Intelligence Semantics

Image Processing & Communications Challenges 6 by Ryszard S. Choraś

By Ryszard S. Choraś

This ebook collects a sequence of study papers within the zone of picture Processing and Communications which not just introduce a precis of present expertise but additionally provide an outlook of capability characteristic difficulties during this quarter. the foremost target of the booklet is to supply a suite of accomplished references on a few contemporary theoretical improvement in addition to novel functions in snapshot processing and communications. The e-book is split into elements and offers the court cases of the sixth foreign photo Processing and Communications convention (IP&C 2014) held in Bydgoszcz, 10-12 September 2014. half I bargains with picture processing. A finished survey of alternative equipment of snapshot processing, computing device imaginative and prescient is usually provided. half II bargains with the telecommunications networks and desktop networks. purposes in those components are thought of.

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Fin (x, y) is the color level input for data the pixel (x, y). max - is the maximum value for color level in the input image. min - is the minimum value for color level in the input image, γ - constant that defines the shape of the stretching curve. S. Choraś (a) (b) Fig. 4. Finger vein images: (a) oryginal image, (b) image after gray scale normalization To obtain the vein binary image several alternatives method can be use from morphological to multi-resolution analysis methods. We use the typical adaptive threshold algorithm.

2 The Idea of Random Walking for Image Segmentation The Random Walker segmentation method [2] works on an image seen as a weighted undirected graph G = (V, E) with vertices v ∈ V corresponding to image pixels and edges e ∈ E ⊆ V × V spanning the pairs of vertices. Each edge eij = (vi , vj ) is assigned a non-negative weight wij related to the difference of grey level values in CT images. Six edges from any pixel pi to its nearest neighbours are considered. The set of graph vertices is then divided into the pre-labelled nodes VL and the unlabelled nodes VU , where VM ∩ VU = ∅ and VM ∪ VU = V .

Such methods can be classified into global shape descriptors , shape signatures and spectral descriptors. Although global descriptors such as area, circularity, eccentricity, axis orientation are simple to compute and also robust in representation, they can only discriminate shapes with large dissimilarities, therefore usually suitable for filtering purpose. Most shape signatures such as complex coordinates, curvature and angular representations are essentially local representations of shape features, they are sensitive to noise and not robust.

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