The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >

A method for registration of 3-D shapes

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

/pdf/object-detection-with-discriminatively-trained-part-based-4cnosvuqcp.pdf

Object Detection with Discriminatively Trained Part-Based Models

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

/pdf/a-taxonomy-and-evaluation-of-dense-two-frame-stereo-4c20etkubp.pdf

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

A comparative study of texture measures with classification based on featured distributions

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper introduces a novel linear algorithm for determining the affine calibration between two camera views of a dynamic scene. The affine calibration is computed directly from the fundamental matrices associated with various moving objects in the scene, as well as from the fundamental matrix for the static background if the cameras are at different locations. A minimum of two fundamental matrices are required, but any number of additional fundamental matrices can be incorporated into the linear system to improve the stability of the computation. The technique is demonstrated on both real and synthetic data.

Affine calibration from moving objects

: This paper generalizes cellular automata by allowing the memory size associated with each cell to be a function of the input size. In particular, we define a cellular analog to the tape-bounded Turing machine for bounded cellular, pyramid cellular, and parallel/sequential automata. We focus on the case in which each cell has memory size proportional to the logarithm of the input size, showing the increased capabilities of these machines for executing a variety of basic image analysis and recognition tasks. (Author)

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA065328&Location=U2&doc=GetTRDoc.pdf

Memory-Augmented Cellular Automata for Image Analysis.

Exploring temporal coherence among light transport paths i s very important to remove temporally perception-sensitiv e artifacts in animation rendering. Using the contribution o f a light transport path to all frames in an animation as the sampling distribution function allows us to adapt Markov Ch ain Monte Carlo (MCMC) algorithms to exploit the temporal and spatial coherence among paths in order to generate a p rceptually pleasant animation. A new perturbation technique calledtime perturbationis developed to explore the temporal coherence among paths. Furthermore, in order to make animation rendering plausible, we distribute itera ive computational tasks to a pool of computers for parallel computation. Each task is rendered with a set of parameters a dapted according to the local properties of each task. We demonstrate that this local adaptation does not introduce b ias statistically. The resulting animations are perceptua lly better than those rendered in a frame-by-frame manner.

Physically-based Animation Rendering with Markov Chain Monte Carlo

Three-dimensional shape representation

This paper introduces a novel, viewer-centered approach to modeling the geometry of the visible occludingcontour of solid 3D shape. A description of the change in appearance of the rim and occluding contour as a functionof viewpoint allows the organization of features of the occluding contour for indexing and matching. This organiza-tion makes the features of the occluding contour explicit for matching in a dynamic context where image features are changing over time, and in a static context where matching methods must iteratively refine an estimation of viewpoint. The rim appearance representation models the exact appearance of the occluding contour formed by the edges of a polyhedron that is assumed to be an approximation of a smooth shape. An algorithm is presented for constructing the rim appearance representation. Bounds on space and time are given, and implementation resultsshow that the rim appearance representation is significantly smaller than the aspect graph and the aspect representa-tion.

Charles R. Dyer

Papers

Affine calibration from moving objects

Memory-Augmented Cellular Automata for Image Analysis.

Physically-based Animation Rendering with Markov Chain Monte Carlo

Three-dimensional shape representation

Representing the dynamics of the occluding contour