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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date May 1987

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Displaying Results 1 - 16 of 16
  • [Front cover]

    Page(s): c1
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  • List of Contributors

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  • [Breaker page]

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  • The Local Structure of Image Discontinuities in One Dimension

    Page(s): 341 - 355
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    The detailed structure of intensities in the local neighborhood of an edge can often indicate the nature of the physical event givinig rise to that edge. We argue that the limit, as we approach arbitrarily close to either side of an edge, of such image parameters as type of texture, texture gradient, color, appropriate directional derivatives of intensity, etc., is a key aspect of this structure. However, the general problem of capturing this local structure is surprisingly complex. Thus, we restrict ourselves in this paper to a relatively simple domain¿one-dimensional cuts through idealized images modeled by piecewise smooth (C1) functions corrupted by Gaussian noise. Within this domain, we define local structure to be the limit of the uncorrupted intensity and of its derivatives as we approach arbitrarily close to either side of a discontinuity. We develop a technique that captures this local structure while simultaneously locating the discontinuities, and demonstrate that these tasks are in fact inseparable. The technique is an extension, using estimation theory, of the classical definition of discontinuity. It handles, in a consistent fashion, both jump discontinuities in the function and jump discontinuities in its first derivative (so-called step-edges are a special case of the former and roof-edges of the latter). It also integrates, again in a consistent fashion, information derived from a number of different neighborhood sizes. View full abstract»

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  • Motion Stereo Using Ego-Motion Complex Logarithmic Mapping

    Page(s): 356 - 369
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    Stereo information can be obtained using a moving camera. If a dynamic scene is acquired using a translating camera and the camera motion parameters are known, then the analysis of the scene may be facilitated by ego-motion complex logarithmic mapping (ECLM). It is shown in this paper that by using the complex logarithmic mapping (CLM) with respect to the focus of expansion, the depth of stationary components can be determined easily in the transformed image sequence. The proposed approach for depth recovery avoids the difficult problems of establishing correspondence and computation of optical flow, by using the ego-motion information. An added advantage of the CLM will be the invariances it offers. We report our experiments with synthetic data to show the sensitivity of the depth recovery, and show results of real scenes to demonstrate the efficacy of the proposed motion stereo in applications such as autonomous navigation. View full abstract»

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  • 3-D Motion Estimation, Understanding, and Prediction from Noisy Image Sequences

    Page(s): 370 - 389
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    This paper presents an approach to understanding general 3-D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Specifically, the model constrains the motion, over a local frame subsequence, to be a superposition of precession and translation. Thus, the instantaneous rotation axis of the object is allowed to change through the subsequence. The trajectory of the rotation center is approximated by a vector polynomial. The parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics. The nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence. The estimation algorithm presented in this paper is linear, i.e., the algorithm consists of solving simultaneous linear equations. Based on the assumption that the motion is smooth, object positions and motion in the near future can be predicted, and short missing subsequences can be recovered. Noise smoothing is achieved by overdetermination and a leastsquares criterion. The framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames. View full abstract»

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  • Matching Perspective Views of a Polyhedron Using Circuits

    Page(s): 390 - 400
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    We present a novel approach for finding corresponding points between two line drawings extracted from perspective views of a moving object whose surface is composed of planar polygons. In our approach, each circuit of the drawings is encoded with a boundary shape code which we call the RLCC code (run length code of convex and concave strings), then a clustering technique is used to obtain the matching result recursively. A series of measures are taken to make the algorithm tolerate considerable dissimilarities which may exist between the two drawings, such as missing lines, scale differences, rotation, perspective shape distortions, etc. Experimental results are presented. View full abstract»

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  • New Methods for Matching 3-D Objects with Single Perspective Views

    Page(s): 401 - 412
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    In this paper we analyze the ability of a computer vision system to derive properties of the three-dimensional (3-D) physical world from viewing two-dimensional (2-D) images. We present a new approach which consists of a model-based interpretation of a single perspective image. Image linear features and linear feature sets are backprojected onto the 3-D space and geometric models are then used for selecting possible solutions. The paper treats two situations: 1) interpretation of scenes resulting from a simple geometric structure (orthogonality) in which case we seek to determine the orientation of this structure relatively to the viewer (three rotations) and 2) recognition of moderately complex objects whose shapes (geometrical and topological properties) are provided in advance. The recognition technique is limited to objects containing, among others, straight edges and planar faces. In the first case the computation can be carried out by a parallel algorithm which selects the solution that has received the largest number of votes (accumulation space). In the second case an object is uniquely assigned to a set of image features through a search strategy. As a by-product, the spatial position and orientation (six degrees of freedom) of each recognized object is determined as well. The method is valid over a wide range of perspective images and it does not require perfect low-level image segmentation. It has been successfully implemented for recognizing a class of industrial parts. View full abstract»

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  • Iconic Indexing by 2-D Strings

    Page(s): 413 - 428
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    In this paper, we describe a new way of representing a symbolic picture by a two-dimensional string. A picture query can also be specified as a 2-D string. The problem of pictorial information retrieval then becomes a problem of 2-D subsequence matching. We present algorithms for encoding a symbolic picture into its 2-D string representation, reconstructing a picture from its 2-D string representation, and matching a 2-D string with another 2-D string. We also prove the necessary and sufficient conditions to characterize ambiguous pictures for reduced 2-D strings as well as normal 2-D strings. This approach thus allows an efficient and natural way to construct iconic indexes for pictures. View full abstract»

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  • Polyhedra Recognition by Hypothesis Accumulation

    Page(s): 429 - 438
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    A new method is presented for the recognition of polyhedra in range data. The method is based on a hypothesis accumulation scheme which allows parallel implementations. The different objects to be recognized are modeled by a set of local geometrical patterns. Local patterns of the same nature are extracted from the scene. For the recognition of an object, local scene and model patterns having the same geometrical characteristics are matched. For each of the possible matches, the geometric transformations (i.e., rotations and translations) are computed, which allows the overlapping of the model elements with those from the scene. This transformation permits the establishment of a hypothesis on the location of the object in the scene and the determination of a point in the transformation space. The presence of an object similar to a model involves the generation of several compatible hypotheses and creates a compact cluster in the transformation space. The recognition of the object is based on the detection of this cluster. The cluster coordinates give the values of the rotations and the translations to be applied to the model such that it corresponds to the object in the scene. The exact location of this object is given by the transformed model. View full abstract»

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  • Forward/Backward Contour Tracing with Feedback

    Page(s): 438 - 446
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    This correspondence describes a contour extraction algorithm which can gradually improve its results until the extracted contours are closed. This is achieved by an architecture with a feedback path for local smoothing. The feedback path is activated only when one or more contours obtained are not closed in order to initiate smoothing in noisy areas of the image to remove local irregularities that cause the problems. A forward/backward boundary tracing mechanism is employed to facilitate locating any troubled areas. A smoothing method appropriate for reducing local irregularities is discussed. The proposed algorithm is very suitable for those applications that demand closed contours, such as character recognition and blob detection. View full abstract»

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  • Edge-Detector Resolution Improvement by Image Interpolation

    Page(s): 446 - 451
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    Most step-edge detectors are designed to detect locally straight edge-segments which can be isolated within the operator kernel. While it can easily be demonstrated that a cross-sectional support of at least 4 pixels is required for the unambiguous detection of a stepedge, edges which cannot be isolated within windows having this width can nevertheless be resolved. This is achieved by preceding the stepedge detection process by image-intensity interpolation. Although resolution can be improved in this fashion, the step-edge position and intensity estimates thus determined may be subject to systematic biases. Also, the higher resolution performance is accompanied by lower robustness to noise. View full abstract»

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  • A Robust Filtering Algorithm for Subpixel Reconstruction of Chain Coded Line Drawings

    Page(s): 451 - 457
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    A robust algorithm is presented for smoothing and achieving subpixel accuracy in the reconstruction of chain coded line drawings. The algorithm does not remove sharp corners and does not need a priori knowledge of curvature statistics. A fast on-line implementation can be achieved using a table look-up. A simplified algorithm can be used for reconstructing digitized polygons. View full abstract»

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  • Stochastic Model Utilizing Spectral and Spatial Characteristics

    Page(s): 457 - 461
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    In remote sensing, because of physical properties of targets, sensor pixels in spatial proximity to one another are class conditionally correlated. Our main objective is to exploit this spatial correlation. Therefore, a two-dimensional causal first order Markov model was used to extract the spatial and spectral information and, based upon it, new object classifiers with improved performance were developed. First, the minimum distance (MT) and the maximum likelihood (ML) object classifiers are discussed. Then, based on the proposed model, these two classifiers are modified, and a linear object classifier is introduced. Finally, experimental results are presented. View full abstract»

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  • Recursive Algorithms for Implementing Digital Image Filters

    Page(s): 461 - 466
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    The B-spline functions are used to develop recursive algorithms for the efficient implementation of two-dimensional linear digital image filters. These filters may be spatially varying. The B-splines are used in a representation of the desired point spread function. We show that this leads to recursive algorithms and hardware implementations which are more efficient than either direct spatial domain filter realizations or FFT implementations. The Z-transform is used to develop a discrete version of Duhamel's theorem. A computer architecture for B-spline image filters is proposed and a complexity analysis and comparison to other approaches is provided. View full abstract»

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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

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Editor-in-Chief
David A. Forsyth
University of Illinois