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23
A Survey of Medical Image Registration
, 1998
"... The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of t ..."
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Cited by 405 (3 self)
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The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. Keywords: registration, matching Received May 25, 1997
Splines: A Perfect Fit for Signal/Image Processing
 IEEE SIGNAL PROCESSING MAGAZINE
, 1999
"... ..."
Unsupervised Contour Representation and Estimation Using BSplines and a Minimum Description Length Criterion
 IEEE Trans. on Image Processing
, 2000
"... This paper describes a new approach to adaptive estimation of parametric deformable contours based on Bspline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region based image model. The parameters of the image model, the con ..."
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Cited by 32 (3 self)
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This paper describes a new approach to adaptive estimation of parametric deformable contours based on Bspline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region based image model. The parameters of the image model, the contour parameters, and the Bspline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion. The result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequacy and good performance of the approach.
Recognizing MultiStroke Symbols
 IN 2002 AAAI SPRING SYMPOSIUM  SKETCH UNDERSTANDING, (PALO ALTO CA, 2002
, 2002
"... We describe a trainable recognizer for multistroke symbols. The learned definitions are described in terms of the constituent geometric primitives (lines and arcs), the properties of individual primitives, and the geometric relationships between them. A definition is learned by examining a few ..."
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Cited by 30 (3 self)
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We describe a trainable recognizer for multistroke symbols. The learned definitions are described in terms of the constituent geometric primitives (lines and arcs), the properties of individual primitives, and the geometric relationships between them. A definition is learned by examining a few examples of a symbol and identifying which properties and relationships occur frequently. During both
The Complex Representation of Algebraic Curves and its Simple Exploitation for Pose Estimation and Invariant Recognition
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2000
"... New representations are introduced for handling 2D algebraic curves (implicit polynomial curves) of arbitrary degree in the scope of computer vision applications. These representations permit fast accurate poseindependent shape recognition under Euclidean transformations with a complete set of inva ..."
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Cited by 12 (0 self)
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New representations are introduced for handling 2D algebraic curves (implicit polynomial curves) of arbitrary degree in the scope of computer vision applications. These representations permit fast accurate poseindependent shape recognition under Euclidean transformations with a complete set of invariants, and fast accurate poseestimation based on all the polynomial coefficients. The latter is accomplished by a new centering of a polynomial based on its coefficients, followed by rotation estimation by decomposing polynomial coefficient space into a union of orthogonal subspaces for which rotations within two dimensional subspaces or identity transformations within one dimensional subspaces result from rotations in x,y measureddata space. Angles of these rotations in the two dimensional coefficient subspaces are proportional to each other and are integer multiples of the rotation angle in the x,y data space. By recasting this approach in terms of a complex variable, i.e, x+iy=z and complex polynomialcoefficients, further conceptual and computational simplification results. Application to shapebased indexing into databases is presented to illustrate the usefulness and the robustness of the complex representation of algebraic curves.
Image registration and object recognition using affine invariants and convex hulls
 IEEE Transactions on Image Processing
, 1999
"... Abstract — This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations, and under possible occlusion and/or appearance of new objects. It presents a set of local ..."
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Cited by 9 (0 self)
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Abstract — This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations, and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering v feature points on the test image with x feature points of the reference image is of order y@x 2 vA vA. vA The method has been tested on real indoor and outdoor images and performs well. Index Terms—Affine invariants, affine transformations, alignment, convex hull, occlusion, perspective, registration, weak. I.
On Deformable Models for Visual Pattern Recognition
, 2002
"... This paper reviews modelbased methods fornonrig# shape recogLj#If8 These methods model, match andclassif nonrigg shapes, which aregefIxq#x problematic for conventationalalgentati using rigg models. ..."
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Cited by 8 (2 self)
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This paper reviews modelbased methods fornonrig# shape recogLj#If8 These methods model, match andclassif nonrigg shapes, which aregefIxq#x problematic for conventationalalgentati using rigg models.
Sampling of Periodic Signals: A Quantitative Error Analysis
"... Abstract—We present an exact expression for the 2 error that occurs when one approximates a periodic signal in a basis of shifted and scaled versions of a generating function. This formulation is applicable to a wide variety of linear approximation schemes including wavelets, splines, and bandlimite ..."
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Cited by 4 (2 self)
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Abstract—We present an exact expression for the 2 error that occurs when one approximates a periodic signal in a basis of shifted and scaled versions of a generating function. This formulation is applicable to a wide variety of linear approximation schemes including wavelets, splines, and bandlimited signal expansions. The formula takes the simple form of a Parseval’slike relation, where the Fourier coefficients of the signal are weighted against a frequency kernel that characterizes the approximation operator. We use this expression to analyze the behavior of the error as the sampling step approaches zero. We also experimentally verify the expression of the error in the context of the interpolation of closed curves. Index Terms—Asymptotic performance, curves, error bounds, periodic representations, sampling. I.
BSpline Curve Representation of Segmented Object in MPEG Compressed Domain
"... Conventional shape representations are usually obtained from the segmented object in pixel domain, which is computationally expensive. In this paper, we present Bspline shape representation of object segmented in compressed domain. We also show that this shape after smoothing and interpolation usin ..."
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Cited by 3 (0 self)
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Conventional shape representations are usually obtained from the segmented object in pixel domain, which is computationally expensive. In this paper, we present Bspline shape representation of object segmented in compressed domain. We also show that this shape after smoothing and interpolation using Bspline curve estimation contains enough information and can be used for shape indexing, classification, and recognition. Keyword: Shape matching, Bspline, Compressed domain shape features. I. INTRODUCTION Shape is one of important features for describing an object of interest. Even though it is easy to understand the concept of 2D shape, it is very difficult to represent, define and describe it. There are many different methods proposed in the literature such as the Fourier Descriptors [12], the moments [3], the Bspline shape representation [47], the autoregressive models [2, 8], the Hough Transform [9], the Fractal geometry methods [10], and the Wavelet Transform ZeroCrossing Rep...
Spline curve matching with sparse knot sets: applications to deformable shape detection and recognition
 29th Annual Conference of the IEEE Industrial Electronics Society
"... This paper presents a new curve matching method for deformable shapes using twodimensional splines. In contrast to the residual error criterion [7], which is based on relative locations of corresponding knot points such that is reliable primarily for dense point sets, we use deformation energy of t ..."
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Cited by 3 (0 self)
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This paper presents a new curve matching method for deformable shapes using twodimensional splines. In contrast to the residual error criterion [7], which is based on relative locations of corresponding knot points such that is reliable primarily for dense point sets, we use deformation energy of thinplatespline mapping between sparse knot points and normalized local curvature information. This method has been tested successfully for the detection and database retrieval of deformable shapes. 1.