Recovery Of Depth Information Research Articles

Conoscopic holograms analysis using variations of the Hough transform

AbstractThe analysis of holograms obtained using a Conoscopic setup is a very complex subject, given their nature. Holograms contain the 3D depth information about an object whose surface is to be measured and later reconstructed. The recovery of depth information has so far been carried out using mathematical transforms in combination with linear regression techniques. Here the Hough transform, a useful Computer Vision technique for detecting features in images is adapted to the analysis of holograms in order to establish distance relationships for a given object. The captured images of holograms are pre‐processed and subsequently analyzed for characteristic patterns that are later used in finding the distance to an object. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 10–13, 2009

Zoom tracking and its applications

We present a new active vision technique called zoom tracking. Zoom tracking is the continuous adjustment of a camera's focal length in order to keep a constant-sized image of an object moving along the camera's optical axis. Two methods for performing zoom tracking are presented: a closed-loop visual feedback algorithm based on optical flow, and use of depth information obtained from an autofocus camera's range sensor. We explore two uses of zoom tracking: recovery of depth information and improving the performance of scale-variant algorithms. We show that the image stability provided by zoom tracking improves the performance of algorithms that are scale variant, such as correlation-based trackers. While zoom tracking cannot totally compensate for an object's motion, due to the effect of perspective distortion, an analysis of this distortion provides a quantitative estimate of the performance of zoom tracking. Zoom tracking can be used to reconstruct a depth map of the tracked object. We show that under normal circumstances this reconstruction is much more accurate than depth from zooming, and works over a greater range than depth from axial motion while providing, in the worst case, only slightly less accurate results. Finally, we show how zoom tracking can also be used in time-to-contact calculations.

Exploiting Walsh-based attributes to stereo vision

This paper presents a new stereo feature matching method that extracts the disparity measure for the recovery of depth information in 2-D stereo images. In this method, a stereo pair of images are transformed row for row into strings carrying spatially varying Walsh coefficients as attributes. The significance of the information carried by the Walsh coefficients is expressed mathematically and through experimental evaluations. The choice of the Walsh coefficients in contrast to other orthogonal transform coefficients is a direct result of their computational simplicity and their interpretative meaning in terms of the information contained in the spatial domain. The string-to-string matching technique used to bring the two strings into correspondence integrates, into a unified process, both the feature detection and the feature matching processes. The uniqueness and the ordering constraints are explicitly integrated into this string-to-string matching technique. Both the issues of Gaussian filtering and the importance of enforcing the epipolar line constraint are addressed in view of the application of the proposed method. Experimental results are given and assessed in terms of both the accuracy in stereo matching and the ensuing computational requirements.