Squigraphs for fine and compact modeling of 3-D shapes
IEEE Transactions on Image Processing
Improving shape retrieval by spectral matching and meta similarity
IEEE Transactions on Image Processing
Anthropometric 3D Face Recognition
International Journal of Computer Vision
Retrieving articulated 3D objects using normalized distance function
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Geodesic shape retrieval via optimal mass transport
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Geodesic Methods in Computer Vision and Graphics
Foundations and Trends® in Computer Graphics and Vision
Matching 2D and 3D articulated shapes using the eccentricity transform
Computer Vision and Image Understanding
Spaces and manifolds of shapes in computer vision: An overview
Image and Vision Computing
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Recognition of images and shapes has long been the central theme of computer vision. Its importance is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we propose a new approach for object matching based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable for linear spaces, the geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. The matching task therefore becomes a one-dimensional comparison problem between probability distributions which is clearly much simpler than comparing three-dimensional structures. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions, and is additionally computationally efficient and inexpensive