A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny-Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.
Sengur et al. REF have proposed a novel edge detection technique based on the texture feature coding method (TFCM).
A Novel Edge Detection Algorithm Based on Texture Feature Coding
Journal of Intelligent Systems
Computer Science