BookDOI
Soft Computing for Image Processing
TLDR
K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. Itoh: Edge Extraction Using Fuzzed Reasoning.- S.K. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.Abstract:
S.K. Pal, A. Ghosh, M.K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. He, H. Itoh: Edge Extraction Using Fuzzy Reasoning.- S.K. Mitra, C.A. Murthy, M.K. Kundu: Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithms.- S. Mitra, R. Castellanos, S.-Y. Yang, S. Pemmaraju: Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images.- B. Uma Shankar, A. Ghosh, S.K. Pal: On Fuzzy Thresholding of Remotely Sensed Images.- W. Skarbek: Image Compression Using Pixel Neural Networks.- L He, Y. Chao, T. Nakamura, H. Itho: Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches.- N B. Karayiannis, T.C. Wang: Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization.- V.D. Gesu: Soft Computing and Image Analysis.- J.H. Han, T.Y. Kim, L.T. Koczy: Fuzzy Interpretation of Image Data.- Classification: M. Grabisch: New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding.- N.K. Kasabov, S.I. Israel, B.J. Woodford: Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition.- P.A. Stadter, N.K Bose: Neuro-Fuzzy Computing: Structure, Performance Measure and Applications.- K. D. Bollacker, J. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.- K. C. Gowda, P. Nagabhushan, H.N. Srikanta Prakash: Symbolic Data Analysis for Image Processing.- Applications: N.M. Nasrabadi, S. De, L.-C. Wang, S. Rizvi, A. Chan: The Use of Artificial Neural Networks for Automatic Target Recognition.- S. Gutta, H. Wechsler:Hybrid Systems for Facial Analysis and Processing Tasks.- V. Susheela Devi, M. Narasimha Murty: Handwritten Digit Recognition Using Soft Computing Tools.- T.L. Huntsburger, J.R. Rose, D. Girard: Neural Systems for Motion Analysis: Single Neuron and Network Approaches.- H.M. Kim, B. Kosko: Motion Estimation and Compensation with Neural Fuzzy Systems.read more
Citations
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Journal ArticleDOI
Image processing with neural networks–a review
TL;DR: The various applications of neural networks in image processing are categorised into a novel two-dimensional taxonomy for image processing algorithms and their specific conditions are discussed in detail.
Journal ArticleDOI
Data mining in soft computing framework: a survey
TL;DR: A survey of the available literature on data mining using soft computing based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model is provided.
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Web mining in soft computing framework: relevance, state of the art and future directions
TL;DR: The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art.
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Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution
TL;DR: A method to address the problem of mixed pixels and to obtain a finer spatial resolution of the land cover classification maps is proposed, which exploits the advantages of both soft classification techniques and spectral unmixing algorithms, in order to determine the fractional abundances of the classes at a sub-pixel scale.
Journal ArticleDOI
Rough Mereological Calculi of Granules: A Rough Set Approach to Computation
Lech Polkowski,Andrzej Skowron +1 more
TL;DR: This work presents applications of Rough Mereology to the important theoretical idea put forth by Lotfi Zadeh, i.e., Granularity of Knowledge, and defines granules of knowledge by means of the operator of mereological class and extends the idea of a granule over complex objects like decision rules as well as decision algorithms.