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Pattern Analysis and Machine Intelligence, IEEE Transactions on

Issue 3 • Date March 2003

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Displaying Results 1 - 10 of 10
  • Editorial - State of the Transactions

    Publication Year: 2003 , Page(s): 289
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  • Graphical Gaussian shape models and their application to image segmentation

    Publication Year: 2003 , Page(s): 316 - 329
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2776 KB) |  | HTML iconHTML  

    This paper presents a novel approach to shape modeling and a model-based image segmentation procedure tailor-made for the proposed shape model. A common way to represent shape is based on so-called key points and leads to shape variables, which are invariant with respect to similarity transformations. We propose a graphical shape model, which relies on a certain conditional independence structure among the shape variables. Most often, it is sufficient to use a sparse underlying graph reflecting both nearby and long-distance key point interactions. Graphical shape models allow for specific shape modeling, since, e.g., for the subclass of decomposable graphical Gaussian models both model selection procedures and explicit parameter estimates are available. A further prerequisite to a successful application of graphical shape models in image analysis is provided by the "toolbox" of Markov chain Monte Carlo methods offering highly flexible and effective methods for the exploration of a specified distribution. For Bayesian image segmentation based on a graphical Gaussian shape model, we suggest applying a hybrid approach composed of the well-known Gibbs sampler and the more recent slice sampler. Shape modeling as well as image analysis are demonstrated for the segmentation of vertebrae from two-dimensional slices of computer tomography images. View full abstract»

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  • Illumination from shadows

    Publication Year: 2003 , Page(s): 290 - 300
    Cited by:  Papers (45)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5380 KB) |  | HTML iconHTML  

    In this paper, we introduce a method for recovering an illumination distribution of a scene from image brightness inside shadows cast by an object of known shape in the scene. In a natural illumination condition, a scene includes both direct and indirect illumination distributed in a complex way, and it is often difficult to recover an illumination distribution from image brightness observed on an object surface. The main reason for this difficulty is that there is usually not adequate variation in the image brightness observed on the object surface to reflect the subtle characteristics of the entire illumination. In this study, we demonstrate the effectiveness of using occluding information of incoming light in estimating an illumination distribution of a scene. Shadows in a real scene are caused by the occlusion of incoming light and, thus, analyzing the relationships between the image brightness and the occlusions of incoming light enables us to reliably estimate an illumination distribution of a scene even in a complex illumination environment. This study further concerns the following two issues that need to be addressed. First, the method combines the illumination analysis with an estimation of the reflectance properties of a shadow surface. This makes the method applicable to the case where reflectance properties of a surface are not known a priori and enlarges the variety of images applicable to the method. Second, we introduce an adaptive sampling framework for efficient estimation of illumination distribution. Using this framework, we are able to avoid a unnecessarily dense sampling of the illumination and can estimate the entire illumination distribution more efficiently with a smaller number of sampling directions of the illumination distribution. To demonstrate the effectiveness of the proposed method, we have successfully tested the proposed method by using sets of real images taken in natural illumination conditions with different surface materials of shadow regions. View full abstract»

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  • IMPSAC: synthesis of importance sampling and random sample consensus

    Publication Year: 2003 , Page(s): 354 - 364
    Cited by:  Papers (31)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1783 KB) |  | HTML iconHTML  

    This paper proposes a new method for recovery of epipolar geometry and feature correspondence between images which have undergone a significant deformation, either due to large rotation or wide baseline of the cameras. The method also encodes the uncertainty by providing an arbitrarily close approximation to the posterior distribution of the two view relation. The method operates on a pyramid from coarse to fine resolution, thus raising the problem of how to propagate information from one level to another in a statistically consistent way. The distribution of the parameters at each resolution is encoded nonparametrically as a set of particles. At the coarsest level, a random sample consensus Monte Carlo Markov chain (RANSAC-MCMC) estimator is used to initialize this set of particles, the posterior can then be approximated as a mixture of Gaussians fitted to these particles. The distribution at a coarser level influences the distribution at a finer level using the technique of sampling-importance-resampling (SIR) and MCMC, which allows for asymptotically correct approximations of the posterior distribution. The estimate of the posterior distribution at the level above is being used as the importance sampling function to generate a new set of particles, which can be further improved by MCMC. It is shown that the method is superior to previous single resolution RANSAC-style feature matchers. View full abstract»

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  • Frame-rate spatial referencing based on invariant indexing and alignment with application to online retinal image registration

    Publication Year: 2003 , Page(s): 379 - 384
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (650 KB) |  | HTML iconHTML  

    This paper describes an algorithm to continually and accurately estimate the absolute location of a diagnostic or surgical tool (such as a laser) pointed at the human retina, from a series of image frames. We treat the problem as a registration problem using diagnostic images to build a spatial map of the retina and then registering each online image against this map. Since the image location where the laser strikes the retina is easily found, this registration determines the position of the laser in the global coordinate system defined by the spatial map. For each online image, the algorithm computes similarity invariants, locally valid despite the curved nature of the retina, from constellations of vascular landmarks. These are detected using a high-speed algorithm that iteratively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Robust alignment and verification steps extend the similarity transformation computed from these initial correspondences to a global, high-order transformation. In initial experimentation, the method has achieved 100 percent success on 1024 × 1024 retina images. With a version of the tracing algorithm optimized for speed on 512 × 512 images, the computation time is only 51 milliseconds per image on a 900MHz PentiumIII processor and a 97 percent success rate is achieved. The median registration error in either case is about 1 pixel. View full abstract»

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  • Reconstruction of partially damaged face images based on a morphable face model

    Publication Year: 2003 , Page(s): 365 - 372
    Cited by:  Papers (33)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5220 KB) |  | HTML iconHTML  

    This paper proposes a method for reconstructing partially damaged faces based on a morphable face model. Faces are modeled by linear combinations of prototypes of shape and texture. With the shape and texture information from an undamaged region only, we can estimate optimal coefficients for linear combinations of prototypes of shape and texture by simple projection for least-square minimization (LSM). Our experimental results show that reconstructed faces are very natural and plausible like real photos. View full abstract»

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  • A morphologically optimal strategy for classifier combination: multiple expert fusion as a tomographic process

    Publication Year: 2003 , Page(s): 343 - 353
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (763 KB) |  | HTML iconHTML  

    We specify an analogy in which the various classifier combination methodologies are interpreted as the implicit reconstruction, by tomographic means, of the composite probability density function spanning the entirety of the pattern space, the process of feature selection in this scenario amounting to an extremely bandwidth-limited Radon transformation of the training data. This metaphor, once elaborated, immediately suggests techniques for improving the process, ultimately defining, in reconstructive terms, an optimal performance criterion for such combinatorial approaches. View full abstract»

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  • Outlier modeling in image matching

    Publication Year: 2003 , Page(s): 301 - 315
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4870 KB) |  | HTML iconHTML  

    We address the question of how to characterize the outliers that may appear when matching two views of the same scene. The match is performed by comparing the difference of the two views at a pixel level aiming at a better registration of the images. When using digital photographs as input, we notice that an outlier is often a region that has been occluded, an object that suddenly appears in one of the images, or a region that undergoes an unexpected motion. By assuming that the error in pixel intensity generated by the outlier is similar to an error generated by comparing two random regions in the scene, we can build a model for the outliers based on the content of the two views. We illustrate our model by solving a pose estimation problem: the goal is to compute the camera motion between two views. The matching is expressed as a mixture of inliers versus outliers, and defines a function to minimize for improving the pose estimation. Our model has two benefits: First, it delivers a probability for each pixel to belong to the outliers. Second, our tests show that the method is substantially more robust than traditional robust estimators (M-estimators) used in image stitching applications, with only a slightly higher computational complexity. View full abstract»

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  • Unsupervised feature selection applied to content-based retrieval of lung images

    Publication Year: 2003 , Page(s): 373 - 378
    Cited by:  Papers (58)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (964 KB) |  | HTML iconHTML  

    This paper describes a new hierarchical approach to content-based image retrieval called the "customized-queries" approach (CQA). Contrary to the single feature vector approach which tries to classify the query and retrieve similar images in one step, CQA uses multiple feature sets and a two-step approach to retrieval. The first step classifies the query according to the class labels of the images using the features that best discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features customized to distinguish "subclasses" within that class. Needing to find the customized feature subset for each class led us to investigate feature selection for unsupervised learning. As a result, we developed a new algorithm called FSSEM (feature subset selection using expectation-maximization clustering). We applied our approach to a database of high resolution computed tomography lung images and show that CQA radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with eight radiologists. The results show that our system using CQA retrieval doubled the doctors' diagnostic accuracy. View full abstract»

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  • Watersnakes: energy-driven watershed segmentation

    Publication Year: 2003 , Page(s): 330 - 342
    Cited by:  Papers (57)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2227 KB) |  | HTML iconHTML  

    The watershed algorithm from mathematical morphology is powerful for segmentation. However, it does not allow incorporation of a priori information as segmentation methods that are based on energy minimization. In particular, there is no control of the smoothness of the segmentation result. In this paper, we show how to represent watershed segmentation as an energy minimization problem using the distance-based definition of the watershed line. A priori considerations about smoothness can then be imposed by adding the contour length to the energy function. This leads to a new segmentation method called watersnakes, integrating the strengths of watershed segmentation and energy based segmentation. Experimental results show that, when the original watershed segmentation has noisy boundaries or wrong limbs attached to the object of interest, the proposed method overcomes those drawbacks and yields a better segmentation. 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.

Full Aims & Scope

Meet Our Editors

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