Color DoG: a three-channel color feature detector for embedded systems

Spencer Fowers; Dah Jye Lee; Doran K. Wilde

doi:10.1117/12.841111

18 January 2010 Color DoG: a three-channel color feature detector for embedded systems

Spencer Fowers, Dah Jye Lee, Doran K. Wilde

Proceedings Volume 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques; 75390X (2010) https://doi.org/10.1117/12.841111
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

A feature tracker is only as good as the features found by the feature detector. Common feature detectors such as Harris, Sobel, Canny, and Difference of Gaussians convolve an image with a specific kernel in order to identify "corners" or "edges". This convolution requires, however, that the source image contain only one value (or color channel) per pixel. This requirement has reduced the scope of feature detectors, trackers, and descriptors to the set of gray scale (and other single-channel) images. Due to the standard 3-channel RGB representation for color images, highly useful color information is typically discarded or averaged to create a gray scale image that current detectors can operate on. This removes a large amount of useful information from the image. We present in this paper the color Difference of Gaussians algorithm which outperforms the gray scale DoG in number and quality of features found. The color DoG utilizes the YCbCr color space to allow for separated processing of intesity and chrominance values. An embedded vision sensor based on a low power field programmable gate array (FPGA) platform is being developed to process color images using the color DoG with no reduction in processing speed. This low power vision sensor will be well suited for autonomous vehicle applications where size and power consumption are paramount.

Citation Download Citation

Spencer Fowers, Dah Jye Lee, and Doran K. Wilde "Color DoG: a three-channel color feature detector for embedded systems", Proc. SPIE 7539, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, 75390X (18 January 2010); https://doi.org/10.1117/12.841111

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available