O(log n) bimodality analysis

Phillips, Tsai-Yun; Rosenfeld, Azriel; Sher, Awal

doi:10.1016/0031-3203(89)90010-1

Cited by 20 publications

(2 citation statements)

References 2 publications

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“…Widely used in statistics, Fisher criterion has also been used for various purposes in the field of image processing and computer vision: filter design [41], texture classification [21], [42]- [45], and feature space dimensionality reduction [46]. Only recently, this criterion has been applied to the evaluation of texture feature extraction operators [13].…”

Section: A Comparison Methodsmentioning

confidence: 99%

Comparison of texture features based on Gabor filters

Grigorescu

Petkov

Kruizinga³

2002

IEEE Trans. on Image Process.

628

267

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Section: A Comparison Methodsmentioning

confidence: 99%

Comparison of texture features based on Gabor filters

Grigorescu

Petkov

Kruizinga³

2002

IEEE Trans. on Image Process.

628

267

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“…Bimodality analysis is used to estimate the threshold value automatically: if P is the histogram of the image, an optimum grey-level threshold separating it into two classes, P1 and P2, is automatically determined by maximizing the Fisher distance between P1 and P2 [9]. The bilevel image obtained by thresholding is considered a binary image in which "white" pixels are assigned the value of "0" and constitute the background, while "black' pixels have the 1051-4651/94 $04.00 0 1994 IEEE 533 value of "1" and form the drawing.…”

Section: Preprocessingmentioning

confidence: 99%

Graphic symbol recognition using a signature technique

Ventura¹,

Schettini²

Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5)

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The authors present a noise-robust and general-purpose method for the recognition of graphic symbols in linedrawing images. The method assumes that a noise-jree symbol data base is available, and that both the model symbols and the processed image have been broken down into elementary structures. Recognition is based on the hypothesis-and-test paradigm. The detection of an elementary structure allows us to use a signature filter, to hypothesize and then recursively reduce a set of matching symbols. Hypothesized symbols are then verified by exploiting distance transform and distance measurement.

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