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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date Mar 2000

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Displaying Results 1 - 7 of 7
  • Morphological reversible contour representation

    Page(s): 227 - 240
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB)  

    In this paper, a novel morphological reversible contour representation of discrete binary images is proposed. A binary image is represented by a set of nonoverlapping multilevel contours and a residual image. In this proposed representation, the total number of pixels representing an image is far less than the total number of pixels obtained by the seed-based morphological contour-skeleton lossless representation. The proposed contour representation is simple, unique, and general without restrictions on the binary image to be represented. The resulting multicontour image component is robust to noise. An efficient differential chain contour coding scheme is employed to further compress the represented image. The proposed method yields very low bit rates compared to the existing morphological techniques. An automatic filling procedure, which properly fills a proper multicontour image according to its topological structure without need of seed points, is proposed View full abstract»

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  • A framework for automatic landmark identification using a new method of nonrigid correspondence

    Page(s): 241 - 251
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB)  

    A framework for automatic landmark identification is presented based on an algorithm for corresponding the boundaries of two shapes. The auto-landmarking framework employs a binary tree of corresponded pairs of shapes to generate landmarks automatically on each of a set of example shapes. The landmarks are used to train statistical shape models, known as point distribution models. The correspondence algorithm locates a matching pair of sparse polygonal approximations, one for each of a pair of boundaries by minimizing a cost function, using a greedy algorithm. The cost function expresses the dissimilarity in both the shape and representation error (with respect to the defining boundary) of the sparse polygons. Results are presented for three classes of shape which exhibit various types of nonrigid deformation View full abstract»

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  • Geodesic active contours and level sets for the detection and tracking of moving objects

    Page(s): 266 - 280
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2484 KB)  

    This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences. Motion detection is performed using a statistical framework for which the observed interframe difference density function is approximated using a mixture model. This model is composed of two components, namely, the static (background) and the mobile (moving objects) one. Both components are zero-mean and obey Laplacian or Gaussian law. This statistical framework is used to provide the motion detection boundaries. Additionally, the original frame is used to provide the moving object boundaries. Then, the detection and the tracking problem are addressed in a common framework that employs a geodesic active contour objective function. This function is minimized using a gradient descent method. A new approach named Hermes is proposed, which exploits aspects from the well-known front propagation algorithms and compares favorably to them. Very promising experimental results are provided using real video sequences View full abstract»

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  • The 3L algorithm for fitting implicit polynomial curves and surfaces to data

    Page(s): 298 - 313
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    We introduce a completely new approach to fitting implicit polynomial geometric shape models to data and to studying these polynomials. The power of these models is in their ability to represent nonstar complex shapes in two(2D) and three-dimensional (3D) data to permit fast, repeatable fitting to unorganized data which may not be uniformly sampled and which may contain gaps, to permit position-invariant shape recognition based on new complete sets of Euclidean and affine invariants and to permit fast, stable single-computation pose estimation. The algorithm represents a significant advancement of implicit polynomial technology for four important reasons. First, it is orders of magnitude taster than existing fitting methods for implicit polynomial 2D curves and 3D surfaces, and the algorithms for 2D and 3D are essentially the same. Second, it has significantly better repeatability, numerical stability, and robustness than current methods in dealing with noisy, deformed, or missing data. Third, it can easily fit polynomials of high, such as 14th or 16th, degree. Fourth, additional linear constraints can be easily incorporated into the fitting process, and general linear vector space concepts apply View full abstract»

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  • Consistent gradient operators

    Page(s): 252 - 265
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5528 KB)  

    We propose optimal gradient operators based on a newly derived consistency criterion. This criterion is based on an orthogonal decomposition of the difference between a continuous gradient and discrete gradients into the intrinsic smoothing effect and the self-inconsistency involved in the operator. We show that consistency assures the exactness of gradient direction of a locally 1D pattern in spite of its orientation, spectral composition, and sub-pixel translation. Stressing that inconsistency reduction is of primary importance, we derive an iterative algorithm which leads to accurate gradient operators of arbitrary size. We compute the optimum 3×3, 4×4, and 5×5 operators, compare them with conventional operators and examine the performance for one synthetic and several real images. The results indicate that the proposed operators are superior with respect to accuracy, bandwidth and isotropy View full abstract»

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  • Learning and design of principal curves

    Page(s): 281 - 297
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1108 KB)  

    Principal curves have been defined as “self-consistent” smooth curves which pass through the “middle” of a d-dimensional probability distribution or data cloud. They give a summary of the data and also serve as an efficient feature extraction tool. We take a new approach by defining principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution. The new definition makes it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction. Our theoretical learning scheme chooses a curve from a class of polygonal lines with k segments and with a given total length to minimize the average squared distance over n training points drawn independently. Convergence properties of this learning scheme are analyzed and a practical version of this theoretical algorithm is implemented. In each iteration of the algorithm, a new vertex is added to the polygonal line and the positions of the vertices are updated so that they minimize a penalized squared distance criterion. Simulation results demonstrate that the new algorithm compares favorably with previous methods, both in terms of performance and computational complexity, and is more robust to varying data models View full abstract»

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  • A noise-adaptive discriminant function and its application to blurred machine-printed Kanji recognition

    Page(s): 314 - 319
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (220 KB)  

    Accurate recognition of blurred images is a practical but previously mostly overlooked problem. In the paper, we quantify the level of noise in blurred images and propose a modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns 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