By Roumen Kountchev, Kazumi Nakamatsu
ISBN-10: 3319321900
ISBN-13: 9783319321905
ISBN-10: 3319321927
ISBN-13: 9783319321929
This e-book offers an advent and eleven self sustaining chapters, that are dedicated to quite a few new techniques of clever photograph processing and research. The e-book additionally provides new tools, algorithms and utilized platforms for clever snapshot processing, at the following simple topics:
- Methods for Hierarchical photograph Decomposition;
- Intelligent electronic sign Processing and have Extraction;
- Data Clustering and Visualization through Echo country Networks;
- Clustering of ordinary photos in automated photograph Annotation Systems;
- Control process for distant Sensing photograph Processing;
- Tissue Segmentation of MR mind pictures Sequence;
- Kidney Cysts Segmentation in CT Images;
- Audio visible consciousness versions in cellular Robots Navigation;
- Local Adaptive photo Processing;
- Learning concepts for clever entry Control;
- Resolution development in Acoustic Maps.
Each bankruptcy is self-contained with its personal references. a few of the chapters are dedicated to the theoretical elements whereas the others are providing the sensible points and the research of the modeling of the constructed algorithms in several software areas.
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Additional resources for New Approaches in Intelligent Image Analysis: Techniques, Methodologies and Applications
Sample text
A decomposition of eight components). In case that after the division the blocks at the image borders are incomplete, they should be extended through extrapolation. Such approach is suitable in case, that the number of the decomposition components, which is limited up to 8, is sufficient for the application. If more components are needed, their number could be increased, by dividing the image into blocks of size 16 × 16, or larger; 6. The HSVD algorithm opens new possibilities for fast image processing in various application areas, as: compression, filtration, segmentation, merging, watermarking, extraction of minimum number of features for pattern recognition, etc.
There is also a possibility for further development of the HAPCA algorithms, through: the use of Integer PCA for lossless coding of MS images; HAPCA with a matrix of size N × N (N—a digit, divisible by 2 or 3), but without using numerical methods, etc. 6 R. Kountchev and R. Kountcheva Hierarchical Adaptive Kernel Principal Component Analysis for Color Image Segmentation The color image segmentation is of high significance in computer vision as the first stage of the processing, concerning the detection and extraction of objects with predefined color, the shape of the visible part of the surface, and the texture.
Kountcheva Fig. 6 Binary tree, representing the HSVD algorithm for the image matrix [X], of size 4 × 4 pffiffiffiffiffiffiffi Each tree branch has a corresponding eigen value ks;k , or resp. rs;k ¼ ks;k for pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ks;k ðmÞ—for the level 2 (m = 1, 2). the level 1, and ks;k ðmÞ or resp. rs;k ðmÞ ¼ The total number of the tree branches shown on Fig. 6, is equal to six. , when they are equal to 0, or are smaller than a small threshold Ds;k ; resp. Ds;k ðmÞ: To cut down one HSVD component [Ci] in one level, it is necessary all values of σi, which participate in this component, to be equal to zero, or very close to it.
New Approaches in Intelligent Image Analysis: Techniques, Methodologies and Applications by Roumen Kountchev, Kazumi Nakamatsu
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