A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
Convexity rule for shape decomposition based on discrete contour evolution
Computer Vision and Image Understanding
Shock Graphs and Shape Matching
International Journal of Computer Vision
International Journal of Computer Vision
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Decomposition and Axial Shape Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Multiscale Medial Loci and Their Properties
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Retrieval by shape similarity with perceptual distance andeffective indexing
IEEE Transactions on Multimedia
Finding shape axes using magnetic fields
IEEE Transactions on Image Processing
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We present a novel approach to shape representation that describes a shape using a set of histograms derived at salient points within the shape. A computationally efficient multiresolution pyramidal framework is used to generate a dense gradient vector field whose characteristics can be altered through the use of a scale parameter α. This parameter regulates the proportion of low and high spatial frequency components used in creating the vector field and can be set such that minor boundary distortions do not significantly change the representation of the shape. Local maximas of the directional disparity measure in the vector field are used for locating shape axes, from where polar sampling of the vector field is then used to build scale and rotational invariant histograms that describes subparts of the shape. A saliency measure based on the size of a part is introduced to provide appropriate weighting to each part during the shape matching process. Experimental results involving silhouettes images are presented to demonstrate the effectiveness of the proposed gradient vector field histograms for similarity-based shape retrieval.