By Ryszard S. Choraś
This publication collects a chain of study papers within the zone of picture Processing and Communications which not just introduce a precis of present know-how but additionally provide an outlook of capability function difficulties during this quarter. the main aim of the ebook is to supply a set of entire references on a few fresh theoretical improvement in addition to novel functions in picture processing and communications. The e-book is split into components and provides the lawsuits of the sixth foreign photograph Processing and Communications convention (IP&C 2014) held in Bydgoszcz, 10-12 September 2014. half I offers with photo processing. A entire survey of alternative tools of picture processing, machine imaginative and prescient is additionally offered. half II offers with the telecommunications networks and laptop networks. purposes in those components are thought of.
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Computing device imaginative and prescient researchers were annoyed of their makes an attempt to immediately derive intensity info from traditional two-dimensional depth pictures. examine on "shape from texture", "shape from shading", and "shape from concentration" remains to be in a laboratory degree and had now not visible a lot use in advertisement computer imaginative and prescient platforms.
Extra info for Image Processing & Communications Challenges 6
Contour-based shape representation exploit shape boundary information. 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.
However, if clustering is not as clear and distinct, then for some items the result of retrieval would be diﬀerent. It can be expected, that the larger value of k, the larger the diﬀerence between both scenarios’ results (since less items fall into any given cluster). 34 P. Czapiewski et al. 1 0 4 6 8 10 12 14 16 Number of clusters 18 20 22 24 Fig. 4. Comparison between cluster-based and full database retrieval results depending on number of clusters Fig. 5. Examples of cluster-based similar outﬁt retrieval: reference outﬁt (ﬁrst column) and retrieval results Given the above, the following experiment was conducted.
We performed two sets of experiments which simulate classiﬁcation or retrieval tasks by means of joining above presented descriptors. In the classical approach to classiﬁcation, we ﬁnd the distance to the centers of classes, thus the experiments showed that the improvement can be achieved through ﬁnding the 42 P. Forczmański Fig. 2. Selected objects from MPEG-7 Shape Database used in the experiments distances to several closest objects according to the k-nearest-neighbors manner. Hence, classiﬁcation was performed on the basis of such rule, namely, the test image was projected into reduced feature-space and the distance to all objects in the database was calculated.