Texture-boundary detection in real-time

Boundary detection is an essential first-step for many computer vision applications. In practice, boundary detection is difficult because most images contain texture. Normally, texture-boundary detectors are complex, and so cannot run in real-time. On the other hand, the few texture boundary detectors that do run in real-time leave much to be desired in terms of quality. This thesis proposes two real-time texture-boundary detectors – the Variance Ridge Detector and the Texton Ridge Detector – both of which can detect high-quality texture-boundaries in real-time. The Variance Ridge Detector is able to run at 47 frames per second on 320 by 240 images, while scoring an F-measure of 0.62 (out of a theoretical maximum of 0.79) on the Berkeley segmentation dataset. The Texton Ridge Detector runs at 10 frames per second but produces slightly better results, with an F-measure score of 0.63. These objective measurements show that the two proposed texture-boundary detectors outperform all other texture-boundary detectors on either quality or speed. As boundary detection is so widely-used, this development could induce improvements to many real-time computer vision applications

Fast Segmentation via Randomized Hashing

This paper describes a feature based approach to segmenting images into coherent regions. The method draws inspiration from earlier work on randomized projection schemes for approximate nearest neighbor computation. The method proceeds by first computing a descriptor vector for each of the pixels in the image. These vectors are then randomly hashed to yield binary vectors. Salient clusters in the hash space are automatically identified by considering the populations associated with various hash codes. Since the method avoids the explicit vector distance computations associated with other methods, it is very amenable to fast implementation. Experimental results are presented on standard data sets. 1 Introduction and Related Work Segmentation, the problem of breaking an image into coherent regions is, of course, a fundamental problem in Computer Vision. This paper proposes a new approach to the segmentation problem that leverages ideas developed in the Theoretical Computer Science literature to derive a new feature space based clustering algorithm that is amenable to real time implementation