Computer Science
Pattern Recognition
Pattern recognition is the process of identifying patterns or regularities in data. In computer science, it involves developing algorithms and techniques to enable machines to recognize and interpret patterns in various forms of data, such as images, signals, and text. This field is crucial for tasks like image and speech recognition, natural language processing, and machine learning.
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eBook - PDFVision Systems
Segmentation and Pattern Recognition
- Goro Obinata, Ashish Dutta, Goro Obinata, Ashish Dutta(Authors)
- 2007(Publication Date)
- IntechOpen(Publisher)
10 An Overview of Advances of Pattern Recognition Systems in Computer Vision Kidiyo Kpalma and Joseph Ronsin IETR (Institut d'Electronique et de Télécommunications de Rennes) UMR – CNRS 6164 Groupe Image et Télédétection Institut National des Sciences Appliquées (INSA) de Rennes 1. Introduction First of all, let's give a tentative answer to the following question: what is Pattern Recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: Pattern Recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes. But what is a pattern? In 1985, Satoshi Watanabe (Watanabe, 1985) defined a pattern as the opposite of chaos; it is an entity, vaguely defined, that could be given a name. In other words, a pattern can be any entity of interest which one needs to recognise and/or identify: it is so worthy that one would like to know its name (its identity). Examples of patterns are: a pixel in an image, a 2D or 3D shape, a typewritten or handwritten character, the gait of an individual, a gesture, a fingerprint, a footprint, a human face, the voice of an individual, a speech signal, ECG time series, a building, a shape of an animal. A Pattern Recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task. The classification scheme is usually based on the availability of the training set that is a set of patterns already having been classified.
- Meisel(Author)
- 1972(Publication Date)
- Academic Press(Publisher)
However, the techniques of mathematical Pattern Recognition are simply a collection of numerical algorithms for solving very particular problems posed in very particular ways. Any success in their application depends on careful formulation by the user and an understanding of the assumptions involved in their use. Because of the rather miraculous feats of sensory Pattern Recognition per- formed by humans, there is a tendency to expect automatic results from computer-based Pattern Recognition. As in any other practical problem, a great deal of thought in preparation of the data and in selection and imple- mentation of methods is required; quick “feasibility studies ” in computer- based Pattern Recognition usually produce quick inconclusive results. Numerical methods in mathematical Pattern Recognition are based on relatively simple concepts; they depend for their success not upon sophistica- tion relative to the human, but upon the computer’s ability to store and process large numbers of samples in an exact manner and upon the computer’s ability to work in high dimensions. The human can do very sophisticated things in three dimensions or less, but begins to falter when dealing with higher dimensions; thus, when faced with a mass of data, the human often rushes to represent that data visually through a handful of two-dimensional charts and graphs. The approaches described in this book can be used to 1.1 What Is Pattern Recognition? 3 extract relationships among many variables not easily represented visually. The concepts and algorithms of mathematical Pattern Recognition have other applications that, on the surface, do not fit the basic description. These other uses include the following, most of which are discussed in greater detail in the body of the text.
- K.C. Fu(Author)
- 1968(Publication Date)
- Academic Press(Publisher)
CHAPTER 1 INTRODUCTION 1 .1 Pattern Recognition The problem of Pattern Recognition is that of classifying or labeling a group of objects on the basis of certain subjective requirements. Those objects classified into the same pattern class usually have some common properties. The classification requirements are subjective since different types of classifications occur under different situations. For example, in recognizing English characters, there are twenty-six pattern classes. However, in distinguishing English characters. from Chinese characters, there are only two pattern classes, i. e., English and Chinese. Human beings perform the task of Pattern Recognition in almost every level of the nervous system. Recently, engineers faced the problem of designing machines for Pattern Recognition. Preliminary results have been very encouraging. There have been some successful attempts to design or to program machines to read printed or typed characters, identify bank checks, classify electro- cardiograms, recognize some spoken words, play checkers and chess, and sort photographs. Other applications of Pattern Recognition include handwritten characters or word recognition, general medical diagnosis, system’s fault identification, seismic wave classification, target detection, weather prediction, speech recognition, etc. The simplest approach for Pattern Recognition is probably the approach of “template-matching.” In this case, a set of templates or prototypes, one for each pattern class, is stored in the machine. The input pattern (with unknown classification) is compared with the template of each class, and the classification is based on a preselected matching criterion or similarity criterion. In other words, if the input pattern matches the template of ith pattern class better than it matches any other template, then the input is classified as from the ith pattern class. Usually, for the simplicity of the machine, the templates are stored 1
eBook - PDFMechatronics Volume 2
Concepts in Artifical Intelligence
- Jeffrey Johnson, Philip Picton(Authors)
- 1995(Publication Date)
- Newnes(Publisher)
C HAPTE R 2 Pattern Recognition 2.1 Introduction Pattem recognition is fundamental to perception and cognition in intelligent systems. A machine's sensors can generate a huge number of combinations of inputs over short periods of time. To be useful it is necessary to transform these data into one of a set of known classes by recognizing patterns in the data. In perception, for example, a microphone delivers a waveform corresponding to sound. Until parts of the wave which correspond to words such as 'yes' and 'no' are recognized, these data may have little or no value. Pattern Recognition occurs also during cognition, for example, when a machine has to decide what to do next. Some patterns of data from the sensors combined with data in memory will require one action, other patterns will require other actions. Human beings are astoundingly good at Pattern Recognition; so good that the pioneers of machine intelligence severely underestimated how difficult it would be to represent patterns of any complexity. For example, can you see a pattern in the following configuration of dots? 9 9 9 000 0 0 0 0 0 0 0 0 0 O 0 0 0 0 9 O0 O 0 0 0 0 You probably see a pattern of one dot, two dots, three dots, etc., and you probably see that the dots form a straight line. Human pattem recognition is used extensively in large and small systems. For example, security systems includehuman guards whose job includes looking at television monitors and recognizing unusual patterns (perception), deciding if this pattern is an emergency (cognition), and raising the alarm if it is (execution). In industrial systems human quality controllers have to look at assemblies and recognize unusual configurations (perception), decide if the configuration is outside the specification (cognition), and physically move rejected parts (execu- tion).
eBook - PDFPattern Recognition: From Classical To Modern Approaches
From Classical to Modern Approaches
- Sankar Kumar Pal, Amita Pal(Authors)
- 2001(Publication Date)
- World Scientific(Publisher)
This chapter is subdivided as follows. Section 10.2 describes the essence of Pattern Recognition. Section 10.3 presents advanced neural network architectures and their associated learning methods. Section 10.4 examines applications of neural Pattern Recognition. Finally, the conclusions are stated in Section 10.5. 10.2 The essence of Pattern Recognition Pattern Recognition is a well-defined field of research that investigates the anal- ysis and design of systems capable of recognizing patterns in sensory data, no- tably in visual and sound data. A long-standing, unsolved goal has been the orientation-, location-, and scale-independent recognition of complex patterns. This area has been traditionally subdivided in statistical Pattern Recognition and syntactical Pattern Recognition. Statistical Pattern Recognition includes studies in discriminant analysis, feature extraction, and cluster analysis among others. Syntactical Pattern Recognition includes grammatical inference and parsing among others. Areas of application for Pattern Recognition algorithms include image anal- ysis, data mining, bioinformatics, optical character recognition, speech pro- cessing, man (medical) and machine diagnostics, financial trading, knowledge engineering, person identification, industrial inspection, among several others. More specific Pattern Recognition applications include fingerprint identification, handwriting recognition, X-ray image classification, DNA sequence analysis, internet search, stock option purchase decision support, target recognition, among many others. 10.2.1 Statistical Pattern Recognition We first introduce some topics in probability theory and the Bayes rule to discuss the case of discriminant analysis from a statistical Pattern Recognition perspective. This provides a concrete example for the statistical approach of Pattern Recognition.
eBook - PDF- Chin-liang Chang(Author)
- 1997(Publication Date)
- World Scientific(Publisher)
Chapter 5 Pattern Recognition Pattern Recognition is a field of classifying objects into classes or categories. For example, voice recognition classifies voices into words, and handwriting recognition classifies handwritings into letters, digits, or words. Each object (pattern) is usually represented by a set of feature (attribute) values. Many problems can be treated as Pattern Recognition problems. For example, consider resource allocation in project management and business reengineering. A list of resources needs to be allocated to a list of activitities. In this case, the resources can be treated as patterns and the activities can be treated as classes. NICEL in Decision Plus [Nicesoft Corporation 1994] is a very convenient language that can be used to represent a solution for a Pattern Recognition problem. Let attributes of a pattern be input variables, and classes cl,...,cn be output variables. The domain of these output variables is [0,1]. Figure 5-1 is a simple fuzzy program that classifies an employee into one of the two classes, Class 1 or Class2, according to his years of education, years of experience, and hourly wage. Figure 5-1 A Fuzzy Program for Pattern Recognition /* classify.nl */ /* Copyright © 1996 by Nicesoft Corporation */ /* edu=Employee f s year of education; exp=Employee , s year of experience; w=Employee f s hourly wage; class l=Class 1; class2=Class 2; */
eBook - PDF- Mike James(Author)
- 2013(Publication Date)
- Newnes(Publisher)
It is difficult to imagine a single Perceptron capable of recognising a particular Pattern Recognition 99 human face, but then why restrict ourselves to a single Perceptron? A really sophisticated recognition machine would use many 'banks' of Perceptrons. Each bank would feed its outputs to the next bank as detected features. As the amount of processing increased, the features that one bank fed to another would become increasingly complicated and more like the sort of features that humans give names to. For example, the first bank may detect lines in an image, the second bank angles and connected lines, a much later bank would detect squares and other shapes and so on. But all this is well beyond the range of the microcomputer! Uses of Pattern Recognition Sometimes the recognition of patterns is an end in its own right. For example, letter recognition is a useful and important Pattern Recognition problem because, if you can recognise letters, you can dispense with all of the work involved in transferring existing printed text to computers. In the same way, speech recognition is a useful facility even if it isn't used in conjunction with any other intelligent software. These and other important recognition problems have tended to emphasise Pattern Recognition as a subject in its own right with few connections with the rest of Al. However it seems reasonable to suppose that this will change as acceptable solutions are found to the simpler Pattern Recognition problems. The subjects of artificial vision and hearing are clearly important for a complete and satisfying solution to the more general Al problem and the emphasis is bound to shift to the interaction between Al and Pattern Recognition. There is another side to Pattern Recognition that is less obvious but equally important to Al. For example, one of the problems in implementing rule-based knowledge systems is recognising the 'condition' that is part of the IF...THEN rule.
eBook - PDFPattern Recognition and Artificial Intelligence, Towards an Integration
Proceedings of an International Workshop held in Amsterdam, May 18-20, 1988
- L.N. Kanal, E.S. Gelsema(Authors)
- 2014(Publication Date)
- North Holland(Publisher)
In the case of both expert systems and pattern-recognition systems, developers must create models of the application area. Although the pattern-recognition literature does not generally mention concepts such as knowledge acquisition or brittleness, these problems are also important in the construction of statistical models. This paper shows how builders of expert and pattern-recognition systems face many of the same challenges, and discusses ways in which the two research communities can learn from each other's experiences in creating different types of computational models for classification tasks. * Current address: Medical Computer Science Group, Knowledge Systems Laboratory, Stanford University School of Medicine, Stanford, California 94305-5479, U.S.A. 336 M. A. Musen and J. van der Lei 1. INTRODUCTION Workers in artificial intelligence (AI) and in Pattern Recognition share many goals. In particular, developers of expert systems, like developers of pattern-recognition systems, are often concerned with the creation of computer programs that can classify entities that people observe in the real world. An expert system such as MYCIN [1] attempts to classify a patient's infection in order to recommend appropriate therapy; a pattern-recognition program such as that used in a white-blood-cell differential counter attempts to classify the leukocytes in a patient's blood smear [2]. Both kinds of systems take, as their input, the values of case-specific features (for example, the patient has focal neurological signs or the cell has coarse granularity) and generate, as their output, corresponding classifications (for example, the infection is tuberculosis or- the cell is a basophil). Although the computational mechanisms are quite different, expert systems and pattern-recognition systems must distinguish among classes of entities on the basis of those entities' features. This common foundation is well recognized by workers in both disciplines [3].
- Mendel(Author)
- 1970(Publication Date)
- Academic Press(Publisher)
Th i s page i ntent i onally left blank R. 0. Duda 1 ELEMENTS OF Pattern Recognition I. Introduction The problem of designing or programming machines to recognize patterns is one of the most fascinating topics in the computer and information sciences. It appears in many different forms in a variety of disciplines, and the problems encountered range from the practical to the profound, from engineering economics to the philosophy of science. The great variety of pattern-recognition problems makes it difficult to say precisely what Pattern Recognition is. However, a good idea of the scope of the field can be gained by considering some typical pattern-recognition tasks. One of the classic examples of Pattern Recognition is the recognition of printed characters-alphanumerics, punctuation marks, mathematical symbols, etc. Much engineering effort has been devoted to the reading of machine-printed material, and optical character readers are available that can recognize a variety of styles of machine printing with remarkable accuracy and a speed far exceeding human abilities. The hallmark of this class of problems is that only a limited number of character types must be distinguished, and each character is derivable from an ideal pattern or template. This means that there is a conceptually easy way of classifying an unknown character-one merely compares 3 4 R. 0. DUDA it with an ideal version of each of the possible characters and sees which one it most nearly resembles. Though simple, this decision procedure is basic, and with various modifications it finds application in many pattern-recognition tasks. As the patterns to be recognized become subject to greater distortion and variability, the template-matching approach becomes less and less appropriate. For example, it would be difficult to classify hand-printed characters this way without using a very large number of templates, and for the recognition of cursive script this approach is virtually hopeless.
- C.E. Klopfenstein(Author)
- 2012(Publication Date)
- Academic Press(Publisher)
It is the purpose of Pattern Recognition, as used here, to provide the scientist with a problem-solving tool to help when the direct and theoretical approaches fail. The problem-solving tool that is referred to in the preceding paragraph is a collection of computer programs bound together in one large master program. The philosophy of Pattern Recognition and the mechanics of its methods occupy a considerable portion of this paper. It is difficult in many cases to decide which came first, the problem or the solution ( or actually the tool to solve the prob-lem). In the case of Pattern Recognition, several problems initiated the birth of several tools. It has been our approach to combine these methods to form a general problem solver. All that is left to do is to define the general problem which can be solved by the tool. A statement of the problem is: Given a collection of objects characterized by a list of measurements made on each object, is it possible to find and/or predict a property of the objects that may or may not be measurable itself, but is thought to be related to the measurements. If a particular application fits this rather general problem, it can be solved by the combination of methods described herein. For chemical applications, the “objects” mentioned above are usually elements, compounds, or mixtures. The measurements are chemical and physical properties that are either measured experi-mentally or found in the literature. The desired property depends on the particular application under study but can range from mo-lecular structural features to relative reactivities in heterogeneous catalysis. Pattern Recognition methods were originally designed to solve the class membership problem. The sought-for property in 6 Bruce R. Kowalski this case is membership in one of two or more possible classes. Classifying molecules as containing a particular functional group or not is an example.
- Chi Hau Chen, Louis-francois Pau, Patrick S P Wang(Authors)
- 1999(Publication Date)
- World Scientific(Publisher)
At the risk of overstating the case, it almost seems that in approaching the performance of a task, serial-digital computer algorithms tend to search all of the system space to find a reasonably good path from start state to goal state. We know such approaches are doomed to failure because of the combinatorial explosion in the number of paths to be tried. In contrast to the systematic, frontal-attack approach, biological systems seem to rely more on experience and education so that any good path or even a seg- ment of a good path is remembered, and that knowledge is transmitted through generations, either genetically or through education. In this latter mode of infor- mation processing, individual operations are of limited significance but patterns, both spatial and temporal, are of central importance. The significance of patterns is established by associations between a pattern (or a set of patterns) and other patterns (or sets of patterns). Accordingly, the formation of such associations and the activation of such linkages are matters of critical importance. One of the practical objectives of Pattern Recognition researchers has always been the ability to design and implement machine systems, which are able to per- form perception tasks competently to degrees of proficiency comparable to that of biological systems. To date it cannot be said that progress in that respect has been as substantial as desired or as expected. If we try to identify reasons for this relative lack of success, we might include the following. It would seem that detailed studies of information processing archi- tectures and procedures in actual biological neuronal studies are so difficult that progress comes at a very slow pace, indeed. Therefore guidance from that source, though much valued, is limited.
eBook - PDF- Tsypkin(Author)
- 1971(Publication Date)
- Academic Press(Publisher)
However, we cannot rush into the search for such a universal method. This is true not only because we have to solve some immediate problems of adaptation, but because it is not yet known whether such a method exists at all. C O M M E N T S 4.1 The fact that Pattern Recognition is the first stage in information processing was often emphasized by Harkevich (1959, 1965). One can spend much time reading about the dis- cussions on the topic “man or a machine.” The author has become familiar with such discussions by reading the books by Taube (1961) and Kobrinskii (1965), and he recommends them to the reader. The special problems in Pattern Recognition were treated in the books by Sebestyen (1962b) and Nilsson (1965). 4.2 The hypothesis of compactness was proposed by Braverman (1962). Its more rigorous form, obtained after a long period of time, 1s known as the hypothesis of representation, which is discussed in Section 4.5. 4.3 Similar functionals were introduced by Yakubovich (1965) for the quadratic loss functions, and by the author (Tsypkin, 1965b, 1966) for the general case. 4.4 Approximation of an arbitrary function using a system of linearly independent or orthogonal functions is broadly used in the solution of various engineering problems. The approach presented here is based on the correspondence by the author (Tsypkin, 1965a) and the paper by Devyaterikov et al. (1967). The algorithms of the type (4.12) for certain particular cases (F(.) is either a linear or relay type of function) were described by Aizernian et al. (1964~) on the basis of their potential function method. Furthermore, they have also obtained the algorithms of the type (4.9) from such algorithms.
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