access, Vol. 7, pp. 103893- 103917, August 2019.
< Request Form
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DA&A-DB2 >
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41. Label
Information of Sun Yat-sen University Multiple Modality Re-ID (SYSU-MM01)
Database and Dongguk Gender Recognition CNN Models (DGR-CNN).
(1) Introduction
We collected gender information of Sun Yat-sen University Multiple
Modality Re-ID (SYSU-MM01) database and trained gender recognition system based
on ResNet-101 using two databases including the SYSU-MM01 and the Dongguk
Body-based Gender Database (DBGender-DB2). We made label information of
SYSU-MM01 database and Dongguk Gender Recognition CNN (DGR-CNN) open to other
researchers.
(2) Request for Label Information and DGR-CNN
To gain access to the label information and DGR-CNN, download the following request form for label information of SYSU-MM01 and DGR-CNN. Please sign and scan the request form and email to Ms. Na Rae Baek (naris27@dongguk.edu).
Any work that uses the label information of SYSU-MM01 database or this CNN model must acknowledge the authors by including the following reference.
Na Rae Baek, Se Woon Cho, Ja Hyung Koo, Noi Quang Truong, and Kang Ryoung Park, “Multimodal Camera-based Gender Recognition Using Human-body Image with Two-step Reconstruction Network,” IEEE Access, Vol. 7, pp. 104025-104044, August 2019.
<
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40. Dongguk cGAN-based Iris Image Generation Model
and Generated Images (DGIM&GI)
(1) Introduction
We trained generation models based on cGAN (pix2pix model) using NICE.II
training dataset (selected from UBIRIS.v2) and MICHE database on visible light
environment and CASIA-Iris-Distance database on NIR environment, respectively.
Additionally, we generated iris images using trained generation models with
each database. We made DGIM (trained generation models) and GI (generated
images from trained model) open to other researchers.
(2) Request for DGIM&GI
To gain access to the DGIM&GI, download the following request form for DGIM&GI. Please sign and scan the request form and email to Mr. Min Beom Lee (smin6180@naver.com).
Any work that uses this DGIM&GI must acknowledge the authors by including the following reference.
Min Beom Lee, Yu Hwan Kim, and Kang Ryoung Park, “Conditional Generative Adversarial Network-Based Data Augmentation for Enhancement of Iris Recognition Accuracy,” IEEE Access, Vol. 7, pp. 122134-122152, September 2019.
<
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39. Dongguk CNN and LSTM models for the
classification of multiple gastrointestinal (GI) diseases, and video indices of
experimental endoscopic videos
(1) Introduction
We trained a cascaded ResNet18 and LSTM model for classification of multiple gastrointestinal diseases by using endoscopic video data. Two different publicly available endoscopic databases [1,2] were considered for the training and validation of our proposed CNN+LSTM based model. Moreover, the trained model is also used in class prediction-based retrieval of endoscopic images. We made our trained model and video indices of experimental endoscopic videos open to other researchers.
1. Gastrolab − The gastrointestinal site. Available online:
http://www.gastrolab.net/ni.htm (accessed on 1 February 2019).
2. Pogorelov, K.; Randel, K. R.; Griwodz, C.;
Eskeland, S. L.; de Lange, T.; Johansen, D.; Spampinato, C.; Dang-Nguyen,
D.-T.; Lux, M.; Schmidt, P. T.; Riegler, M.; Halvorsen, P. KVASIR: A
multi-class image dataset for computer aided gastrointestinal disease
detection. In Proceedings of the 8th ACM Multimedia Systems Conference, Taipei,
Taiwan, 20–23 June 2017; pp. 164–169.
(2) Request for our CNN+LSTM models and video indices
To gain access to the models and video indices, download the following request form for CNN+LSTM models and video indices. Please sign and scan the request form and email to Mr. Muhammad Owais (malikowais266@gmail.com).
Any work that uses these CNN+LSTM models and video indices must acknowledge the authors by including the following reference.
Muhammad Owais, Muhammad Arsalan, Jiho Choi, Tahir
Mahmood, and Kang Ryoung Park, “Artificial
Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using
Endoscopy Videos for Clinical Diagnosis,” Journal of Clinical Medicine, Vol. 8, Issue
7(986), pp. 1-33, July 2019.
<
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38. Dongguk
Dual-Camera-based Gaze Database (DDCG-DB1) and
CNN models with Algorithms
(1) Introduction
A natural gaze-detection database [Dongguk dual-camera-based gaze database (DDCG-DB1)] is constructed from the images of 26 drivers by dual near-infrared (NIR) light cameras with illuminators in a vehicle environment, and classified into nine situations such as wearing of sunglasses, different glasses, and hats with mobile phones. We make DDCG-DB1 and our CNN model trained with this database open to other researchers.
(2) Request for DDCG-DB1 and CNN model
To gain access to the DDCG-DB1 with CNN model, download the following request form. Please scan the request form and email to Mr. Hyo Sik Yoon (yoonhs@dongguk.edu).
Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.
Hyo Sik Yoon, Na Rae Baek, Noi Quang Truong, and Kang
Ryoung Park, “Driver Gaze Detection Based on Deep Residual
Networks Using the Combined Single Image of Dual Near-Infrared Cameras,” IEEE
Access, Vol. 7, pp. 93448-93461, July 2019.
===========================================================================================================================================================================================================
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37. Dongguk Mobile Finger-Wrinkle Database (DMFW-DB1) and CNN model
with Algorithms
(1) Introduction
We collected the smartphone-acquired
finger-wrinkle open database DMFW-DB1 using the LG V20’s
frontal-viewing camera (8 mega-pixels (2,160 ×
3,840 pixels), 30 fps, auto-mode) from 33 people (both hands) in five different
indoor environments. In addition, we trained finger-wrinkle recognition system
based on ResNet-101. We make DMFW-DB1 and our CNN model trained with this
database open to other researchers.
(2) Request for DMFW-DB1 and CNN model
To gain access to the DMFW-DB1 with CNN model, download the following request form. Please scan the request form and email to Mr. Chan Sik Kim (kimchsi90@dongguk.edu).
Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.
Chan Sik Kim, Nam Sun Cho, and Kang Ryoung Park, “Deep Residual Network-Based Recognition of Finger
Wrinkles Using Smartphone Camera,” IEEE Access, Vol. 7, pp. 71270- 71285, June 2019.
===========================================================================================================================================================================================================
<
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36. Dongguk low-resolution drone camera dataset (DLDC-DB1, DLDC-DB2)
& CNN models
(1) Introduction
We used the Dongguk drone camera dataset ver.2 (DDroneC-DB2) open dataset to make an artificial low-resolution dataset DLDC-DB1 by generating low-resolution images of 80×80 pixels from the original images of 320×320 pixels using bicubic interpolation. Additionally, we collected the real low-resolution dataset DLDC-DB2 using a visible light camera of low-resolution, equipped on a DJI Phantom 4 drone, while landing. The camera presents a downward view of the drone and captures images of 320×240 pixels. We make our CNN models trained by these datasets and open to other researchers, also.
(2) Request for DLDC-DB1, DLDC-DB2 and CNN models
To gain access to the datasets with CNN models, download the following request form. Please scan the request form and email to Mr. Noi Quang Truong (noitq.hust@gmail.com).
Any work that uses or incorporates the dataset must acknowledge the authors by including the following reference.
Noi Quang Truong, Phong Ha Nguyen, Se Hyun Nam, and
Kang Ryoung Park, “Deep
Learning-Based Super-Resolution Reconstruction and Marker Detection for Drone
Landing,”IEEE Access, Vol. 7, pp. 61639-61655, May
2019.
===========================================================================================================================================================================================================
<
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35. Dongguk CNN Model for CBMIR
(1) Introduction
We trained an enhanced ResNet50 model for classification and
retrieval of multimodal medical images. 12 different publicly available
databases [1] including 50 classes were considered for the training and
validation of our enhanced ResNet50. Finally, the trained model is used in
content-based medical image retrieval (CBMIR) by performing deep feature-based
classification of medical images.
We made our trained model open to other researchers.
1.
Multiple Medical imaging database: Available online: https://sites.google.com/site/aacruzr/image-datasets
(accessed on 28 Feb 2019).
(2) Request for CNN Model for CBMIR
To gain access to the models, download the following request form for CBMIR-CNN. Please sign and scan the request form and email to Mr. Muhammad Owais (malikowais266@gmail.com).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
Muhammad Owais, Muhammad Arsalan, Jiho Choi, and Kang
Ryoung Park, “Effective
Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR)
by Using Artificial Intelligence,” Journal of Clinical
Medicine, Vol. 8, Issue 4(462), pp. 1-31, April 2019
<
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34. Dongguk Person ReID CNN Models (DPRID-CNN)
(1) Introduction
We trained Person Re-Identification based on ResNet-50 using two
databases including Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1) [1] and the Sun
Yat-sen University multiple modality Re-ID (SYSU-MM01) finger-vein database
[2]. We made trained models open to other researchers.
1. DBPerson Recog-DB1.
Available in this page No.3.
2. SYSU-MM01. Available
online: https://github.com/wuancong/SYSU-MM01
(accessed on 28 Feb 2019).
(2) Request for DPRID-CNN
To gain access to the models, download the following request form for DPRID-CNN. Please sign and scan the request form and email to Mr. Jin Kyu Kang (kangjinkyu@dgu.edu).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
Jin Kyu
Kang, Toan Minh Hoang, and Kang Ryoung Park, “Person Re-Identification Between Visible and
Thermal Camera Images Based on Deep Residual CNN Using Single Input,” IEEE
Access, Vol. 7, pp. 57972-57984, May 2019.
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33. Dongguk
DenseNet-based Finger-vein Recognition Model (DDFRM) with algorithms
(1) Introduction
We trained finger-vein recognition system based on DenseNet-161
using two databases including the
Hong Kong Polytechnic University Finger Image Database (version 1) [1] and the
Shandong University homologous multi-modal traits (SDUMLA-HMT) finger-vein
database [2]. We made trained models/Algorithm open to other researchers.
1. Kumar, A.; Zhou, Y. Human
identification using finger images. IEEE
Trans. Image Process. 2012, 21, 2228-2244.
2. SDUMLA-HMT Finger Vein
Database. Available online: http://mla.sdu.edu.cn/info/1006/1195.htm (accessed
on 7 May 2018).
(2) DDFRM with Algorithm Request
To gain access to the models and algorithm, download the following request form for DDFRM with algorithm. Please sign and scan the request form and email to Mr. Jong Min Song (whdwhd93@gmail.com).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
Jong Min
Song, Wan Kim, and Kang Ryoung Park, “Finger-vein
Recognition Based on Deep DenseNet Using Composite Image,” IEEE Access, Vol. 7, pp. 66845- 66863,
June 2019.
<
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32. Dongguk OR-Skip-Net Model for Image Segmentation with Algorithm
and Black Skin People (BSP) Label Information
(1) Introduction
We trained outer skip connection-based deep convolutional network (OR-Skip-net) for image segmentation related to medical diagnosis and other applications, to evaluate the segmentation performance system using ten databases including HGR [1], EDds [2], LIRIS [2], SSG [2], UT [2], AMI [2], Pratheepan [3], BSP, Warwick-QU [4], and NICE.II [9]. We made trained models/Algorithm and BSP label information open to other researchers.
1.
Hand detection and pose
estimation for creating human-computer interaction project. Available online:
http://sun.aei.polsl.pl/~mkawulok/gestures/ip.html (accessed on October 31,
2018).
2.
Skin detection datasets for
video monitoring. Available online:
http://www-vpu.eps.uam.es/publications/SkinDetDM/ (accessed on November 5,
2018).
3.
Pratheepan dataset + ground
truth. Available online: http://cs-chan.com/downloads_skin_dataset.html
(accessed on November 5, 2018).
4.
GlaS@MICCAI'2015: Gland
segmentation challenge contest. Available online:
https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/ (accessed on 24
January 2019).
5.
NICE. II. Noisy iris challenge
evaluation - part II. Available online: http://nice2.di.ubi.pt/ (accessed on
November 8, 2018).
(2) Request for OR-Skip-Net Model with Algorithm and Black Skin
People (BSP) Label Information
To gain access to the models, algorithm and BSP label information, download the following request form. Please sign, scan the request form, and email to Mr. Arsalan (arsal@dongguk.edu).
Any work that uses this CNN model with algorithm and label information must acknowledge the authors by including the following reference.
Muhammad Arsalan, Dong Seop Kim, Muhammad Owais, and Kang Ryoung Park, “OR-Skip-Net: Outer Residual Skip Network for Skin Segmentation in Non-Ideal Situations,”Expert Systems With Applications, in press, 2020.
<
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and
BSP label information >
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31. Dongguk Banknote Type and Fitness Database (DF-DB3) & CNN
Model with algorithms
(1) Introduction
We make Dongguk Banknote
Type and Fitness Database (DF-DB3) based on Indian
rupee (INR) (INR10/20/50/100/500/1000), the Korean won (KRW) (KRW
1000/5000/10000/50000) and United States dollar (USD) (USD 5/10/20/50/100)
banknotes, and trained CNN
Models of AlexNet, GoogleNet, ResNet-18/50 with algorithms available for the
fair comparison by other researchers.
(2) Request for Dongguk Banknote Type and Fitness Database (DF-DB3)
& CNN Model with algorithms
To gain access to these
files, download the following request form. Please scan the request form and
email to Dr. Tuyen Danh Pham (phamdanhtuyen@dongguk.edu). Any work that uses
these files with algorithm must acknowledge the authors by including the
following reference.
===========================================================================================================================================================================================================
<
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Model with algorithms >
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30. Dongguk RetinaNet for Detecting Road Marking Objects with
Algorithms and Annotated Files for Open Databases
(1) Introduction
Although the open databases
of the Malaga urban dataset [1], the Daimler dataset [2], and the Cambridge
dataset [3] have been widely used in previous studies, they do not provide
annotated information of road markings. This increases the time and load for
system implementation. Therefore, we provide the manually annotated information
of road markings for the Malaga urban dataset, the Daimler dataset, and the
Cambridge dataset. We also provide the proposed RetinaNet models trained by
these databases based on different backbones with and without pre-trained
weights to other researchers.
1. The Málaga Stereo and Laser
Urban Data Set –
MRPT. Available online: https://www.mrpt.org/MalagaUrbanDataset (accessed on 1
October 2018).
2. Daimler Urban Segmentation
Dataset.
Available online:
http://www.6d-vision.com/scene-labeling (accessed on 1 October 2018).
3. Cambridge-driving Labeled Video Database (CamVid). Available online: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/ (accessed on 1 October 2018).
(2) Request for Dongguk RetinaNet for Detecting Road Marking Objects
with Algorithms and Annotated Files for Open Databases
To gain access to these files, download the following request form. Please scan the request form and email to Mr. Toan Minh Hoang (hoangminhtoan@dongguk.edu). Any work that uses these files with algorithm must acknowledge the authors by including the following reference.
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Algorithms and Annotated Files for Open Databases >
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29. Dongguk CNN Model for NIR Ocular Recognition (DC4NO) with
algorithm
(1) Introduction
We made our algorithm for rough pupil detection based on sub-block based template matching and deep ResNet models trained with three open databases: CASIA-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand [1]. We made these trained CNN models for ocular recognition with algorithm open to other researchers.
1. CASIA-iris version 4.
Available online:
http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp
(accessed on 9 November 2018)
(2) Request for DC4NO with algorithm
To gain access to the DC4NO with algorithm, download the following request form. Please scan the request form and email to Mr. Young Won Lee (lyw941021@dongguk.edu).
Any work that uses this DC4NO with algorithm must acknowledge the authors by including the following reference.
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28. Dongguk Face Presentation Attack Detection Algorithms by Spatial
and Temporal Information (DFPAD-STI)
(1) Introduction
We made stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features for face presentation attack detection using the images from CASIA database [1] and Replay-mobile dataset [2], respectively. We made these trained CNN model open to other researchers.
1. Zhang, Z.; Yan, J.; Liu, S.; Lei, Z.; Yi, D. Li, S. Z.
A face anti-spoofing database with diverse attack. In Proceedings of the 5th
International Conference on Biometric, New Delhi, India, 29 March – 1 April, 2012.
2. Costa-Pazo, A.; Bhattacharjee, S.; Vazquez-Fernandez,
E.; Marcel, S. The replay-mobile face presentation attack database. In
Proceedings of the International Conference on the Biometrics Special Interest
Group, Darmstadt, Germary, 21-23 September, 2016.
(2) Request for DFPAD-STI
To gain access to the DFPAD-STI, download the following request form. Please scan the request form and email to Prof. Dat Tien Nguyen (nguyentiendat@dongguk.edu).
Any work that uses this DFPAD-STI must acknowledge the authors by including the following reference.
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27. Dongguk Dual Camera-based Driver Database (DDCD-DB1) and Trained
Faster R-CNN Model with Algorithm
(1) Introduction
When acquiring DDCD-DB1, the driver’s gaze area was divided into 15 zones. The drivers gazed at the 15 zones
divided out beforehand in order, and a total of 26 participants were each
assigned 8 different situations (i.e., wearing a hat, wearing four different
types of glasses (rimless, gold-rimmed, half-frame, and horn-rimmed), wearing
sunglasses, making a call through mobile phone, covering face through hand,
etc.), and the data were collected by two NIR cameras with NIR illuminators. As
the participants gazed at the designated regions in turn, natural head
rotations that would occur in actual driving were permitted, and other
restrictions or instructions were not provided. When acquiring actual driving
data, as there was the risk of a traffic accident, rather than actually
driving, a real vehicle (model name of SM5 new impression by Renault Samsung
[41]) was started from a parked state in various locations (from daylight road
to a parking garage).
In addition, we made two
faster R-CNN models trained with our DDCD-DB1 and open database (CAVE-DB
[1]),
respectively, public.
1. Smith, B.A.; Yin, Q.;
Feiner, S.K.; Nayar, S.K. Gaze Locking: Passive Eye Contact Detection for
Human-Object Interaction. In Proceedings of the 26th Annual ACM Symposium on
User Interface Software and Technology, St. Andrews, Scotland, UK, 8-11 October
2013; pp. 271–280.
(2) Request for DDCD-DB1 and faster R-CNN
model with algorithms
To gain access to DDCD-DB1 and faster R-CNN model with algorithms, download the following request form. Please scan the request form and email to Mr. Sung Ho Park (pshgod91@dongguk.edu).
Any work that uses this DDCD-DB1 and faster R-CNN model with algorithms must acknowledge the authors by including the following reference.
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model with algorithms >
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26. Dongguk FRED-Net with Algorithm
(1) Introduction
We trained fully residual encoder-decoder network (FRED-Net) CNN models for iris and road scene segmentations, to evaluate the segmentation performance system using seven databases including NICE-II [1], MICHE [2], CASIA distance [3], CASIA interval [3], IITD [4], CamVid [5], and KITTI [6]. We made trained models/Algorithm open to other researchers.
6.
NICE.II. Noisy Iris Challenge
Evaluation-Part II. Available online: http://nice2.di.ubi.pt/index.html (accessed
on 28 December 2017).
7.
De Marsico, M.; Nappi, M.;
Riccio, D.; Wechsler, H. Mobile iris challenge evaluation (MICHE)-I, biometric
iris dataset and protocols. Pattern Recognit. Lett. 2015, 57, 17–23.
8.
CASIA-Iris-databases.
Available online: http://biometrics.idealtest.org/dbDetailForUser.do?id=4
(accessed on 28 December 2017).
9.
IIT Delhi Iris Database.
Available online:
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm (accessed on 28
December 2017).
10.
Brostow, G. J.; Fauqueur, J.;
Cipolla, R. Semantic object classes in video: A high-definition ground truth
database. Pattern Recognit. Lett. 2009, 30, 88–97.
11.
Geiger, A.; Lenz, P.; Stiller,
C.; Urtasun, R. Vision meets robotics: The KITTI dataset. Int. J. Robot. Res.
2013, 32, 1231–1237.
(2) FRED-Net Model with Algorithm Request
To gain access to the models and algorithm, download the following FRED-Net model with algorithm request form. Please sign and scan the request form and email to Mr. Arsalan (arsal@dongguk.edu).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
Muhammad Arsalan, Dong Seop Kim, Min Beom Lee, Muhammad Owais, and Kang Ryoung Park, “FRED-Net: Fully residual encoder–decoder network for accurate iris segmentation,” Expert Systems with Applications, Vol. 122, pp. 217-241, May 2019.
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25. Dongguk Face and Body Database (DFB-DB1) with CNN models and
algorithms
(1) Introduction
DFB-DB1 was created for the experiments using images of 22 people obtained by two types of cameras to assess the performance of the proposed method in a variety of camera environments. The first camera was a Logitech BCC 950, and the camera specifications include a camera viewing angle of 78°, a maximum resolution of full high-definition (HD) 1080 p, and auto-focusing at 30 frames per second (fps). The second camera was a Logitech C920, and its specifications include a maximum resolution of full HD 1080p, a viewing angle of 78° at 30 fps, and auto focusing. Images were taken in an indoor environment with indoor lights on, and each camera was installed at a height of 2 m 40 cm. The database was divided into two categories according to the camera. In the first database, the images were captured by the Logitech BCC 950, and the second database is composed of the images obtained by the Logitech C920, and the angle of camera was similar to that for capturing the first database.
In addition, we open our two CNN models trained with DFB-DB1 and open database of ChokePoint database [1], respectively, in addition to our algorithms.
1. ChokePoint Database. Available online: http://arma.sourceforge.net/chokepoint/ (accessed on 21 Feb. 2018).
(2) Request for DFB-DB1 with CNN model and
algorithms
To gain access to DFB-DB1 with CNN model and algorithms, download the following request form. Please scan the request form and email to Mr. Ja Hyung Koo (koo6190@naver.com).
Any work that uses this DFB-DB1 with CNN model and algorithms must acknowledge the authors by including the following reference.
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DFB-DB1 and CNN model Request Form >
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24. Dongguk Night-Time Face Detection database (DNFD-DB1) and
algorithm including CNN model
(1) Introduction
DNFD-DB1
is a self-constructed database acquired through a fixed single visible-light
camera at a distance of approximately 20–22 m at
night. The resolution of the camera is 1600 × 1200
pixels, but the image is cropped to the average adult height, which is
approximately 600. A total of 2,002 images of 20 different people were
prepared, and there are 4–6 people in each frame. To carry out the
2-fold cross-validation, those 20 people
were divided into two subsets of 10 people. In addition, we made two 2-stage
Faster R-CNN models
trained with our DNFD-DB1 and open database
of Fudan University [1], respectively, public.
[1] Open
database of Fudan University. Available online:
https://cv.fudan.edu.cn/_upload/tpl/06/f4/1780/template1780/humandetection.htm
(accessed on 26 March 2018).
(2) DNFD-DB1 and CNN model Request
To gain access to DNFD-DB1 and CNN model, download the following request form. Please scan the request form and email to Mr. Se Woon Cho (jsu319@naver.com).
Any work that uses this DNFD-DB1 and CNN model must acknowledge the authors by including the following reference.
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DNFD-DB1 and CNN model Request Form >
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23. Dongguk Iris Spoof Detection CNN Model version 2 (DFSD-CNN-2)
with Algorithm
(1) Introduction
We trained CNN models using local and global image features based on VGG-19-Net architecture for presentation attack detection for iris recognition system using two public databases, Warsaw-2017 [1] and Notre Dame–2015 [2], respectively. We made trained models open to other researchers.
12. Yambay, D.; Becker, B.; Kohli, N.; Yadav, D.; Czajka, A.; Bowyer, K. W.;
Schuckers, S.; Singh, R.; Vatsa, M.; Noore, A.; Gragnaniello, D.; Sansone, C.;
Verdoliva, L.; He, L.; Ru, Y.; Li, H.; Liu, N.; Sun, Z.; Tan, T. LivDet iris
2017 – iris
liveness detection competition 2017. In Proceedings of the International
Conference on Biometrics, Denver, CO, USA, 1-4 October 2017.
13. Doyle, J. S.; Bowyer, K. W. Robust detection of textured contact lens in
iris recognition using BSIF. IEEE Access, 2015, 3, 1672-1683.
(2) DFSD-CNN-2 Model Request
To gain access to the models and algorithm, download the following DFSD-CNN-2 request form. Please sign and scan the request form and email to Prof. Nguyen (nguyentiendat@dongguk.edu).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
D. T. Nguyen,
T. D. Pham, Y. W. Lee, and K. R. Park, "Deep Learning-Based Enhanced Presentation Attack Detection for Iris
Recognition by Combining Features from Local and Global Regions Based on NIR
Camera Sensor," Sensors, Vol. 18, Issue 8(2601), pp. 1-32,
August 2018.
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22. Dongguk Fitness Database (DF-DB2) & CNN Model
(1) Introduction
We collected banknote
fitness databases (DF-DB2) from three national currencies, which are Korean won (KRW), Indian rupee (INR), and Unites
States dollar (USD).
Six denominations exist in the
INR dataset: 10, 20, 50, 100, 500, and 1000 rupees, and two denominations exist
in the KRW dataset: 1000 and 5000 wons, each of which consists of three fitness
levels of fit, normal, and unfit for recirculation, called the case 1 fitness
level. In these case 1 datasets, each banknote image was captured using VR
sensors on both sides, and IRT sensors on the front side. Five denominations
exist for the USD: 5, 10, 20, 50 and 100 dollars, divided into two fitness
levels of fit and unfit, called the case 2 fitness level. The number of images
captured per banknote was two, including the VR and IRT images of one side of
the banknote. In addition, we made CNN models trained with our DF-DB2
public.
(2) DF-DB2 and CNN model Request
To gain access to DF-DB2 and CNN models, download the following request form. Please scan the request form and email to Prof. Tuyen Danh Pham (phamdanhtuyen@gmail.com).
Any work that uses this DF-DB2 and CNN Model must acknowledge the authors by including the following reference.
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21. Dongguk-body-movement-based human identification database
version 2 (DBMHI-DB2) & CNN Model
(1) Introduction
We have
collected our database in both dark and bright environments. The database included both front and back view images of humans. Our
database has been collected in five different places in different days with
same camera heights. The database consists of data of 100 people including men and women. The database includes both thermal
and visible light images but only thermal images have been utilized in this
research. The people in our database have different heights and widths, and
their sizes vary from 27 to 150 pixels in width and from 90 to 390 pixels in
height. In addition, we made our trained CNN and CNN-LSTM model public.
(2) DBMHI-DB2 database & the trained CNN model Request
To gain access to the database and CNN model, download the following DBMHI-DB2 and CNN model request form. Please scan the request form and email to Mr. Ganbayar Batchuluun (ganabata87@dongguk.edu).
Any work that uses or incorporates the database and CNN model must acknowledge the authors by including the following reference.
Ganbayar Batchuluun, Hyo Sik Yoon, Jin Kyu Kang, and Kang Ryoung Park, "Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network," IEEE Access, Vol. 6, pp. 63164-63186, October 2018.
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20. Dongguk Multimodal Recognition CNN of Finger-vein and Finger
shape (DMR-CNN) with Algorithm
(1) Introduction
We trained multimodal recognition system of finger-vein and finger
shape based on ResNet-50 and ResNet-101 using two databases including the Shandong University homologous
multi-modal traits (SDUMLA-HMT) [1] and the Hong Kong Polytechnic University
Finger Image Database (version 1) [2]. We made trained models/Algorithm open to
other researchers.
1. SDUMLA-HMT Finger Vein
Database. Available online: http://mla.sdu.edu.cn/info/1006/1195.htm
(accessed on 7 May 2018).
2. Kumar, A.; Zhou, Y. Human
identification using finger images. IEEE
Trans. Image Process. 2012, 21, 2228-2244.
(2) DMR-CNN Model with Algorithm
Request
To gain access to the models and algorithm, download the following DMR-CNN model with algorithm request form. Please sign and scan the request form and email to Mr. Wan Kim (daiz0128@naver.com).
Any work that uses this CNN model must acknowledge the authors by including the following reference.
W. Kim, J. M. Song, and K. R. Park, “Multimodal Biometric Recognition Based on
Convolutional Neural Network by the Fusion of Finger-vein and Finger Shape
Using Near-Infrared (NIR) Camera Sensor,” Sensors, Vol. 18, Issue 7(2296), pp. 1-34, July 2018.
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