Distortion-invariant kernel filters for general pattern recognition

Patnaik, Rohit; Casasent, David P.

doi:10.1117/12.776417

Cited by 2 publications

(12 citation statements)

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“…We use this kernel technique to form higher-order "kernel DIFs." We have shown [3], [5] that fast FFT on-line implementation of polynomial and Gaussian kernel filters are possible (since they contain x T y VIPs) and that an efficient implementation is possible only for kernel SDF filters.…”

Section: Kernel Technique and Formulation Of Kernel Sdf Filtersmentioning

confidence: 98%

“…We present new test results with more and different confuser-class objects and for both polynomial and Gaussian kernel SDF filters; all prior kernel DIF (except ours [3], [5] ) work did not address rejection of unseen confuserclass objects and shifts of the test input. We also include and emphasize new test results at farther ranges than before.…”

Section: Introductionmentioning

confidence: 98%

“…We include new insight into the difference between vector-based and pixel-based kernels. We also present more test results with our recently introduced [5] "preprocessed kernel SDF filters"; these are a combination of minimum noise and correlation energy (MINACE) [6] filter preprocessing and kernel SDF filters. In our new work, we consider whether automated selection of the Minace-preprocessing parameter improves test set performance.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present new test results with these kernel DIFs. We emphasize kernel versions of the synthetic discriminant function (SDF) [2] filter, since we showed [5] that they are the most efficient to use. We include new insight into the difference between vector-based and pixel-based kernels.…”

Section: Introductionmentioning

confidence: 99%

“…The database and training and test procedures are described in Sec. 5. Test results for different filters and test cases are presented in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Kernel synthetic discriminant function (SDF) filters for fast object recognition

Patnaik

Casasent

2009

SPIE Proceedings

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In most object recognition applications, the object is present with different distortions (e.g. aspect view and scale) and its location is unknown. Our objective is to develop higher-order classifiers that can be applied (efficiently and fast) for different locations of the object over the test input. A type of classifier, the distortion-invariant filter (DIF), is attractive for fast object recognition, since it can be applied for different shifts using the fast Fourier transform (FFT); a single DIF handles different object distortions, e.g. all aspect views and some range of scale. In our prior work (Proc. SPIE 7252-02), we combined DIFs and the kernel technique to form higher-order "kernel DIFs." In this paper, we present new test results with these kernel DIFs; we emphasize kernel versions of the synthetic discriminant function (SDF) filter, since we recall that they are the most efficient to use. We include new insight into the difference between vector-based and pixel-based kernels. We also present more test results with our recently introduced (Proc. SPIE 6977-03) combination of minimum noise and correlation energy (MINACE) filter preprocessing and kernel SDF filters (these form "preprocessed kernel SDF filters"); in our new work, we consider whether automated selection of the Minace-preprocessing parameter improves filter performance. We consider the classification of different pairs of true-class CAD (computer-aided design) infrared (IR) objects and the rejection of unseen problematic (blob) real IR clutter and unseen confuser-class CAD IR objects with full 360° aspect-view distortions and with different ranges of scale distortions present. We present new test results with more and different confuser-class objects and for both polynomial and Gaussian kernel SDF filters. We also include new test results at farther ranges than before; these are emphasized in this paper.

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Section: Kernel Technique and Formulation Of Kernel Sdf Filtersmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%