Local Stereo Matching Using Adaptive Local Segmentation

We propose a new dense local stereo matching framework for gray-level images based on an adaptive local segmentation using a dynamic threshold. We define a new validity domain of the fronto-parallel assumption based on the local intensity variations in the 4-neighborhood of the matching pixel. The preprocessing step smoothes low textured areas and sharpens texture edges, whereas the postprocessing step detects and recovers occluded and unreliable disparities. The algorithm achieves high stereo reconstruction quality in regions with uniform intensities as well as in textured regions. The algorithm is robust against local radiometrical differences; and successfully recovers disparities around the objects edges, disparities of thin objects, and the disparities of the occluded region. Moreover, our algorithm intrinsically prevents errors caused by occlusion to propagate into nonoccluded regions. It has only a small number of parameters. The performance of our algorithm is evaluated on the Middlebury test bed stereo images. It ranks highly on the evaluation list outperforming many local and global stereo algorithms using color images. Among the local algorithms relying on the fronto-parallel assumption, our algorithm is the best ranked algorithm. We also demonstrate that our algorithm is working well on practical examples as for disparity estimation of a tomato seedling and a 3D reconstruction of a face

Person Detection and Tracking Using Binocular Lucas-Kanade Feature Tracking and K-means Clustering

In this thesis, we present the design and implementation of a method for real-time person detection and tracking. Many current methods for detecting and tracking people rely on color contrast or movement to segment the image. Using color, however, requires the target and the background to be significantly different, and motion segmentation requires the target to be in constant motion relative to the background, often requiring stationary cameras. Pattern detection methods have also been applied to the problem of detecting pedestrians, but these approaches are slower and require stationary cameras to function. The method we present in this work does not require a color difference or constant motion to operate. We use Lucas-Kanade features to track feature points between left and right images, producing a sparse disparity map which is then segmented through the application of k-means clustering. We apply a Viola-Jones face detector to determine which, if any, of the resulting feature clusters represent a trackable person. This algorithm is tested using two identical standard cameras mounted on a mobile robot platform. Results are presented demonstrating detection and tracking of a person in several different situations, including partial occlusion and self-occlusion

Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images

In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework