Pattern recognition and image processing are rapidly growing technologies within engineering and computer science. Algorithms for pattern recognition aim to observe the environment, learn to distinguish patterns of interest from their background and make pattern classes. A series of methods are developed to group individual patterns into specific classes according to their common properties, resulting in different pattern classes. Because of emerging applications which are computationally demanding, a wide specter of ...
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Pattern recognition and image processing are rapidly growing technologies within engineering and computer science. Algorithms for pattern recognition aim to observe the environment, learn to distinguish patterns of interest from their background and make pattern classes. A series of methods are developed to group individual patterns into specific classes according to their common properties, resulting in different pattern classes. Because of emerging applications which are computationally demanding, a wide specter of algorithms is developed to deal with problems such as data mining, classification of multimedia data, and obtaining biometric features like face and fingerprint recognition. Contrast enhancement, labeling of connected components, segmentation and feature detection methods are created based on the existing signal processing algorithms. Clustering is a widely used concept in pattern recognition and image processing. It becomes scientific not through uniqueness but through transparent and open communication. Various desirable characteristics of clusterings and various approaches to define a context-dependent truth are listed, and it is discussed what impact these ideas can have on the comparison and the choice of clustering methods in practical applications. This subject is discussed in detail in the first section of this book. The following six chapters of the book present methods for solving problems of pattern and texture recognition. The remaining content of this book focuses on the advances of specific methods and algorithms in the field of image processing. The specific advances discussed in this book include the use of dynamic detection and rejection of liveness-recognition, a system to recognize patterns at cellular and specimen levels, specific scale-adapted features and various techniques to encode visual structure. Advances in solving TV deblurring and denoising problems and the suppression of mixed Gaussian and impulsive noise in color images are also investigated. Dynamic detection and rejection of liveness-recognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalization is investigated. Bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm are presented. Experiments on the latest face video databases and fingerprint spoofing database illustrate the efficiency of proposed techniques. A system to recognize patterns at cellular and specimen levels, in images of HEp-2 cells is developed. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Detailed descriptions and extensive experiments with various features and encoding methods are provided. Specific scale-adapted features are computed in reference to the estimated scale of an image, based on the distribution of scale normalized Laplacian responses in a scale-space representation. Intrinsic-scale-adaption is performed to compute features, independent of the intrinsic texture scale, leading to a significantly increased discriminative power for a large amount of texture classes. In a final step, the rotation- and scale-invariant features are combined in a multi-resolution representation, which improves the classification accuracy in texture classification scenarios with scaling and rotation significantly. A linear time complexity method for computing a canonical form is suggested. This method is using Euclidean distances between pairs of a small subset of vertices. This approach has comparable retrieval accuracy but lower time complexity than using global geodesic distances, allowing it to be used on higher resolution meshes, or for more meshes to be considered within a time budget. Vision is one of the most important of the senses, and humans use it extensively during navigation.
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Add this copy of Pattern Recognition and Image Processing to cart. $120.57, like new condition, Sold by Media Smart rated 4.0 out of 5 stars, ships from Hawthorne, CA, UNITED STATES, published 2016 by Arcler Education Inc.
