Fundamentals of digital image processing
Fundamentals of digital image processing
Vector quantization and signal compression
Vector quantization and signal compression
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Nonlinear approximation of random functions
SIAM Journal on Applied Mathematics
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We propose a technique that finds optimized descriptors for pattern matching applications. We formulate the pattern matching problem as the search of a pattern library for vectors defined in a query manifold. Our approach trades off the computational complexity involved in the search with matching accuracy by representing the query manifold with its complexity-dependent approximations. This is done in an optimal way so that a user with a given complexity budget accomplishes the optimal matching performance for that budget. Our work can be seen as defining a covering around the query manifold with the aid of the derived descriptors. The higher the allowed computational complexity, the tighter the covering, and the more accurate the match. Our formulation results in sparse descriptors which naturally emerge as the optimal solutions. The proposed descriptors are adaptively optimized for the particular search problem so that application-specific simplifications are taken full advantage of. Thanks to our algebraic approach, the presented formulation is general and can readily be applied to many different types of signals in addition to images and video.