Advances in Intelligent Analysis of Medical Data and - download pdf or read online

By Abdel-Badeeh M. Salem (auth.), Roumen Kountchev, Barna Iantovics (eds.)

ISBN-10: 3319000284

ISBN-13: 9783319000282

ISBN-10: 3319000292

ISBN-13: 9783319000299

This quantity is end result of the the fruitful and brilliant discussions throughout the MedDecSup'2012 foreign Workshop bringing jointly a correct physique of data, and new advancements within the more and more vital box of scientific informatics. This rigorously edited e-book offers new rules aimed toward the improvement of clever processing of assorted forms of scientific info and the perfection of the modern computers for scientific selection help. The publication provides advances of the clinical details structures for clever archiving, processing, research and search-by-content with a view to enhance the standard of the clinical prone for each sufferer and of the worldwide healthcare process. The e-book combines in a synergistic method theoretical advancements with the practicability of the ways built and provides the final advancements and achievements in scientific informatics to a huge variety of readers: engineers, mathematicians, physicians, and PhD scholars.

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Experiments show that the use of the skin color as a feature of gesture in the image is the effective method for automatic localization in real-time applications. Sometimes “broken” skin regions and “holes” into skin region appear. The following morphological processing removes this disadvantage. Also one can mention another restriction: the background color must differ from the skin color; otherwise the system will detect all regions looking as skin image in the color space. Table 2. 0004, where u is a top boundary, d is a bottom boundary, gu = Ju r2 + Ku r + Lu , gd = Jd r2 + Kd r + Ld , W = (r – 0,33)2 + (g – 0,33)2 , Ju = –1,377, Ku = 1,074, Lu = 0,145, Jd = –0,776, Kd = 0,560, Ld = 0,177 Log opponent IRgBy-color space (hue, saturation) is classified as skin if: 110 ≤ hue ≤ 180 and 0 ≤ saturation ≤ 130, hue = arctan 2 (Rg By ) , saturation = Rg 2 + By 2 .

Power distribution for Set 3, level 1: a - not arranged, b - arranged a b Fig. 6. Power distibution for Set 3, level 2: a - not arranged, b - arranged Decorrelation of Sequences of Medical CT Images 51 Fig. 7. , Set 7 (R=7), and on Fig. 8 a, b - their corresponding graphic distributions. The data in the last column of Table 2 show, that in the first 3 eigen images are concentrated 95,7 % of the total mean power of all 9 images in GMI. Table 2. 0 a. 0 b. Fig. 8. a. Mean Power Distribution; b. Relative Mean Power Distribution for all sets of eigen images 52 R.

At the initial stage of computer processing there is no difference between performances of sounds of speech and gesture symbols. The great difference will be on the last stage – a stage of a result interpretation. An ideal gesture recognition system must be robust to handle and palm variations over a wide range. In common case, we have three main issues: (1) the start and the end of gesture definition in temporal domain (in sequence of continuous gestures); (2) 2D segmentation of 3D palms localization in spatial domain (overlapping images of fingers, warping transformations in 3D space); (3) feature extraction and recognition.

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Advances in Intelligent Analysis of Medical Data and Decision Support Systems by Abdel-Badeeh M. Salem (auth.), Roumen Kountchev, Barna Iantovics (eds.)

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