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This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from(More)
Classical feature extraction and data projection methods have been well studied in the pattern recognition and exploratory data analysis literature. We propose a number of networks and learning algorithms which provide new or alternative tools for feature extraction and data projection. These networks include a network (SAMANN) for J.W. Sammon's (1969)(More)
Numerous eeorts have been made in developing \intelligent" programs based on the Von Neumann's centralized architecture. However, these eeorts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a number of scientiic disciplines are designing artiicial neural networks (ANNs)(More)
This paper presen,ts our techniques and results on automatic analysis of t ennis video t o facilitate contentbased retrieval. Our approach is based o n th,e generat ion of a n imuge nlwdel f o r the tennis court-lines. W e derive this model b?j rrsiny the knouile&e about dirriensions arid connectivity ( fo rm) of a tennis court and typical cam era g eo ni e(More)
The exponential growth of the internet has led to a great deal of interest in developing useful and efficient tools and software to assist users in searching the Web. Document retrieval, categorization, routing and filtering can all be formulated as classification problems. However, the complexity of natural languages and the extremely high dimensionality(More)
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to(More)
Surface curvature properties have been successfully employed for surface classification and 3D object recognition. A number of methods have been proposed in the computer vision literature for the estimation of curvature; some are based on the analytic computation of derivatives from a local surface fit, and others estimate derivatives or curvature directly(More)
Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving “sparse” classifiers, where irrelevant/redundant parameters are automatically set to zero. Two well-known ways of obtaining sparse classifiers are: use a zero-mean Laplacian prior on the parameters, and the “support vector machine” (SVM).(More)
There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied(More)