Design and application of quadratic correlation filters for target detection

Mahalanobis, Abhijit; Muise, Robert; Stanfill, S. Robert; Nevel, Alan Van

doi:10.1109/taes.2004.1337458

Cited by 73 publications

(36 citation statements)

References 12 publications

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“…We compare the proposed algorithm with another effective target detection algorithm using Fukunaga-Koontz transform [11] in the ROC curve in Fig. 2, where (a) corresponding to the detection result of Fig.…”

Section: Resultsmentioning

confidence: 99%

Infrared Small Target Detection Using PPCA

Cao

Liu

Yang

2008

Int J Infrared Milli Waves

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Probabilistic PCA (PPCA) is an extension of PCA which reformulated PCA in a probabilistic framework. In this paper we propose a infrared small target detection algorithm using PPCA analogous to the face detection scheme using PCA, or known as "eigenface". By computing the parameters of PPCA, we map the input vector from the image onto a subspace. After reconstructing the vector, the distance between the original vector and the reconstructed one will indicate the possibility of the input being a target. Experimental results show the effectiveness of this algorithm compared with other methods.

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Section: Resultsmentioning

confidence: 99%

Infrared Small Target Detection Using PPCA

Cao

Liu

Yang

2008

Int J Infrared Milli Waves

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“…We first compare the detection performance of the Fukunaga Koontz transform ͑FKT͒ 1,5 with that of eigentargets proposed by us under the same experimental conditions. Figure 1 shows the two experimental images to be used in this letter.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The issue of detecting targets can be viewed as a two-class pattern recognition problem. 1 An image to be detected consists of a target class and a background class. We can use the pattern recognition algorithms to detect targets.…”

Section: Introductionmentioning

confidence: 99%

Point target detection of infrared images with eigentargets

Liu

Yang

et al. 2007

Opt. Eng

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Abstract.A novel detection method for infrared ͑IR͒ point targets based on eigentargets is presented. The conception of eigentargets is proposed by making reference to eigenfaces in the field of face recognition. After creating the target training images using the Gaussian intensity function, we refine our method and obtain eigentargets that are a group of eigenvectors of the training set. These eigenvectors are then used to calculate the target map, defined by a target map function, at every location in the image to be detected. The large values in the target map image indicate the presence of targets. In comparison with other commonly used detection methods, our method has better performance with better receiver operating characteristics, and larger signalto-noise ratio ͑SNR͒ and background suppression factor.

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“…Implementation of this technique has led to diverse results such as identification and ensuing tracking of targets [3,4], recognition of faces [2], and obtaining hyperspectral images [5].…”

Section: Introductionmentioning

confidence: 99%