Optical Implementation Of The Hotelling Trace Criterion For Image Classification

Gu, Zu-Han; Lee, Sing H.

doi:10.1117/12.7973371

Cited by 27 publications

(5 citation statements)

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“…By using the Hotelling trace criterion (HTC), 27 the feature vector with a larger value of J can be presumed to have the better classification characteristics. To compare the performance of different feature vectors, the sequential forward selection 28 algorithm was used.…”

Section: Take-home Messagementioning

confidence: 99%

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

Zhang

Yang

Jin

et al. 2010

Gastrointestinal Endoscopy

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Section: Take-home Messagementioning

confidence: 99%

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

Zhang

Yang

Jin

et al. 2010

Gastrointestinal Endoscopy

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“…L1 to L4=images after low-pass filtering and down-sampling. [1] The variance of the images H1 to H4 and L4 were taken as the image features [8][14] [17].…”

Section: Non Separable Wavelet Transformmentioning

confidence: 99%

A survey of algorithms for the analysis of diffused liver disease from B-mode ultrasound images

Zhou

Wan

2009

2009 9th International Conference on Electronic Measurement &Amp; Instruments

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Ultrasound Imaging has been widely used in diffused liver disease detection and diagnosis for not only it is non-invasive but very convenient as well. However, as the examination of ultrasound images by physicians is often subjective, thus an objective and fast diagnostic methods are urgently needed. Recent years Researches have conducted intensive studies in this realm and have propelled the development of liver diseases analysis by ultrasound images. This paper gives out a general survey on the development of algorithms for analysis of liver diseases by B-mode ultrasound images. First, the background of liver disease, history of liver disease detection and diagnosis with B-mode ultrasound are introduced. Then, the main part of paper is concentrated on ultrasound liver image feature extraction and classification algorithms. Finally, the conclusion is drawn.

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“…Among the most notable statistical classification algorithms are those proposed by Fukunaga and Koontz (1970), Foley and Sammon (1975), and the Hotelling trace algorithm (Fukunaga, 1972), all of which have been used in image-classification schemes Lee, 1982a, 1982b;Gu and Lee, 1984;Casasent and Sharma, 1984;Wu and Stark, 1985;Wernick and Morris, 1986). A broader interpretation of image classification reveals that image recognition in realistic environments is always, in fact, a sorting problem.…”

Section: Distortion-invariant Filtersmentioning

confidence: 99%

Pattern Recognition Using Photon-Limited Images

Morris

1989

Optical Processing and Computing

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Optical Implementation Of The Hotelling Trace Criterion For Image Classification

Cited by 27 publications

References 0 publications

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

A survey of algorithms for the analysis of diffused liver disease from B-mode ultrasound images

Pattern Recognition Using Photon-Limited Images

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