Papers by Rae-hong Park
International Journal of Computer Graphics & Animation (IJCGA), 2017
A single two dimensional (2D) image does not contain depth information. An infinite number of poi... more A single two dimensional (2D) image does not contain depth information. An infinite number of points in the three dimensional (3D) space are projected to the same point in the image plane. But a single 2D image has some monocular depth cues, by which we can make a hypothesis of depth variation in the image to generate a depth map. This paper proposes an interactive method of depth map generation from a single image for 2D-to-3D conversion. Using a hypothesis of depth variation can reduce the human effort to generate a depth map. The only thing required from a user is to mark some salient regions to be distinguished with respect to depth variation. The proposed algorithm makes hypothesis of each salient region and generates a depth map of an input image. Experimental results show that the proposed method gives natural depth map in terms of human perception.
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IEEE Transactions on Circuits and Systems for Video Technology, 2015
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Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processi... more Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processing. VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reducedreference mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV distributions because network resource is restricted in the case of mobile video. The proposed method is more efficient than the conventional methods in view of the computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process rather than reconstructing the distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different from the conventional methods that assess each image frame independently. For testing the similarity between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we compare the proposed method with the conventional methods with coded video clips, which are coded under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show that the proposed method gives the performance comparable with the conventional methods, especially, the proposed method requires much lower transmission data.
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Machine Vision Applications in Industrial Inspection X, 2002
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Machine Vision Applications in Industrial Inspection IX, 2001
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2014 IEEE International Conference on Image Processing (ICIP), 2014
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Systems, Man and …, 1998
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International Journal of Computer Graphics & Animation (IJCGA), 2015
This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method... more This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volumevisualization, a 3-D visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image. Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation and 3-D volume calculation methods can provide practical supports to surgery options and medical practice to medical students.
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International Journal of Computer Graphics & Animation (IJCGA), 2015
This paper proposes an objective assessment method for perceptual image quality of tone mapped im... more This paper proposes an objective assessment method for perceptual image quality of tone mapped images. Tone mapping algorithms are used to display high dynamic range (HDR) images onto standard display devices that have low dynamic range (LDR). The proposed method implements visual attention to define perceived structural distortion regions in LDR images, so that a reasonable measurement of distortion between HDR and LDR images can be performed. Since the human visual system is sensitive to structural information, quality metrics that can measure structural similarity between HDR and LDR images are used. Experimental results with a number of HDR and tone mapped image pairs show the effectiveness of the proposed method.
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This paper proposes a segmentation method and a thr
ee-dimensional (3-D) volume calculation meth... more This paper proposes a segmentation method and a thr
ee-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomograph
y (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmen
tation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segm
entation, kidney regions are segmented from CT slic
e
images by using a graph-cut method that is applied
to the middle slice of input CT slice images. Then,
the
same method is used for the remaining CT slice imag
es. In cyst segmentation, cyst regions are segmente
d
from the kidney regions by using fuzzy C-means clus
tering and level-set methods that can reduce noise
of
non-cyst regions. For 3-D volume calculation, cyst
volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst
in each CT slice image equals to the number of pixe
ls in
the cyst regions multiplied by spatial density of C
T slice images, and then the volume of cysts is cal
culated
by multiplying the cyst area and thickness (interva
l) of CT slice images. In 3-D volume visualization,
a 3-D
visualization technique is used to show the distrib
ution of cysts in kidneys by using the result of cy
st volume
calculation. The total 3-D volume is the sum of the
calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D
volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide prac
tical supports to surgery options and medical
practice to medical students.
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In this paper, we propose an algorithm that automatically detects and removes moving logos in vid... more In this paper, we propose an algorithm that automatically detects and removes moving logos in video. The
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.
Bookmarks Related papers MentionsView impact
current issue by Rae-hong Park
In this paper, we propose an algorithm that automatically detects and removes moving logos in vid... more In this paper, we propose an algorithm that automatically detects and removes moving logos in video. The
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.
Bookmarks Related papers MentionsView impact
Uploads
Papers by Rae-hong Park
ee-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomograph
y (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmen
tation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segm
entation, kidney regions are segmented from CT slic
e
images by using a graph-cut method that is applied
to the middle slice of input CT slice images. Then,
the
same method is used for the remaining CT slice imag
es. In cyst segmentation, cyst regions are segmente
d
from the kidney regions by using fuzzy C-means clus
tering and level-set methods that can reduce noise
of
non-cyst regions. For 3-D volume calculation, cyst
volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst
in each CT slice image equals to the number of pixe
ls in
the cyst regions multiplied by spatial density of C
T slice images, and then the volume of cysts is cal
culated
by multiplying the cyst area and thickness (interva
l) of CT slice images. In 3-D volume visualization,
a 3-D
visualization technique is used to show the distrib
ution of cysts in kidneys by using the result of cy
st volume
calculation. The total 3-D volume is the sum of the
calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D
volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide prac
tical supports to surgery options and medical
practice to medical students.
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.
current issue by Rae-hong Park
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.
ee-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomograph
y (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmen
tation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segm
entation, kidney regions are segmented from CT slic
e
images by using a graph-cut method that is applied
to the middle slice of input CT slice images. Then,
the
same method is used for the remaining CT slice imag
es. In cyst segmentation, cyst regions are segmente
d
from the kidney regions by using fuzzy C-means clus
tering and level-set methods that can reduce noise
of
non-cyst regions. For 3-D volume calculation, cyst
volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst
in each CT slice image equals to the number of pixe
ls in
the cyst regions multiplied by spatial density of C
T slice images, and then the volume of cysts is cal
culated
by multiplying the cyst area and thickness (interva
l) of CT slice images. In 3-D volume visualization,
a 3-D
visualization technique is used to show the distrib
ution of cysts in kidneys by using the result of cy
st volume
calculation. The total 3-D volume is the sum of the
calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D
volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide prac
tical supports to surgery options and medical
practice to medical students.
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.
proposed algorithm consists of logo detection, tracking and extraction, and removal. In the logo detection
step, we automatically detect moving logos using the saliency map and the scale invariant feature transform.
In the logo tracking and extraction step, an improved mean shift tracking method and backward tracking
technique are used in which only logo regions are extracted by using color information. Finally, in the logo
removal step, the detected logo region is filled with the region of neighborhood using an exemplar-based
inpainting that can fill a large detected region without artifacts. These steps are effectively interconnected
using control flags. Experimental results with various test sequences show that the proposed algorithm is
effective to detect, track, and remove moving logos in video.