Invariance Signatures: Characterizing Contours by Their Departures from Invariance

Squire, David; Caelli, Terry

doi:10.1006/cviu.2000.0809

Cited by 19 publications

(5 citation statements)

References 30 publications

(50 reference statements)

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“…Transformation groups or group actions are tools to generate application-specific invariants [36,[83][84][85][86][87][88][89][90][91] and are central to invariant theory. [9,92] Invariants can be algebraic; [82,[93][94][95] geometrical [83,96] combinations of coplanar points or planes; [94,95,[97][98][99][100][101][102][103] differential [20,36,[104][105][106][107][108][109][110] or Integral. [21,29,111,112] Since algebraic and geometric invariants are defined for the whole shape rather than boundary, and since differential invariants are very sensitive to boundary noise, we use circular II that are relatively robust to boundary noise.…”

Section: Approaches To Match Shapesmentioning

confidence: 99%

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Shape localization, quantification and correspondence using Region Matching Algorithm

Janan

Brady

2015

JBGC

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We propose a method for local, region-based matching of planar shapes, especially as those shapes that change over time. This is a problem fundamental to medical imaging, specifically the comparison over time of mammograms. The method is based on the non-emergence and non-enhancement of maxima, as well as the causality principle of integral invariant scale space. The core idea of our Region Matching Algorithm (RMA) is to divide a shape into a number of "salient" regions and then to compare all such regions for local similarity in order to quantitatively identify new growths or partial/complete occlusions. The algorithm has several advantages over commonly used methods for shape comparison of segmented regions. First, it provides improved key-point alignment for optimal shape correspondence. Second, it identifies localized changes such as new growths as well as complete/partial occlusion in corresponding regions by dividing the segmented region into sub-regions based upon the extrema that persist over a sufficient range of scales. Third, the algorithm does not depend upon the spatial locations of mammographic features and eliminates the need for registration to identify salient changes over time. Finally, the algorithm is fast to compute and requires no human intervention. We apply the method to temporal pairs of mammograms in order to detect potentially important differences between them.

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Section: Approaches To Match Shapesmentioning

confidence: 99%

“…Medical images, especially mammograms, are at best piecewise homogeneous. Various shape signatures have been proposed; [21,30,93,112,124,125] however, none of them quantify changes in regions while matching shapes. This is what distinguishes our method.…”

Section: Approaches To Match Shapesmentioning

confidence: 99%

Shape localization, quantification and correspondence using Region Matching Algorithm

Janan

Brady

2015

JBGC

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“…Most common techniques for contour-based approaches consist of shape signatures 4 , boundary moments 5 , polygonal and curve decomposition 6 , syntactic analysis 7 , scale space analysis 8,9 , spectral transform (e.g. Fourier descriptors 10 and wavelet descriptors 11 ) , and defining shape invariants using boundary primitives 12 .…”

Section: Introductionmentioning

confidence: 99%

A comparison of local and global scale approaches in characterizing shapes

2009

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Scale is a fundamental concept in computer vision and pattern recognition, especially in the fields of shape analysis, image segmentation, and registration. It represents the level of detail of object information in scenes. Global scale methods in image processing process the scene at each of various fixed scales and combine the results, as in scale space approaches. Local scale approaches define the largest homogeneous region at each point, and treat these as fundamental units. A similar dichotomy exists for describing shapes also. To vary the level of detail depending on application, it is desirable to be able to detect dominant points on shape boundaries at different scales. In this paper, we compare global and local scale approaches to shape analysis. For global scale, the Curvature Scale Space (CSS) method is selected, which is a state of the art shape descriptor, and is used in the MPEG-7 standard. The local scale approach is based on the notion of curvature-scale (c-scale), which is a new local scale concept that brings the idea of local morphometric scale (such as ball-, tensor-, and generalized scale) developed for images to the realm of boundaries. All previous methods of extracting dominant points lack this concept of a local scale. In this paper, we present a thorough evaluation of these global and local scale methods. Our analysis indicates that locally adaptive scale has advantages over global scale in shape description, just as it has also been demonstrated in image filtering, segmentation, and registration.

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“…For example, the traditional chain coding [3] is inefficient since it has a long list of code. The graph representation [4] is too complex to be indexed. There have been good approaches [5][6][7][8][9][10][11][12][13] for image retrieval when there is only one whole object (or a fragment of it) in the visual query.…”

Section: Introductionmentioning

confidence: 99%