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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.


Papers
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Journal ArticleDOI
TL;DR: An automatic method of inspecting the scaling accuracy of needle-type instrument gauges using a two-stage Hough transform technique is described, which is very suitable for high-speed hardware implementation.
Abstract: An automatic method of inspecting the scaling accuracy of needle-type instrument gauges using a two-stage Hough transform technique is described. The system measures and verifies the relative accuracy of a gauge's response to a specified set of analog input signals. The method does not require that the gauge's position, orientation, or size be known a priori and the algorithm is very suitable for high-speed hardware implementation.

31 citations

Proceedings ArticleDOI
16 Aug 2008
TL;DR: The study shows that the semantically enhanced layout is preferred by non-native speakers, suggesting it has the potential to be useful for people with other forms of limited literacy, too.
Abstract: Pictorial communication systems convert natural language text into pictures to assist people with limited literacy. We define a novel and challenging problem: picture layout optimization. Given an input sentence, we seek the optimal way to lay out word icons such that the resulting picture best conveys the meaning of the input sentence. To this end, we propose a family of intuitive "ABC" layouts, which organize icons in three groups. We formalize layout optimization as a sequence labeling problem, employing conditional random fields as our machine learning method. Enabled by novel applications of semantic role labeling and syntactic parsing, our trained model makes layout predictions that agree well with human annotators. In addition, we conduct a user study to compare our ABC layout versus the standard linear layout. The study shows that our semantically enhanced layout is preferred by non-native speakers, suggesting it has the potential to be useful for people with other forms of limited literacy, too.

29 citations

Journal ArticleDOI
01 May 1984
TL;DR: An image segmentation algorithm is described that uses an overlapped pyramid to represent an image at multiple levels of resolution to create a forest embedded within the pyramid.
Abstract: An image segmentation algorithm is described that uses an overlapped pyramid to represent an image at multiple levels of resolution. The procedure `lifts' objects to levels of lower and lower resolution until they become `spot' or `streaklike' and are identifiable by local processing (using 3 by 3 operators). They are then `rooted.' The result is a forest embedded within the pyramid, with the single tree rooted at the pyramid apex representing the background regions and the remaining trees representing compact object regions. In addition to the definition of the pyramid linking algorithm, the convergence of the algorithm is proved and optimal rooting rules for binary images are analyzed.

28 citations

ReportDOI
01 May 1979
TL;DR: In this paper, an edge-based procedure for extracting primitives from textures was proposed, which groups edges into region boundaries by joining facing pairs of edge points, and a pilot evaluation was performed by examining the usefulness of these primitives for texture classification.
Abstract: : Many textures are characterizable as a collection of primitive elements arranged over a background field. This paper defines an edge-based procedure for extracting primitives from textures. The technique groups edges into region boundaries by joining facing pairs of edge points. A pilot evaluation is performed by examining the usefulness of these primitives for texture classification. (Author)

28 citations

Journal ArticleDOI
TL;DR: In this article, the authors define dynamic perceptual organization as an extension of the traditional (static) perceptual organization approach, and propose a new paradigm for motion understanding and show why it can be done independently of the recovery of scene structure and scene motion.
Abstract: To date, the overwhelming use of motion in computational vision has been to recover the three-dimensional structure of the scene. We propose that there are other, more powerful, uses for motion. Toward this end, we define dynamic perceptual organization as an extension of the traditional (static) perceptual organization approach. Just as static perceptual organization groups coherent features in an image, dynamic perceptual organization groups coherent motions through an image sequence. Using dynamic perceptual organization, we propose a new paradigm for motion understanding and show why it can be done independently of the recovery of scene structure and scene motion. The paradigm starts with a spatiotemporal cube of image data and organizes the paths of points so that interactions between the paths, and perceptual motions such as common , relative , and cyclic are made explicit. The results of this can then be used for high-level motion recognition tasks.

25 citations


Cited by
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Journal ArticleDOI
Paul J. Besl1, H.D. McKay1
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, 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.
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. >

17,598 citations

Journal ArticleDOI
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
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.

10,501 citations

Journal ArticleDOI
TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new 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.

7,458 citations

Journal ArticleDOI
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.

6,650 citations

MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
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.

6,340 citations