Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2006-08-29
2006-08-29
Ahmed, Samir (Department: 2623)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C706S045000
Reexamination Certificate
active
07099504
ABSTRACT:
Systems and methods for object or pattern detection that use a nonlinear support vector (SV) machine are described. In the illustrated and described embodiment, objects or patterns comprising faces are detected. The decision surface is approximated in terms of a reduced set of expansion vectors. In order to determine the presence of a face, the kernelized inner product of the expansion vectors with the input pattern are sequentially evaluated and summed, such that if at any point the pattern can be rejected as not comprising a face, no more expansion vectors are used. The sequential application of the expansion vectors produces a substantial saving in computational time.
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Burges, “Simplified Support Vector Decision Rules, ”Bell Laboratories, Lucent Technologies, 7 pages.
Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Microsoft Research Technical Report, Apr. 21, 1998, 21 pages.
Rowley et al., “Neural Network-Based Face Detection,” IEEE 1996, pp. 203-208.
Rowley et al., “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis Machine Intelligene, vol. 20, No. 1, Jan. 1998, pp. 23-38.
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Blake Andrew
Romdhani Sami
Schoelkopf Bernhard
Torr Philip H. S.
Ahmed Samir
Microsoft Corporation
Microsoft Corporation
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