AbstractÐWe present an integrated approach to the derivation of scene descriptions from a pair of stereo images, where the steps of feature correspondence and surface reconstruction are addressed within the same framework. Special attention is given to the development of a methodology with general applicability. In order to handle the issues of noise, lack of image features, surface discontinuities, and regions visible in one image only, we adopt a tensor representation for the data and introduce a robust computational technique called tensor voting for information propagation. The key contributions of this paper are twofold: First, we introduce ªsaliencyº instead of correlation scores as the criterion to determine the correctness of matches and the integration of feature matching and structure extraction. Second, our tensor representation and voting as a tool enables us to perform the complex computations associated with the formulation of the stereo problem in three dimensions at a reasonable computational cost. We illustrate the steps on an example, then provide results on both random dot stereograms and real stereo pairs, all processed with the same parameter set. Index TermsÐBinocular stereo, tensor voting, perceptual grouping, surface inference. 1

Abstract- A new general registration method for images of different nature is presented in this paper. As grey-levels or textures cannot be used for the registration of images from separate spectral bands, an edge-based method has been developed. Edge images are processed to extract straight linear segments, which are then grouped to form triangles. A set of candidate transformations is determined by matching triangles from the source and destination images. The transformations are then evaluated by matching the transformed set of source segments to the set of destination segments. As the coincidence of vertices or edge overlapping cannot be assumed in the registration of images of different nature, a new function for evaluating the matching quality between source and destination segments which does not rely on overlapping measures is proposed. Results and subjective evaluation of the registration of visual and thermal infrared images are presented.

This paper proposes a novel optimization algorithm for image-space matching and three-dimensional space analysis, using an adapted scheme of evolutionary computation that employs the concept of symbiosis in a collective of homogeneous populations. It is applied to the automatic generation of disparity surfaces used for depth estimation in stereo vision. The global task of approximating the complete disparity surface is decomposed to a large number of smaller local problems, each solvable by a smaller processing unit. Coevolution is sustained in such a way as to counteract the arbitrary decomposition of the original super-problem, so that the local evolutions of all the subproblems become interlocked. This, in the long run, provides a consistent global solution, and it does so via an asynchronous and massively parallel architecture. The entire surface is partitioned to a set of adjoining patches represented by distinct species or populations, with phenotypes corresponding to different polynomial functionals. The credit assignment functions take into account both self and symbiotic terms in an adaptive and dynamic manner, in order to produce disparity patches that are fit within their own domain and at the same time fit in association with their symbionts. This persistent propagation of local interactions to a global scale throughout evolution generates a unified disparity surface composed of the many smaller patch surfaces.

........................................................................................... x CHAPTER 1 INTRODUCTION ...............................................................................1 1.1 WHAT IS IMAGE-BASED RENDERING AND MODELING...........................................1 1.2 ISSUES.................................................................................................................4 1.3 OBJECTIVE AND APPROACH .................................................................................6 1.4 OUTLINE AND NOTATIONS ...................................................................................8 CHAPTER 2 ELEMENTS OF PROJECTIVE GEOMETRY ...............................10 2.1 PROJECTIVE SPACE............................................................................................10 2.1.1 Definitions .................................................................................................10 2.1.2 Canonical affine space embedding and the plane-at-...

In this paper, we propose a novel approach to stereo correspondence based on the optimisation of a continuous disparity surface defined parametrically using Radial Basis Functions. Principal advantages over other methods include the use of Constrained Non Linear Programming to perform regularisation as a Hierarchical Multiobjective Optimisation which differs from the standard Weighted Sum approach, so that regularisation becomes more consistent with the notion of Pareto Optimality. Furthermore, the optimisation algorithm is capable of handling arbitrary constraints on the sought parameters, so that a variety of types of a priori scene information can be incorporated explicitly to the problem definition. To exemplify this we derive a new continuous unary formulation of the Disparity Gradient Limit constraint and propose other types of potential constraints for a priori knowledge. Furthermore, the optimisation employs a smoothness oriented regularisation operator to preserve surface discontinuities, a flexible block decomposition approach of the disparity surface to allow parallelisation and a correlation-based fitting with heuristics to initialise the parameters and avoid local optima effectively. Experiments with standard stereo imagery show that the method handles adequately the imposed constraints and produces surfaces with accurate level of elevation detail. Keywords stereo correspondence, multiobjective constrained non-linear optimisation, disparity gradient limit I.

We propose a new solution to stereo matching which is combined the area-based stereo method using hierarchical windows with the pixel-based stereo methods using global optimization. The areabased method has a problem that the best size of area is unknown. Since the proposed method changes the sizes of windows hierarchically, the local minima problem and a-priori knowledge of the size can be removed. The proposed method brings better robustness in the analysis of stereo images having periodic texture and textureless areas of images, and a scene having abrupt disparity changes, compared to the ordinary stereo method using hierarchical multi-resolution approach. 1

SUMMARY: This paper presents a preliminary crash avoidance framework for heavy equipment control systems. Safe equipment operation is a major concern on construction sites since fatal on-site injuries are an industry-wide problem. The proposed framework has potential for effecting active safety for equipment operation. The framework contains algorithms for spatial modeling, object tracking, and path planning. Beyond generating spatial models in fractions of seconds, these algorithms can successfully track objects in an environment and produce a collision-free 3D motion trajectory for equipment.