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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 6 • Date June 2003

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Displaying Results 1 - 12 of 12
  • Guest editors' introduction to the special section on perceptual organization in computer vision

    Publication Year: 2003 , Page(s): 641
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    Freely Available from IEEE
  • A topology preserving level set method for geometric deformable models

    Publication Year: 2003 , Page(s): 755 - 768
    Cited by:  Papers (154)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1928 KB) |  | HTML iconHTML  

    Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models-the ability to automatically handle topology changes-turns out to be a liability in applications where the object to be segmented has a known topology that must be preserved. We present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm. View full abstract»

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  • An in-depth study of graph partitioning measures for perceptual organization

    Publication Year: 2003 , Page(s): 642 - 660
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2943 KB) |  | HTML iconHTML  

    In recent years, one of the effective engines for perceptual organization of low-level image features is based on the partitioning of a graph representation that captures Gestalt inspired local structures, such as similarity, proximity, continuity, parallelism, and perpendicularity, over the low-level image features. Mainly motivated by computational efficiency considerations, this graph partitioning process is usually implemented as a recursive bipartitioning process, where, at each step, the graph is broken into two parts based on a partitioning measure. We focus on three such measures, namely, the minimum, average, and normalized cuts. The minimum cut partition seeks to minimize the total link weights cut. The average cut measure is proportional to the total link weight cut, normalized by the sizes of the partitions. The normalized cut measure is normalized by the product of the total connectivity (valencies) of the nodes in each partition. We provide theoretical and empirical insight into the nature of the three partitioning measures in terms of the underlying image statistics. In particular, we consider for what kinds of image statistics would optimizing a measure, irrespective of the particular algorithm used, result in correct partitioning. Are the quality of the groups significantly different for each cut measure? Are there classes of images for which grouping by partitioning does not work well? Also, can the recursive bipartitioning strategy separate out groups corresponding to K objects from each other? In the analysis, we draw from probability theory and the rich body of work on stochastic ordering of random variables. Our major conclusion is that optimization of none of the three measures is guaranteed to result in the correct partitioning of K objects, in the strict stochastic order sense, for all image statistics. View full abstract»

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  • A Bayesian discriminating features method for face detection

    Publication Year: 2003 , Page(s): 725 - 740
    Cited by:  Papers (87)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8826 KB) |  | HTML iconHTML  

    This paper presents a novel Bayesian discriminating features (BDF) method for multiple frontal face detection. The BDF method, which is trained on images from only one database, yet works on test images from diverse sources, displays robust generalization performance. The novelty of this paper comes from the integration of the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1D Harr wavelet representation, and its amplitude projections. While the Harr wavelets produce an effective representation for object detection, the amplitude projections capture the vertical symmetric distributions and the horizontal characteristics of human face images. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. While the face class is usually modeled as a multivariate normal distribution, the nonface class is much more difficult to model due to the fact that it includes "the rest of the world." The estimation of such a broad category is, in practice, intractable. However, one can still derive a subset of the nonfaces that lie closest to the face class, and then model this particular subset as a multivariate normal distribution. View full abstract»

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  • Expandable Bayesian networks for 3D object description from multiple views and multiple mode inputs

    Publication Year: 2003 , Page(s): 769 - 774
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1805 KB) |  | HTML iconHTML  

    Computing 3D object descriptions from images is an important goal of computer vision. A key problem here is the evaluation of a hypothesis based on evidence that is uncertain. There have been few efforts on applying formal reasoning methods to this problem. In multiview and multimode object description problems, reasoning is required on evidence features extracted from multiple images and nonintensity data. One challenge here is that the number of the evidence features varies at runtime because the number of images being used is not fixed and some modalities may not always be available. We introduce an augmented Bayesian network, the expandable Bayesian network (EBN), which instantiates its structure at runtime according to the structure of input. We introduce the use of hidden variables to handle correlation of evidence features across images. We show an application of an EBN to a multiview building description system. Experimental results show that the proposed method gives significant and consistent performance improvement to others. View full abstract»

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  • Contrast restoration of weather degraded images

    Publication Year: 2003 , Page(s): 713 - 724
    Cited by:  Papers (143)  |  Patents (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3060 KB) |  | HTML iconHTML  

    Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. We present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera. View full abstract»

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  • Feature selection for multiclass discrimination via mixed-integer linear programming

    Publication Year: 2003 , Page(s): 779 - 783
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (444 KB) |  | HTML iconHTML  

    We reformulate branch-and-bound feature selection employing L or particular Lp metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings. View full abstract»

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  • Image segmentation with ratio cut

    Publication Year: 2003 , Page(s): 675 - 690
    Cited by:  Papers (80)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6363 KB) |  | HTML iconHTML  

    This paper proposes a new cost function, cut ratio, for segmenting images using graph-based methods. The cut ratio is defined as the ratio of the corresponding sums of two different weights of edges along the cut boundary and models the mean affinity between the segments separated by the boundary per unit boundary length. This new cost function allows the image perimeter to be segmented, guarantees that the segments produced by bipartitioning are connected, and does not introduce a size, shape, smoothness, or boundary-length bias. The latter allows it to produce segmentations where boundaries are aligned with image edges. Furthermore, the cut-ratio cost function allows efficient iterated region-based segmentation as well as pixel-based segmentation. These properties may be useful for some image-segmentation applications. While the problem of finding a minimum ratio cut in an arbitrary graph is NP-hard, one can find a minimum ratio cut in the connected planar graphs that arise during image segmentation in polynomial time. While the cut ratio, alone, is not sufficient as a baseline method for image segmentation, it forms a good basis for an extended method of image segmentation when combined with a small number of standard techniques. We present an implemented algorithm for finding a minimum ratio cut, prove its correctness, discuss its application to image segmentation, and present the results of segmenting a number of medical and natural images using our techniques. View full abstract»

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  • Contour grouping with prior models

    Publication Year: 2003 , Page(s): 661 - 674
    Cited by:  Papers (26)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3330 KB) |  | HTML iconHTML  

    Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. Here, we address the problem of finding the bounding contour of an object in an image when some prior knowledge about the object is available. We introduce a framework for combining prior probabilistic knowledge of the appearance of the object with probabilistic models for contour grouping. A constructive search technique is used to compute candidate closed object boundaries, which are then evaluated by combining figure, ground, and prior probabilities to compute the maximum a posteriori estimate. A significant advantage of our formulation is that it rigorously combines probabilistic local cues with important global constraints such as simplicity (no self-intersections), closure, completeness, and nontrivial scale priors. We apply this approach to the problem of computing exact lake boundaries from satellite imagery, given approximate prior knowledge from an existing digital database. We quantitatively evaluate the performance of our algorithm and find that it exceeds the performance of human mapping experts and a competing active contour approach, even with relatively weak prior knowledge. While the priors may be task-specific, the approach is general, as we demonstrate by applying it to a completely different problem: the computation of human skin boundaries in natural imagery. View full abstract»

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  • Mosaicing new views: the Crossed-Slits projection

    Publication Year: 2003 , Page(s): 741 - 754
    Cited by:  Papers (56)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4695 KB) |  | HTML iconHTML  

    We introduce anew kind of mosaicing, where the position of the sampling strip varies as a function of the input camera location. The new images that are generated this way correspond to a new projection model defined by two slits, termed here the Crossed-Slits (X-Slits) projection. In this projection model, every 3D point is projected by a ray defined as the line that passes through that point and intersects the two slits. The intersection of the projection rays with the imaging surface defines the image. X-Slits mosaicing provides two benefits. First, the generated mosaics are closer to perspective images than traditional pushbroom mosaics. Second, by simple manipulations of the strip sampling function, we can change the location of one of the virtual slits, providing a virtual walkthrough of a X-Slits camera; all this can be done without recovering any 3D geometry and without calibration. A number of examples where we translate the virtual camera and change its orientation are given; the examples demonstrate realistic changes in parallax, reflections, and occlusions. View full abstract»

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  • The generic viewpoint assumption and planar bias

    Publication Year: 2003 , Page(s): 775 - 778
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (257 KB) |  | HTML iconHTML  

    We show that generic viewpoint and lighting assumptions resolve standard visual ambiguities by biasing toward planar surfaces. Our model uses orthographic projection with a two-dimensional affine warp and Lambertian reflectance functions, including cast and attached shadows. We use uniform priors on nuisance variables such as viewpoint direction and the light source. Limitations of using uniform priors on nuisance variables are discussed. View full abstract»

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  • Statistical modeling and conceptualization of visual patterns

    Publication Year: 2003 , Page(s): 691 - 712
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4330 KB) |  | HTML iconHTML  

    Natural images contain an overwhelming number of visual patterns generated by diverse stochastic processes. Defining and modeling these patterns is of fundamental importance for generic vision tasks, such as perceptual organization, segmentation, and recognition. The objective of this epistemological paper is to summarize various threads of research in the literature and to pursue a unified framework for conceptualization, modeling, learning, and computing visual patterns. This paper starts with reviewing four research streams: 1) the study of image statistics, 2) the analysis of image components, 3) the grouping of image elements, and 4) the modeling of visual patterns. The models from these research streams are then divided into four categories according to their semantic structures: 1) descriptive models, i.e., Markov random fields (MRF) or Gibbs, 2) variants of descriptive models (causal MRF and "pseudodescriptive" models), 3) generative models, and 4) discriminative models. The objectives, principles, theories, and typical models are reviewed in each category and the relationships between the four types of models are studied. Two central themes emerge from the relationship studies. 1) In representation, the integration of descriptive and generative models is the future direction for statistical modeling and should lead to richer and more advanced classes of vision models. 2) To make visual models computationally tractable, discriminative models are used as computational heuristics for inferring generative models. Thus, the roles of four types of models are clarified. 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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Meet Our Editors

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