Charles R. Dyer

Bio: Charles R. Dyer is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Motion estimation & Motion field. The author has an hindex of 43, co-authored 141 publications receiving 9919 citations. Previous affiliations of Charles R. Dyer include University of Wisconsin System & University of Maryland, College Park.

Automated thermal coagulation segmentation of three-dimensional elastographic imaging using an active contour model

Abstract: Delineation of RF-ablator induced coagulation (thermal lesion) boundaries is an important clinical problem not well addressed by conventional imaging modalities. Automation of this process is certainly desirable. Elastography, that estimates and images the local strain corresponding to small, externally applied, quasi-static compressions, can be used for visualization of thermal coagulations. Several studies have demonstrated that coagulation volumes computed from multiple planar slices through the region of interest are more accurate than volumes estimated assuming simple shapes and incorporating single or orthogonal diameter estimates. The paper presents an automated segmentation approach for thermal coagulations on three-dimensional elastographic data to obtain both area and volume information. This approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the proposed algorithm is shown to be comparable to manual delineation by medical physicists (r=0.99 for 36 RF-induced coagulations). The correlation coefficient of the coagulation volume between auto-segmented elastography and manually-delineated pathology is 0.96.

Dynamic view interpolation without affine reconstruction

Abstract: This chapter presents techniques for view interpolation between two reference views of a dynamic scene captured at different times. The interpolations produced portray one possible physically-valid version of what transpired in the scene during the time between when the two reference views were taken. We show how straight-line object motion, relative to a camera-centered coordinate system, can be achieved, and how the appearance of straight-line object motion relative to the background can be created. The special case of affine cameras is also discussed. The methods presented work with widely-separated, uncalibrated cameras and sparse point correspondences. The approach does not involve finding the camera-to-camera transformation and thus does not implicitly perform affine reconstruction of the scene. For circumstances in which the camera-to-camera transformation can be found, we introduce a vector-space of possible synthetic views that follows naturally from the given reference views. It is assumed that the motion of each object in the original scene consists of a series of rigid translations.

Building global surface models by purposive and qualitative viewpoint adjustment

Abstract: We present an approach for recovering a global surface model of an object from the deformation of the occluding contour using an active (i.e., mobile) observer able to control its motion. In particular, we consider two problems: (1) How can the observer's viewpoint be controlled in order to generate a dense sequence of images that allows incremental reconstruction of an unknown surface? And (2) how can we construct a global surface model from the generated image sequence? We achieve the first goal by purposefully and qualitatively controlling the observer's instantaneous direction of motion in order to control the motion of the visible rim over the surface. We achieve the second goal by using a stationary calibrated trinocular camera rig and a mechanism for controlling the relative position and orientation of the viewed surface with respect to the trinocular rig. Unlike previous shape-from-motion approaches which derive quantitative shape information from an arbitrarily generated sequence of images, we develop a collection of simple and efficient viewing strategies that allow the observer to achieve the global reconstruction goal by maintaining specific geometric relationships with the viewed surface. These relationships depend only on tangent computations on the occluding contour. To demonstrate the feasibility and effectiveness of our approach we apply the developed algorithms to synthetic and real scenes.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Distributed curve matching in camera networks using projective joint invariant signatures

Abstract: An efficient method based on projective joint invariant signatures is presented for distributed matching of curves in a camera network The fundamental projective joint invariants for curves in the real projective space are the volume cross-ratios A curve in m-dimensional projective space is represented by a signature manifold comprising n-point projective joint invariants, where n is at least m + 2 The signature manifold can be used to establish equivalence of two curves in projective space However, without correspondence between the two curves, matching signature manifolds is a computational challenge In this paper we overcome this challenge by finding discriminative sections of signature manifolds consistently across varying viewpoints and scoring the similarity between these sections This motivates a simple yet powerful method for distributed curve matching in a camera network Experimental results with real data demonstrate the classification performance of the proposed algorithm with respect to the size of the sections of the invariant signature in various noisy conditions

Research on Self Calibration Without Minimization

Abstract: In this paper we present a new metric camera self-calibration algorithm that does not require the global minimization of an error function and can produce all legal solutions to the three-camera self-calibration problem in a single pass. By contrast, virtually all previous self-calibration algorithms rely on nonlinear global optimization unless special assumptions are made about the camera or its motion. The key drawback to global-optimizationbased methods is that, for nontrivial error functions, they can run indefinitely. Therefore, because our new algorithm produces all solutions quickly and in a fixed amount of time, it is arguably the fastest self-calibration algorithm in existence. In addition, our algorithm makes it possible to determine experimentally the number of solutions to the three-camera self-calibration problem; an upper-bound of 21 was given by Schaffilitzky [17], but our experiments show this number is more typically 1 or 2. Finally, because our algorithm runs very quickly and requires only the theoretical minimum of three camera views, it can be used in conjunction with RANSAC for great robustness to noise when more than three views are available. This work was partially sponsored by the National Science Foundation under Grant No. IIS-9988426 and by the Defense Advanced Research Projects Agency (DARPA) and Rome Laboratory, Air Force Materiel Command, USAF, under agreement number F3060297-1-0138.

A method for registration of 3-D shapes

Abstract: 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. >

Object Detection with Discriminatively Trained Part-Based Models

Abstract: 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.

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

Abstract: 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.

Planning Algorithms: Introductory Material

Abstract: 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.