Rectification of the chordal axis transform skeleton and criteria for shape decomposition

Prasad, Lakshman

doi:10.1016/j.imavis.2006.06.025

Cited by 15 publications

(18 citation statements)

References 23 publications

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“…In this section, we give a brief explanation on how the CAT segments and skeletonizes 2D shapes. We also shed some light on inadequacies of the CAT in its current form [29] and propose various amendments to qualify it for shape description and matching.…”

Section: Skeletonisation and Segmentationmentioning

confidence: 99%

“…the main problem with this version of the CAT is the exclusive third degree topology of junction points located at the centroids of junction triangles. The rectified CAT [29] approaches a solution to this problem by using polygonal elements instead of triangular elements. These polygons are defined by a subset of the chords of the CDT, denoted by the chords strength profile (CSP), and are classified into three types (see Fig 2).…”

Section: The Cat In Its Present Formmentioning

confidence: 99%

“…The region is then triangulated using constrained Delaunay triangulation (CDT). The rectified chordal axis transform [29] (CAT) which is a descendant of the medial axis transform, and a set of pruning and merging operations provide a skeleton with an association between skeletal segments and subregions. Using this skeleton, we segment the shape and embed the segmentation in a hierarchy where the leaves are the protruding parts corresponding to the terminal segments defined by the CAT.…”

Section: Introductionmentioning

confidence: 99%

“…• We bring the CAT into shape description and strengthen some of its weaknesses that are revealed by dissimilarities between skeletons of objects of the same class. It is worth mentioning that Prasad [29] does not provide a matching technique adapted to the CAT representation method.…”

Section: Introductionmentioning

confidence: 99%

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Shape matching by part alignment using extended chordal axis transform

Yasseen

Verroust-Blondet

Nasri

2016

Pattern Recognition

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One of the main challenges in shape matching is overcoming intra-class variation where objects that are conceptually similar have significant geometric dissimilarity. The key to a solution around this problem is incorporating the structure of the object in the shape descriptor which can be described by a connectivity graph customarily extracted from its skeleton. In a slightly different perspective, the structure may also be viewed as the arrangement of protruding parts along its boundary. This arrangement does not only convey the protruding part's ordering along the anti clockwise direction, but also relates these parts on different levels of detail. In this paper, we propose a shape matching method that estimates the distance between two objects by conducting a part-to-part matching analysis between their visual protruding parts. We start by a skeleton-based segmentation of the shape inspired by the Chordal Axis Transform. Then, we extract the segments that represent the protruding parts in its silhouette on varied levels of detail. Each one of these parts is described by a feature vector. A shape is thus described by the feature vectors of its parts in addition to their angular and linear proximities to each other. Using dynamic programming, our algorithm finds a minimal cost correspondence between parts. Our experimental evaluations validate the proposition that part correspondence allows conceptual matching of precisely dissimilar shapes.

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Section: Skeletonisation and Segmentationmentioning

confidence: 99%

Section: The Cat In Its Present Formmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Shape matching by part alignment using extended chordal axis transform

Yasseen

Verroust-Blondet

Nasri

2016

Pattern Recognition

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“…To improve the decomposition results and avoid over-segmentation problem, distance transform and other more complex transform are applied. For example, Svensson and Baja use distance transform to define object thickness (Svensson and Baja, 2002); Prasad makes use of constrained Delaunay triangulations to define chordal axis transform (CAT) (Prasad, 2007); Kim et al takes advantage of mathematical morphology and convex rule to partition the 3D shape, namely constrained morphological decomposition (CMD) (Kim et al, 2005). Other 3D shape decomposition algorithms use geodesic distance to avoid over-segmentation.…”

Section: Related Workmentioning

confidence: 99%

3D shape recursive decomposition by Poisson equation

Pan

Chen

Zhi

2009

Pattern Recognition Letters

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View selection for sketch-based 3D model retrieval using visual part shape description

2016

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Hand drawings are the imprints of shapes in human's mind. How a human expresses a shape is a consequence of how he or she visualizes it. A query-by-sketch 3D object retrieval application is closely tied to this concept from two aspects. First, describing sketches must involve elements in a figure that matter most to a human. Second, the representative 2D projection of the target 3D objects should be limited to "the canonical views" from a human cognition perspective. We advocate for these two rules by presenting a new approach for sketch-based 3D object retrieval that describes a 2D shape by the visual protruding parts of its silhouette. Furthermore, we present a list of candidate 2D projections that represent the canonical views of a 3D object. The general rule is that humans would visually avoid part occlusion and symmetry. We quantify the extent of part occlusion of the projected silhouettes of 3D objects by skeletal length computations. Sorting the projected views in the decreasing order of skeletal lengths gives access to a subset of best representative views. We experimentally show how views that cause misinterpretation and mismatching can be detected according to the part occlusion criteria. We also propose criteria for locating side, off axis, or asymmetric views.

show abstract

Rectification of the chordal axis transform skeleton and criteria for shape decomposition

Cited by 15 publications

References 23 publications

Shape matching by part alignment using extended chordal axis transform

Shape matching by part alignment using extended chordal axis transform

3D shape recursive decomposition by Poisson equation

View selection for sketch-based 3D model retrieval using visual part shape description

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