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351
Similarity Searching in Medical Image DataBases
, 1997
"... We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of "labeled" ..."
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Cited by 95 (8 self)
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We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of "labeled" or "expected" objects (e.g., "heart", "lungs" etc.) are common in all images of a given application domain in addition to a variable number of "unexpected" or "unlabeled" objects (e.g., "tumor", "hematoma" etc.). The method can answer queries by example such as "find all Xrays that are similar to Smith's Xray". The stored images are mapped to points in a multidimensional space and are indexed using stateoftheart database methods (Rtrees). The proposed method has several desirable properties: (a) Database search is approximate so that all images up to a prespecified degree of similarity (tolerance) are retrieved, (b) it has no "false dismissals" (i.e., all images qualifying query selection criteria are retrieved) and (c) it is much faster than sequential scanning for searching in the main memory and on the disk (i.e., by up to an order of magnitude) thus scalingup well for large databases.
A Stochastic Grammar of Images
 Foundations and Trends in Computer Graphics and Vision
, 2006
"... This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents bot ..."
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Cited by 85 (20 self)
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This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and nonterminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple Andâ€“Or graph representation where each Ornode points to alternative subconfigurations and an Andnode is decomposed into a number of components. This representation supports recursive topdown/bottomup procedures for image parsing under the Bayesian framework and make it convenient to scale
Representation and Detection of Deformable Shapes
 PAMI
, 2004
"... We describe some techniques that can be used to represent and detect deformable shapes in images. The main di#culty with deformable template models is the very large or infinite number of possible nonrigid transformations of the templates. This makes the problem of finding an optimal match of a ..."
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Cited by 83 (3 self)
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We describe some techniques that can be used to represent and detect deformable shapes in images. The main di#culty with deformable template models is the very large or infinite number of possible nonrigid transformations of the templates. This makes the problem of finding an optimal match of a deformable template to an image incredibly hard. Using a new representation for deformable shapes we show how to e#ciently find a global optimal solution to the nonrigid matching problem. The representation is based on the description of objects using triangulated polygons. Our matching algorithm can minimize a large class of energy functions, making it applicable to a wide range of problems. We present experimental results of detecting shapes in medical images and images of natural scenes. Our method does not depend on initialization and is very robust, yielding good matches even in images with high clutter.
Tracking people by learning their appearance
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
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Signal Matching Through Scale Space
 International Journal of Computer Vision
, 1987
"... Given a collection of similar signals that have been deformed with respect to each other, the general signalmatching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term. The minimization reduc ..."
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Cited by 78 (4 self)
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Given a collection of similar signals that have been deformed with respect to each other, the general signalmatching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term. The minimization reduces to a dynamic system governed by a set of coupled, firstorder differential equations. The dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale. Among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization, of variational surface reconstruction, and of coarsetofine matching. Our solution captures these in a precise, succinct, and unified form. Results are presented for onedimensional signals, a motion sequence, and a stereo pair. 1
Using Generative Models for Handwritten Digit Recognition
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1996
"... We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable Bsplines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maxi ..."
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Cited by 73 (8 self)
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We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable Bsplines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of prenormalization of input images, but can ...
Shapes, Shocks, and Deformations I: The Components of TwoDimensional Shape and the ReactionDiffusion Space
 International Journal of Computer Vision
, 1994
"... We undertake to develop a general theory of twodimensional shape by elucidating several principles which any such theory should meet. The principles are organized around two basic intuitions: first, if a boundary were changed only slightly, then, in general, its shape would change only slightly. Th ..."
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Cited by 67 (5 self)
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We undertake to develop a general theory of twodimensional shape by elucidating several principles which any such theory should meet. The principles are organized around two basic intuitions: first, if a boundary were changed only slightly, then, in general, its shape would change only slightly. This leads us to propose an operational theory of shape based on incremental contour deformations. The second intuition is that not all contours are shapes, but rather only those that can enclose "physical" material. A theory of contour deformation is derived from these principles, based on abstract conservation principles and HamiltonJacobi theory. These principles are based on the work of Sethian [82, 86], the OsherSethian level set formulation [65], the classical shock theory of Lax [53, 54], as well as curve evolution theory for a curve evolving as a function of the curvature and the relation to geometric smoothing of GageHamiltonGrayson [32, 37]. The result is a characterization of th...
Image segmentation using deformable models
 Handbook of Medical Imaging. Vol.2 Medical Image Processing and Analysis
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Statistical Approaches to FeatureBased Object Recognition
, 1997
"... . This paper examines statistical approaches to modelbased object recognition. Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations. This motivates the development of new maximumlikeli ..."
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Cited by 63 (1 self)
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. This paper examines statistical approaches to modelbased object recognition. Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations. This motivates the development of new maximumlikelihood and MAP recognition formulations which are based on normal feature models. These formulations lead to an expression for the posterior probability of the pose and correspondences given an image. Several avenues are explored for specifying a recognition hypothesis. In the first approach, correspondences are included as a part of the hypotheses. Search for solutions may be ordered as a combinatorial search in correspondence space, or as a search over pose space, where the same criterion can equivalently be viewed as a robust variant of chamfer matching. In the second approach, correspondences are not viewed as being a part of the hypotheses. This leads to a criterion that is a smooth funct...