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.
5 Key excerpts on "Pattern Recognition"
eBook - ePubProcess Mining Techniques for Pattern Recognition
Concepts, Theory, and Practice
- Vikash Yadav, Anil Kumar Dubey, Harivans Pratap Singh, Gaurav Dubey, Erma Suryani, Vikash Yadav, Anil Kumar Dubey, Harivans Pratap Singh, Gaurav Dubey, Erma Suryani(Authors)
- 2022(Publication Date)
- CRC Press(Publisher)
...5 Pattern Recognition Akanksha Toshniwal DOI: 10.1201/9781003169550-5 CONTENTS 1 Introduction 2 Probability Theory 2.1 Bayesian Probability Theory 3 Curve Fitting 3.1 Bias and Variance 3.1.1 Bias Variance Trade-Off 4 The Curse of Dimensionality 5 Classification 6 Clustering Note References 1 INTRODUCTION Searching for patterns has a long history. For example, in the 16th century, Johannes Kepler discovered the empirical law of planetary motion based on the extensive astronomical observations by Tycho. The discovery of quantum physics established regularities in atomic spectra in the early 20th century. The human brain is programmed to observe regular patterns and reacts in the event of any irregularities. For example: a human baby can recognize a real elephant animal by mapping the features from the elephant drawings or cartoons movies. In computer science, the field of Pattern Recognition is concerned with the automatic discovery of regularities in data using computer algorithms and, with the use of these regularities, taking actions such as classifying the data into different categories (Bishop, 2006). Consider an example such as recognizing categories of portraits, illustrated in Figure 5.1. Each image has 40 binary attributes and five landmark locations. Each image is represented by a vector of size 45, and the goal is to develop a machine algorithm which takes these feature vectors as input and produces the identification of classes like eyeglasses, wearing hat, wavy hair, bangs, moustache, smiling face, pointy nose, or oval face or to build a machine which performs face attribute recognition, face detection, landmark (or facial part) localization, and face editing and synthesis. Since humans have a wide variety of facial features, this is a non-trivial problem. FIGURE 5.1 Sample images from the CeleFaces data set...
eBook - ePub- King-Sun Fu(Author)
- 2019(Publication Date)
- CRC Press(Publisher)
...Chapter 8 APPLICATION OF Pattern Recognition TO MEDICAL DATA ANALYSIS Kendall Preston Jr. TABLE OF CONTENTS I. Introduction II. Computerized Medical Data Organization III. Medical Data Analysis A. Automated Treatment Planning B. Computerized Medical Decision-Making IV. Automated Patient Monitoring V. Medical Image Display and Analysis A. Medical Image Display B. Analysis of Image Data 1. Multispectral Spatial Distribution Techniques 2. Scene Segmentation and Feature Extraction 3. Cellular Logic Image Processing VI. Conclusion References I. Introduction This chapter concentrates on the two major clinical applications of Pattern Recognition in medicine, namely, (1) the automation of the analysis of the electrocardiogram and (2) the automation of the analysis of images of the human white blood cell. Also, in order to present a unified picture of the state of the art in the field, mention is made of the status of (1) the computerization of medical taxonomy, (2) the automatic diagnosis of disease, and (3) automation of image generation and display, particularly with reference to computed tomography. II. Computerized Medical Data Organization Computerized taxonomy as related to medical data is, of course, pattern classification rather than Pattern Recognition. Since, however, the output of all Pattern Recognition systems in medical data analysis is the classified pattern, and since this classification must be codified for both storage and retrieval, it is important to briefly review current taxonomic systems in the medical data field. Systems for medical data classification date back to the early 18th century, e.g., the Nosología Methodica of de Lacroix (1706 to 1777). With the advent of general purpose electronic computers in the 20th century, a revolution has taken place in the methodology applied to medical data classification...
eBook - ePubCognitive Psychology and Information Processing
An Introduction
- R. Lachman, J. L. Lachman, E. C. Butterfield(Authors)
- 2015(Publication Date)
- Psychology Press(Publisher)
...Objects are only one type of the many kinds of patterns present in environmental stimulation that people recognize. For example, to understand speech we must recognize the auditory patterns that correspond to meaningful words. Reading similarly involves recognition of the arbitrary visual patterns used to represent letters of the alphabet. These examples illustrate that Pattern Recognition is essential to almost all our waking activities. In fact, every living thing must recognize patterns when it interacts meaningfully with its world. Pattern Recognition research is an important part of cognitive psychology, as well as of such other disciplines, as neurobiology, computer science, and communications engineering. All of these disciplines are concerned with systems (man, animal, or machine) that convert complex inputs (such as patterns of lights and sounds) into recognizable and meaningful words, objects, or events. From the information-processing point of view, how people recognize patterns is one of the more interesting and researchable aspects of the larger question of how they perceive and interact with their environment. Yet the study of Pattern Recognition was not always thought to be important for understanding perception and cognition. Nineteenth- and early twentieth-century experimental psychologists took their task to be the discovery of the elements of sensation and the explanation of how the laws of association combined these elements into larger complexes or patterns. In the view of the earliest experimental psychologists, the elements of sensation existed entirely in the stimulus, and the function of the peripheral nervous system was to decompose complex physical stimuli into their simpler elements. The function of the brain was to combine the elemental sensations into perceptions. Peripheral decomposition and central combination were thought to occur passively, in accordance with the laws of association...
eBook - ePubIntroduction to Artificial Intelligence
Second, Enlarged Edition
- Philip C. Jackson(Author)
- 2013(Publication Date)
- Dover Publications(Publisher)
...However, other types of pattern perception will be discussed in the next two sections and in the last section of this chapter. A wide variety of approaches have been followed toward visual pattern perception by machines. An attempt will be made to summarize some of the most important approaches and indicate the ways in which each approach is related to the others. However, there is not space in this chapter for a complete survey of the subject. For a more complete summary of vision systems, refer to the book by Duda and Hart (1973), and the survey papers by Rosenfeld (1972) and Turner (1971). SOME BASIC DEFINITIONS AND EXAMPLES AI researchers have adopted a set of basic definitions for the word “pattern” which are fairly consistent with the definitions used by researchers in other fields (e.g., “numerical taxonomy,” “behavioristic psychology,” “theoretical linguistics”). The definitions are not very hard to understand. However, since the word “pattern” is usually not defined in everyday conversation, this section is devoted to an explication of its use in AI research and a discussion of some general problems involving “patterns” that have been considered by AI researchers. A pattern is a collection of objects, each of which has the property that it satisfies a certain criterion, known as the pattern rule for the pattern. The objects in a pattern are said to be pattern examples. (Research papers sometimes confuse these ideas, using the word “pattern” to denote what we have chosen to call pattern rules and pattern examples.) Artificial intelligence research has been concerned with several basic problems involving patterns, pattern rules, and pattern examples. 1. (Classification) Given an object and a collection of pattern rules, determine which pattern rules are satisfied by the object. 2. (Matching) Given a pattern rule and a collection of objects, find those objects which satisfy the pattern rule. 3...
eBook - ePub- Nick Lund(Author)
- 2020(Publication Date)
- Routledge(Publisher)
...5 Pattern Recognition Introduction Template matching theories Feature detection theories Prototype theories Pattern Recognition: an integrated view The role of context and expectations in Pattern Recognition Summary Introduction Examine the following set of characters and decide which one is different: Did you decide that the character third from the right was different? If you inspect the line carefully you will realise that all the characters are different. However, we tend to perceive the line of characters as being one ‘A’ and nine ‘T’s. There is something about nine of the characters that we recognise as a T even though they are all different. There are hundreds of different fonts used in books yet we are able to recognise letters in a fraction of a second and read the words accurately. Everyone’s handwriting is different but what initially seems to be a meaningless scrawl becomes recognisable as letters. Recognition of letters and reading are skills we barely think about as adults, but it is a complicated process. Pattern Recognition has been defined as ‘the ability to abstract and integrate certain elements of a stimulus into an organised scheme for memory storage and retrieval’ (Solso, 1998). Pattern Recognition is central to perception and attention and involves an interaction between sensation, perception, short-term memory and long-term memory. Features of Pattern Recognition Solso (1998) has outlined five principles of Pattern Recognition, which are based both on laboratory studies and on everyday experiences. He points out that Pattern Recognition enables us to: Recognise familiar patterns quickly and accurately. We recognise letters without effort and can easily pick out pictures of our house, friends, etc. Recognise and classify unfamiliar objects. No matter how unique a font is we are able to analyse letters...
