Titlebook: Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications; 5th International Sy Reneta P. Barneva,Vale

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0302-9743 tational Modeling of Objects Presented in Images, CompIMAGE 2016, held in Niagara Falls, NY, USA, in September 2016.. The 18 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 30 submissions. The papers cover the following topics: theoretical contri

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Electron Transport Proteins and Cytochromes,ns of some natural graphs on the digital spaces ., . a natural number. For the case ., i.e., for the digital plane ., the induced closure operators are shown to satisfy an analogue of the Jordan curve theorem, which allows using them as convenient background structures for studying digital images.

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Interactions of SRB with Animals and Plants,d conditioning variables. Ease of interpretation and completeness of the presented material lend themselves to the use of the worked out method in a wide range of tasks in various applications of data analysis in science and practice, engineering, economy and management, environmental and social issues, biomedicine, and related fields.

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Arthritis

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Structuring Digital Spaces by Path-Partition Induced Closure Operators on Graphsns of some natural graphs on the digital spaces ., . a natural number. For the case ., i.e., for the digital plane ., the induced closure operators are shown to satisfy an analogue of the Jordan curve theorem, which allows using them as convenient background structures for studying digital images.

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Two-Dimensional Input-Revolving Automatahe various types of column-revolving operations considered here. We compare the families of array languages accepted by the variants of these automata along with the well known families of Siromoney matrix languages. We discuss some of the closure properties of the new families of array languages and give an application in steganography.

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Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI the abnormal MRI by analyzing the energy profile of the image pixels. After tumor segmentation, the tumor features are analyzed to classify the degree of malignancy. This approach can be applied to segment both high grade and low grade tumors.