Abstract
SURF is one of the most robust local invariant feature descriptors. SURF is implemented mainly for gray images. However, color presents important information in the object description and matching tasks as it clearly in the human vision system. Many objects can be unmatched if their color contents are ignored. To overcome this drawback this paper proposed a method CSURF (Color SURF) that combines features of Red, Green and blue layers to detect color objects. It edits matched process of SURF to be more efficient with color space. Experimental results show that CSURF is more precious than traditional SURF and CSURF invariant to RGB color space
Highlights
Finding similarity between different images is representing a challenge problem in computer vision application
The experiment will work on ten images at different levels of illumination directions, view direction and Following figure shows how Color SURF descriptor (CSURF) surpass traditional SURF in matching between images under illumination direction
It is clear from following figure how CSURF results in image(b) have better performance than SURF result in image(a) that under different view of direction
Summary
Finding similarity between different images is representing a challenge problem in computer vision application. It uses in pattern recognition, object tracking, image retrieval, etc. SURF has been constructed essentially for gray images, ignoring totally the information found in the color space as most of feature extractors. This paper introduces a method that helps SURF to match color images with respect to its color in RGB space. The rest of this paper is organized as follows: section two Introduce survey of other color descriptors, section three represent methodology of the proposed color SURF (CSUR), section four represents experimental results, section five introduces conclusion
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