JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
GPU-Based Optimization of Self-Organizing Map Feature |
| 영문제목(English Title) |
GPU-Based Optimization of Self-Organizing Map Feature |
| 저자(Author) |
Kajal Sharma
Saifullah
Inkyu Moon
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| 원문수록처(Citation) |
VOL 12 NO. 02 PP. 0128 ~ 0134 (2014. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.
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| 키워드(Keyword) |
Feature matching
Graphics processing unit
Self-organizing map
Stereo vision
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| 파일첨부 |
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