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Items: 1 to 20 of 71

1.

Unified synthetic discriminant function computational formulation.

Casasent D.

Appl Opt. 1984 May 15;23(10):1620. No abstract available.

PMID:
18212877
2.

Modified filter synthetic discriminant functions for improved optical correlator performance.

Wang RK, Chatwin CR, Huang MY.

Appl Opt. 1994 Nov 10;33(32):7646-54. doi: 10.1364/AO.33.007646.

PMID:
20962973
3.

Composite training images for synthetic discriminant functions.

Brasher JD, Woodson M.

Appl Opt. 1996 Jan 10;35(2):314-7. doi: 10.1364/AO.35.000314.

PMID:
21069014
4.

Unconstrained correlation filters.

Mahalanobis A, Vijaya Kumar BV, Song S, Sims SR, Epperson JF.

Appl Opt. 1994 Jun 10;33(17):3751-9. doi: 10.1364/AO.33.003751.

PMID:
20885767
6.

Optimally regularised kernel Fisher discriminant classification.

Saadi K, Talbot NL, Cawley GC.

Neural Netw. 2007 Sep;20(7):832-41. Epub 2007 Jun 2.

PMID:
17600674
7.

Acoustic absorption calculation in irreducible porous media: a unified computational approach.

Lee CY, Leamy MJ, Nadler JH.

J Acoust Soc Am. 2009 Oct;126(4):1862-70. doi: 10.1121/1.3205399.

PMID:
19813800
8.

Soar and the case for unified theories of cognition.

Cooper R, Shallice T.

Cognition. 1995 May;55(2):115-49. Review.

PMID:
7789089
9.

On the computational approach to immobilized pH gradients.

Celentano FC, Gianazza E, Righetti PG.

Electrophoresis. 1991 Oct;12(10):693-703.

PMID:
1802687
10.

Image clustering using local discriminant models and global integration.

Yang Y, Xu D, Nie F, Yan S, Zhuang Y.

IEEE Trans Image Process. 2010 Oct;19(10):2761-73. doi: 10.1109/TIP.2010.2049235. Epub 2010 Apr 26.

PMID:
20423802
11.

Theoretical framework for the design of purely real synthetic-discriminant-function-type correlation filters.

Mahalanobis A, Song S.

Appl Opt. 1992 Dec 10;31(35):7450-6. doi: 10.1364/AO.31.007450.

PMID:
20802621
12.

Optical pattern recognition using a synthetic discriminant amplitude-compensated matched filter.

Wang ZQ, Gillespie WA, Cartwright CM, Soutar C.

Appl Opt. 1993 Jan 10;32(2):184-9. doi: 10.1364/AO.32.000184.

PMID:
20802676
13.

Generalized discriminant analysis using a kernel approach.

Baudat G, Anouar F.

Neural Comput. 2000 Oct;12(10):2385-404.

PMID:
11032039
14.

Optimal trade-off synthetic discriminant function filters for arbitrary devices.

Kumar BV, Carlson DW, Mahalanobis A.

Opt Lett. 1994 Oct 1;19(19):1556-8.

PMID:
19855582
15.

Discriminant subspace analysis: a Fukunaga-Koontz approach.

Zhang S, Sim T.

IEEE Trans Pattern Anal Mach Intell. 2007 Oct;29(10):1732-45.

16.

Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis.

Van Gestel T, Suykens JA, Lanckriet G, Lambrechts A, De Moor B, Vandewalle J.

Neural Comput. 2002 May;14(5):1115-47.

PMID:
11972910
17.

Incoherent optical associative memory by using synthetic discriminant function filters.

Taniguchi M, Matsuoka K, Ichioka Y.

Appl Opt. 1992 Jun 10;31(17):3295-301. doi: 10.1364/AO.31.003295.

PMID:
20725282
18.

Projection-slice synthetic discriminant functions for optical pattern recognition.

Riasati VR, Abushagur MA.

Appl Opt. 1997 May 10;36(14):3022-34.

PMID:
18253307
19.

Phase selection of synthetic discriminant function filters.

Refregier P, Huignard JP.

Appl Opt. 1990 Nov 10;29(32):4772-8. doi: 10.1364/AO.29.004772.

PMID:
20577465
20.

Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition.

Gao D, Han S, Vasconcelos N.

IEEE Trans Pattern Anal Mach Intell. 2009 Jun;31(6):989-1005. doi: 10.1109/TPAMI.2009.27.

PMID:
19372605

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