THE NEURAL APPROACH TO PATTERN RECOGNITION
April 2004 | BY JOHN PETER JESAN
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Artificial neural networks could surpass the capabilities of conventional computer-based pattern recognition systems. The word"recognition" plays an important role in our lives. It is a basic property of all human beings; when a person sees an object, he or she first gathers all information about the object and compares its properties and behaviors with the existing knowledge stored in the mind. If we find a proper match, we recognize it. For example, when we see a dog, first we recognize that it's an animal; when wetake closer look, we recognize that it has four legs and a tail. As we are aware, most animals have four legs and a tail. The question is how we recognize that it's a dog. We look at other properties of dogs such as face, shape of ear, mouth, nose, body structure, teeth, eyes and voice. We might have seen lots of dogs and learned what a dog will look like. After making proper analysis of all theproperties, we recognize and conclude that it is a dog. Going a step further, we can differentiate or recognize our own dog when our dog is with other dogs. This recognition concept is simple and familiar to everybody in the real world environment, but in the world of artificial intelligence, recognizing such objects is an amazing feat. The functionality of the human brain is amazing; it is notcomparable with any artificial machines or software. Let us go deeper and analyze what is recognition and how it is done through machines. Pattern Recognition The act of recognition can be divided into two broad categories: recognizing concrete items and recognizing abstract items. The recognition of concrete items involves the recognition of spatial and temporal items. Examples of spatial items arefingerprints, weather maps, pictures and physical objects. Examples of temporal items are waveforms and signatures. Recognition of abstract items involves the recognition of a solution to a problem, an old conversation or argument, etc. In other words, recognizing items that do not exist physically. In this article, I am concerned with recognition of concrete items. Applications include finger printidentification, voice recognition, face recognition, character recognition, signature recognition and classification of objects in scientific/research areas such as astronomy, engineering, statistics, medical, machine learning and neural networks. Recognition of an item involves three levels of processing: input filtering, feature extraction and classification Filtering is removing unwantedinformation or data from input. Depending on the application, the filter algorithm or method will change. For example, consider finger print identification. Each time we scan our fingerprints through a (non-ink) fingerprint device, the scanned output may be different. The difference may be due to a change in contrast or brightness or in the background of the image. There could be some distortion. Inorder to process the input, we may need only lines in the fingerprints and we may not need the other parts or background of the fingerprint. In order to filter out the unwanted portion of the image and replace it with a white background, we need a filter mechanism. Once the image is filtered through the filter mechanism, we will get standard clean finger prints only with lines, which in turn helpswith the process of feature extraction. Feature extraction is a process of studying and deriving useful information
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The neural approach to pattern recognition
Feature extraction is a process of studying and deriving useful information from the filtered input patterns. The derived information may be...