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
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Shape retrieval with eigen-CSS search
Image and Vision Computing
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People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algorithm for recognizing 3D objects using a new feature space, built from curvature scale space images, as a shape representation that is scale, translation, rotation and reflection invariant. As well, the new shape representation removes the inherent ambiguity of the zero position of arc length for a scale space image. The 2-stage matching algorithm, conducted in the eigenspaces of the feature space, is analogous to the way people recognize an object: first identifying the type of object, and then determining the actual object. We test the new algorithm on a 3D database comprising 209 colour objects in 2926 different view poses, and achieve a 97% recognition rate for the object type and 95% for the object pose.