IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric invariance in computer vision
Geometric invariance in computer vision
VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
On Interactive Object Shape Modeling Using Algebraic Curves
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
The 3L Algorithm for Fitting Implicit Polynomial Curves and Surfaces to Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Computational Logic (TOCL)
Locating and Recognizing Text in WWW Images
Information Retrieval
Covariant-Conics Decomposition of Quartics for 2D Shape Recognition and Alignment
Journal of Mathematical Imaging and Vision
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An effective approach has appeared in the literature for recognizing a 2D curve or 3D surface objects of modest complexity based on representing an object by a single implicit polynomial of 3/sup rd/ or 4/sup th/ degree, computing a vector of Euclidean or affine invariants which are functions of the polynomial coefficients, followed by Bayesian object recognition of the invariants, thus producing a low computational cost robust recognition. This paper extends the approach, as well as an initial work on mutual invariants recognizers, to the recognition of objects too complicated to be represented by a single polynomial. Hence, an object to be recognized is partitioned into patches, each patch is represented by a single implicit polynomial, mutual invariants are computed for pairs of polynomials for pairs of patches, and the object recognition is via a Bayesian recognition of vectors of self and mutual invariants. We discuss why the complete object geometry can be captured by the geometry of pairs of patches, how to design mutual invariants, and how to match patches in the data with those in the database at a low computational cost. The approach is a low computational cost recognition of partially occluded articulated objects in an arbitrary position and in noise by recognizing the self or joint geometry of one or more patches.