Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of Affine-Invariant Fourier Descriptors to Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette-Based Isolated Object Recognition through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Contour Shapes Using Class Segment Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Contour-Based Object Detection in Range Images
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
3D object recognition by eigen-scale-space of contours
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Affine-similar shape retrieval: application to multiview 3-D object recognition
IEEE Transactions on Image Processing
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Shape retrieval programs are comprised of two components: shape representation and matching algorithm. Building the representation on scale space filtering and the curvature function of a closed boundary curve, curvature scale space (CSS) has been demonstrated to be a robust 2D shape representation. The adoption of the CSS image as the default in the MPEG-7 standard, using a matching algorithm utilizing maxima of the CSS image contours, makes this feature of interest perforce. In this paper, we propose a framework in two stages for a novel approach to both representing and matching the CSS feature. Our contribution consists of three steps, each of which effects a profound speedup on CSS image matching. Each step is a well-known technique in other domains, but the proposed concatenation of steps leads to a novel approach to this subject which captures shape information more efficiently and decreases distracting noise. First, using experience derived from medical imaging, we define a set of marginal-sum features summarizing the CSS image. Second, the standard algorithm using CSS maxima involves a complicated and time-consuming search, since the zero of arc length is not known in any new contour. Here, we obviate this search via a phase normalization transform in the spatial dimension of the reduced marginal-CSS feature. Remarkably, this step also makes the method rotation- and reflection-invariant. Finally, the resulting feature space is amenable to dimension reduction via subspace projection methods, with a dramatic speedup in time, and as well orders of magnitude reduction in space. The first stage of the resultant program, using a general-purpose eigenspace, has class-categorization accuracy compatible with the original contour maxima program. In a second stage, we generate specialized eigenspaces for each shape category, with little extra runtime complexity because search can still be carried out in reduced dimensionality. In a leave-one-out categorization using the MPEG-7 contour database, a classification success rate of 94.1% over 1400 objects in 70 classes is achieved with very fast matching, and 98.6% in the top-2 classes. A leave-all-in test achieves 99.8% correct categorization. The method is rotation invariant, and is simple, fast, and effective.