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Pattern recognition systems --- Pattern recognition systems.

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Linear and affine subspaces are commonly used to describe appearance of objects under different lighting, viewpoint, articulation, and identity. A natural problem arising from their use is - given a query image portion represented as a point in some high dimensional space - find a subspace near to the query. This paper presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ tree based search or locality sensitive hashing to find a near subspace. Further speedup may be achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting etc.

Pattern recognition systems

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Optical pattern recognition

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The purpose of this standard is to establish a common language for describing functional tests of memory devices. That language definition includes the vocabulary and grammar necessary to specify the functional testing of memory devices. The language is intended to be used as a general descriptive language rather than one for programming a tester. As a result, the grammar and syntax are defined in a looser and more general way than would be required for a programming language.

Pattern recognition systems.

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Pattern recognition systems