Pattern Recognition is a process of identifying a stimulus and is among the best-developed of human perceptual and cognitive skills. It is a process whereby sensory input is recognized as a meaningful entity. It seeks similarities and regularities recognizing a correspondence between a stimulus and information in memory. Processing sensory information and the comparison to a representation stored in semantic memory allows the perceiver to recognize the stimuli.
Theories of Pattern Recognition are derived from the general theories of perception. The field of Pattern Recognition addresses pattern in all forms and is concerned with the classification or description of observation. The research is connected to advanced programming techniques of machine representations, relating information of aspects of the outside world to machine behavior. Pattern Recognition by computers is not just concerned with the identification of visual or audio patterns (machine vision and voice recognition) but also includes statistical data or data such as the patterns of interaction and communication of individuals and groups based on their increasingly recorded electronic footprints.
A collection of emerging information technologies is grouped around cognitive computing and the study of how machines can observe the environments, learn to distinguish patterns of interest and make reasonable decisions about the categories of the patterns. Use of this technology is widespread not only in robotics, in medical diagnosis and EKG signal analysis and character, voice, handwriting recognition and biometrics but also financial forecast copyright surveillance, psychological profiling, automated target recognition and a steady increase of new applications. Pattern Recognition has been embraced as a key technology for future generations of user interfaces incorporating augmented reality features in human computer interface design.
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