Increase Efficiency of SURF using RGB Color Space

Abstract

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

Towards Automated Order Picking Robots for Warehouses and Retail

“…RGBSURF While the SURF feature points are determined from the grayscale image, the RGBSURF descriptors are computed individually in the color image's R, G, and B-channels [23,27] yielding a descriptor of length 3 • 64 = 192.…”

“…A good survey on color extensions for feature point descriptors is given in [23]. The RGBSURF method favored in our system was inspired by [11,27]. In the Amazon Robotics Challenge, Convolutional Neural Network based (CNN) methods appear to be prevalent [12,20].…”