2013 IEEE International Conference on Industrial Technology (ICIT) 2013
DOI: 10.1109/icit.2013.6505840
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Statistical dependence of pixel intensities for pattern recognition

Abstract: Abstract-In this paper, we describe an algorithm for speeding up object recognition by reducing the amount of pixels taken into account when processing images. We show that some statistically stable regions can be found on image. Taking just one pixel from each region preserves the most of information of the image. We employ linear dependency between pixel intensity values to organize neighbouring pixels in groups. Bayesian classification was chosen to prove suitability. We present the results that show comput… Show more

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Cited by 5 publications
(3 citation statements)
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“…Previous study also suggests that we may exploit only a subset of all image pixels for image classification [39]. This somewhat implies that different pixels play different roles in image classification.…”
Section: The Analysis Of the Proposed Algorithmmentioning
confidence: 97%
“…Previous study also suggests that we may exploit only a subset of all image pixels for image classification [39]. This somewhat implies that different pixels play different roles in image classification.…”
Section: The Analysis Of the Proposed Algorithmmentioning
confidence: 97%
“…According to Ref. [43], we can set different weights for different pixel values to perform image classification tasks since different pixel points play different roles in image classification. And our proposed algorithm achieves more attention to pixel values for medium intensity, which justifies our proposed image representation from the other side.…”
Section: Algorithm Analysismentioning
confidence: 99%
“…The first one relies on computing statistical values and suffer from rotation irresistance, insufficiency of number of features and sensibility of image noise. Such methods are [1], [2], [3]. In contrast to this, the spectral group of methods effectively measure image energy, generates rotataion resistant image feature vectors which cannot be influenced by image noise.…”
Section: Introductionmentioning
confidence: 99%