Computer Recognition Systems 4 by Iwona Kostorz, Rafal Doroz (auth.), Robert Burduk, Marek

By Iwona Kostorz, Rafal Doroz (auth.), Robert Burduk, Marek Kurzyński, Michał Woźniak, Andrzej Żołnierek (eds.)

The desktop attractiveness structures are these days probably the most promising instructions in synthetic intelligence. This booklet is the main accomplished examine of this box. It encompasses a number of seventy eight conscientiously chosen articles contributed by means of specialists of development acceptance. It experiences on present examine with admire to either technique and purposes. particularly, it comprises the next sections:

  • Biometrics,
  • Features, studying and classifiers,
  • Image processing and desktop vision,
  • Knowledge acquisition in line with reasoning methods
  • Medical applications,
  • Miscellaneous applications,

This ebook is a smart reference device for scientists who care for the issues of designing computing device development reputation platforms. Its goal readers might be to boot researchers as scholars of machine technology, man made intelligence or robotics.

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Binarization is carried out for every pixel of image IBG . If a pixel is part of the background, it takes the value of 0 (white). , h. , h − 4. The final result of binarization is presented in Figure 2b. After the pre-processing, image feature extraction is conducted. First stage of the feature extraction is Hough 44 L. Smacki and K. Wrobel transform [4, 5, 6]. This method is used to detect straight lines in the lip imprint image. In the first step pixels of image IBIN belonging to lip pattern area (marked black in Figure 2b) are transferred to polar coordinate system.

Although we have moved from experimental software to the mainstream technology, in terms of speed, accuracy and practical usefulness of automatic face recognition there is still much to improve. In real life many users get irritated by the fact that even small changes of pose or illumination usually cause identification failures. pl R. Burduk et al. ): Computer Recognition Systems 4, AISC 95, pp. 23–31. com © Springer-Verlag Berlin Heidelberg 2011 24 M. Smiatacz and other operations. e. the decision making algorithms.

1). Fig. 1 a) Original fingerprint image, b) magnified area with marked dominant orientation of each pixel The values of orientation angles of fingerprint pattern have a critical impact on almost all subsequent processes in automatic fingerprint recognition systems. Orientation field has been widely used for fingerprint image enhancement [2][3], singular points detection [4][5][6] and classification [7][8][9][10]. However, there are many low quality fingerprint images caused by poor skin condition (scars, wet or dry fingers), noisy acquisition devices or bad imprint techniques.

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