By Dr. Zhengyou Zhang, Dr. Olivier Faugeras (auth.)
he challenge of interpreting sequences of pictures to extract 3-dimensional T movement and constitution has been on the center of the learn in computing device vi sion for a few years. it is important to on the grounds that its good fortune or failure will ascertain even if imaginative and prescient can be utilized as a sensory procedure in reactive structures. The enormous examine curiosity during this box has been inspired at the very least by means of the subsequent issues: 1. The redundancy of knowledge contained in time-varying photos can over come numerous problems encountered in studying a unmarried photo. 2. there are many vital functions together with computerized automobile driv ing, site visitors keep an eye on, aerial surveillance, scientific inspection and international version building. even if, there are lots of new difficulties which will be solved: tips to effi ciently procedure the considerable info contained in time-varying photos, easy methods to version the swap among photos, find out how to version the uncertainty inherently linked to the imaging method and the way to unravel inverse difficulties that are as a rule ill-posed. there are naturally many percentages for attacking those difficulties and plenty of extra stay to be explored. We talk about some of them during this publication in line with paintings performed over the past 5 years within the desktop imaginative and prescient and Robotics workforce at INRIA (Institut nationwide de Recherche en Informatique et en Automatique).
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Extra resources for 3D Dynamic Scene Analysis: A Stereo Based Approach
4] presented a method to determine motion from planar patches. Haralick et al. 6] presented solutions to pose estimation problems from point data including 3D - 3D and 2D (perspective projection) 3D pose estimation problems. The chapter is organized as follows. First, two representations for line segments are presented, and the problem we address are formulated. 7J but with some modification. Finally we provide the results of our comparative study of different methods.
Let X and xi be the corresponding true values. 22) Yi = It - Xi . , Ho: Y: = 0 against the alternate hypothesis Under hypothesis Ho ("same geometric object") and the assumption that the two estimates have statistically independent errors, the covariance matrix of the difference is given by Ay; = A + Ax; . 24) which is usually called the (squared) Mahalanobis distance betweeri the estimates Xi. We can simply interpret c5J"f as the square of the Euclidean distance between It and Xi weighted by the sum of their covariances.
Another difficulty with this representation is that the covariance matrix cannot correctly characterize the uncertainty of the segment which of [a, b, p, be the intersection of the line with the it supports. For example, let [p, q, qf of 46 4. Representations of Geometric Objects y Fig. 3. The uncertainty in (p, q) does not reflect that of a segment xy-plane. A segment with small uncertainty but far away from that point may give an uncertainty in p and q greater than a segment with bigger uncertainty but nearby that point.