By Iwona Kostorz, Rafal Doroz (auth.), Robert Burduk, Marek Kurzyński, Michał Woźniak, Andrzej Żołnierek (eds.)
The machine popularity structures are these days probably the most promising instructions in man made intelligence. This e-book is the main entire examine of this box. It encompasses a selection of seventy eight conscientiously chosen articles contributed by means of specialists of development reputation. It stories on present learn with appreciate to either method and purposes. specifically, it contains the subsequent sections:
- Features, studying and classifiers,
- Image processing and computing device vision,
- Knowledge acquisition in line with reasoning methods
- Medical applications,
- Miscellaneous applications,
This booklet is a smart reference device for scientists who care for the issues of designing machine trend acceptance platforms. Its aim readers might be to boot researchers as scholars of desktop technology, synthetic intelligence or robotics.
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IEICE Int. Journal Electronics Express 1(18), 575–581 (2004) Lip Print Recognition Based on Mean Differences Similarity Measure Lukasz Smacki and Krzysztof Wrobel Abstract. This paper presents a new method for comparing and classifying lip print images. In the proposed method a lip print image is first binarized and then subjected to the Hough transform. As a result a collection of line segments approximating the lip print pattern is obtained. Each segment is described by its length, angle and midpoint coordinates.
In order to obtain the performance, the proposed method was compared with the most popular gradient-based enhancement method proposed in . The mean difference between orientation estimated by the automatic method and orientation estimated by a police expert, was measured. 5) show that the proposed orientation field enhancement method has a 12% lower error level in comparison method presented in . Fig. 5 Mean difference between orientation estimated by automatic methods and orientation estimated by police expert.
E. the coordinate system constructed specifically for a given class. In the experimental section their performance was compared to the results provided by a two class SVM algorithm. 2 Multi-axis SDF Method The original SDF method was introduced in . g. typical 2D bitmap images) with dimensions n1 × n2 . The vector d for which the function reaches the maximum value is the sought for pattern projection vector that we will denote as dopt . , Ym projected onto d. Authors of the original paper suggested to calculate only one vector, dopt , for each class with the help of rank decomposition procedure  which was able to overcome the small sample size problem.