In Kyu Park - Academia.edu (original) (raw)
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Papers by In Kyu Park
1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997
A robust color image retrieval algorithm is proposed, based on the hybrid graph representation, i... more A robust color image retrieval algorithm is proposed, based on the hybrid graph representation, i.e. a dual graph which consists of a modified color adjacency graph (MCAG) and a spatial variance graph (SVG). The MCAG is proposed to enhance the indexing performance and the database capacity by increasing the feature dimension. In addition, the SVG is introduced in order to utilize the geometric statistics of the chromatic segment in the spatial domain. In the matching, we expand the histogram intersection into the graph intersection, in which graph matching is performed using simple matrix operations. Intensive discussions and experimental results are provided to evaluate the performance of the proposed algorithm. Experiments are carried out with M.J. Swain's et al.'s (1991) test images and the VIRAGE images. It is shown that the proposed algorithm provides high retrieval performance with tolerable computational complexity
Journal of Broadcast Engineering, 2014
In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and i... more In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and its application to fast panoramic image generation on the latest embedded GPU. Parallelized algorithms are implemented using recently developed OpenCL as the embedded GPGPU software platform. We compare the implementation efficiency and speed performance of conventional OpenGL Shading Language and OpenCL. Experimental result shows that implementation on OpenCL has comparable performance with GLSL. Compared with the performance on the embedded CPU in the same application processor, the embedded GPU runs 3~4 times faster. As an example of using feature extraction, panorama image synthesis is performed on embedded GPU by applying image matching using detected features.
1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997
A robust color image retrieval algorithm is proposed, based on the hybrid graph representation, i... more A robust color image retrieval algorithm is proposed, based on the hybrid graph representation, i.e. a dual graph which consists of a modified color adjacency graph (MCAG) and a spatial variance graph (SVG). The MCAG is proposed to enhance the indexing performance and the database capacity by increasing the feature dimension. In addition, the SVG is introduced in order to utilize the geometric statistics of the chromatic segment in the spatial domain. In the matching, we expand the histogram intersection into the graph intersection, in which graph matching is performed using simple matrix operations. Intensive discussions and experimental results are provided to evaluate the performance of the proposed algorithm. Experiments are carried out with M.J. Swain's et al.'s (1991) test images and the VIRAGE images. It is shown that the proposed algorithm provides high retrieval performance with tolerable computational complexity
Journal of Broadcast Engineering, 2014
In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and i... more In this paper, we parallelize the popular feature detection algorithms, i.e. SIFT and SURF, and its application to fast panoramic image generation on the latest embedded GPU. Parallelized algorithms are implemented using recently developed OpenCL as the embedded GPGPU software platform. We compare the implementation efficiency and speed performance of conventional OpenGL Shading Language and OpenCL. Experimental result shows that implementation on OpenCL has comparable performance with GLSL. Compared with the performance on the embedded CPU in the same application processor, the embedded GPU runs 3~4 times faster. As an example of using feature extraction, panorama image synthesis is performed on embedded GPU by applying image matching using detected features.