Gabor Filter and Rough Clustering Based Edge Detection

This paper introduces an efficient edge detection method based on Gabor filter and rough clustering. The input image is smoothed by Gabor function, and the concept of rough clustering is used to focus on edge detection with soft computational approach. Hysteresis thresholding is used to get the actual output, i.e. edges of the input image. To show the effectiveness, the proposed technique is compared with some other edge detection methods.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation

We deal with the probiem of segmenting static images, a procedure known to be difficult in the case of very noisy patterns, The proposed approach rests on the transformation of a static image into a data flow in which the first image points to be processed are the brighter ones. This solution, inspired by human perception, in which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of asynchronous processing. The asynchronous processing of image points has required the design of a specific architecture that exploits time differences in the processing of information. The results otained when very noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of the approach to other computer vision problem