The objective of this book is to talk about the usage of Fuzzy Logic in Pattern Recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that i have chosen to process the data completely depends on the type of data. Pattern recognition as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explanation of the process generating the data clarity seen and so on. In this book I have focused to venture the ways in which Fuzzy Logic is applied to pattern recognition which is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. Most pattern recognition techniques involve treating the data as a variable applies standard processing techniques to it. Here I have discussed dynamic and efficient way of pattern recognition technique which should help to analyze evolving large number of data sets and making right decision
Stroke Analysis on CT Images: A Pattern Recognition Approach discusses briefly everything from stroke,it's causes, conventional methods of stroke analysis on CT images to analysis using texture properties and classification of CT images. Texture analysis and other pattern recognition approaches ,for stroke analysis, are largely discussed in this book with examples and great number of formulas, and real results. A great reference for beginners.
Pattern Recognition is widely spread as a basis for classification purposes. However, the common pattern recognition and classification methods are usually used on non-spatially-correlated data. For example, moment invariants are a very basic and popular tool for this purpose. Such methods cannot be easily applied on flow data, like wind streams and water flows, magma flows, blood flows for medical purposes, air flow simulations for the design of cars and airplanes. For this reason, this book explains how classic moment invariants have been transformed to facilitate pattern recognition on flow data. The properties of the "Flow Moment Invariants" are analyzed and used to perform Feature-based and Comparative Visualization of flow data.
Character recognition is one of the oldest sub fields of pattern recognition. Recognition systems have been effectively developed for recognition of printed and handwritten characters of non-Indian languages like Chinese, English and Arabic. Efforts are being done to design efficient recognition systems for few of the Indian languages including Bangla, Hindi, and Telgu. This book presents a study on isolated Marathi handwritten numeral recognition. Marathi, written in Devnagari script, is the 4th most spoken language in India and the 15th most spoken language in the world. Different feature extraction methods have been proposed in this book. Well known classifiers namely K-NN and Sopport Vector Machines are used for classification and recognition. Experiments are carried out on a large numeral dataset created manually by us. Recognition results are being compared with the results presented by different authors in the literature.
This book is beneficial for those researchers who are working on Mahine Learning techniques, Support Vector Machine, Handwritten Characters of any language or Pattern Recognition. Handwritten character recognition is one of the application domains in pattern classification. Recognition of Handwritten Devanagari Numerals/ Characters is a complicated task due to the unconstrained shape variations, different writing styles and different kinds of noise. Also, handwriting depends much on the writer and because one does not always write the same digit in exactly the same way. Support Vector Machine is one of the better classifier among all Machine Learning algorithms for pattern recognition. Most researchers have applied it on English, Persian, Chinese, Arabic and Tamil characters as well as numerals and acquired better recognition rate. For extracting features from each sample, the hybrid approach of Moment Invariant and Affine Moment Invariant has been adopted. Overall 18 features corresponding to each numeral proceed for classification using Support Vector Machine classifier. The recognition rate of this method is 99.48%.
Over the past decade, pattern recognition has been one of the fastest growth points in chemometrics. This has been catalysed by the increase in capabilities of automated instruments such as LCMS, GCMS, and NMR, to name a few, to obtain large quantities of data, and, in parallel, the significant growth in applications especially in biomedical analytical chemical measurements of extracts from humans and animals, together with the increased capabilities of desktop computing. The interpretation of such multivariate datasets has required the application and development of new chemometric techniques such as pattern recognition, the focus of this work. Included within the text are: ‘Real world’ pattern recognition case studies from a wide variety of sources including biology, medicine, materials, pharmaceuticals, food, forensics and environmental science; Discussions of methods, many of which are also common in biology, biological analytical chemistry and machine learning; Common tools such as Partial Least Squares and Principal Components Analysis, as well as those that are rarely used in chemometrics such as Self Organising Maps and Support Vector Machines; Representation in full colour; Validation of models and hypothesis testing, and the underlying motivation of the methods, including how to avoid some common pitfalls. Relevant to active chemometricians and analytical scientists in industry, academia and government establishments as well as those involved in applying statistics and computational pattern recognition.
Over the last decades, numerous face recognition methods have been proposed to overcome the problem limited by the current technology associated with face variations. Among them, the PCA/LDA method has known to be one of the best face recognition methods. In this thesis, we implement a face recognition method, using PCA&LDA Algorithm and compare these both algorithms with respect to time, memory and accuracy. Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM’s
During my Ph.D study I had been challenged by many pattern recognition tasks which coming from very different disciplines. Each task had it's unique problems. It took great efforts to find a suitable way to solve each of the problem. In return, this thesis has covered a very broad spectrum of Chemometrics pattern recognition techniques, from well known and commonly used models such as PCA, PLS-DA to state of the art models such as kernel learning and support vector machines. There is also a good coverage of the methods which are much less known to the community but could be very useful methods such as principal coordiante analysis, featureless pattern recognitions and multivariate pattern comparison. The usefulness of these methods were demonstrated through several real-life applications including single nucleotide polymorphism analysis, DNA microarray analysis and a few human and mice metabolomic profiling studies. The comprehensive coverage of chemometrics pattern recognition techniues combined with several applications could be very useful to those who work in a similar discipline.
Nowadays neural networks is playing a vital role in various fields and emerge as a important tool in predictions and classification.The book contains introductory neural network technique for recognition of handwritten characters. Handwritten character recognition is one of the most active areas of research. Developing a handwritten Devnagri character recognition system is a big challenge due to high variability of writing styles. This book is a novel approach in recognition of handwritten Devnagri characters and analysis of various suitable performance measures. The character recognition and performance measures analysis is done by using neural network tool box in MATLAB. It will be helpful to researchers working in this area to develop appropriate methodology. The book also throws light on performance evaluation of the existing algorithms on pattern recognition.The contents of the book include 1.Neural Networks in Pattern Recognition 2.Handwritten Character Recognition 3.Segmentation 4.Feature Extraction 5.Classification 6.Back Propagation Network 7.Scaled Conjugate Gradient Algorithm 8.Levenberg-Marquardt Algorith
Stochastic inference is defined as an accuracy measure over the decision of a learning algorithm. The typical accuracy measures used for pattern recognition are confidence and credibility. These measures are challenging to define, compute and exploit to improve pattern recognition. In this research we define a confidence and a credibility measure based on the VC dimension of a learning algorithm defined by Vapnik and Chervonenkis and the notion of algorithmic randomness as defined by Kolmogorov. The resulting confidence and credibility measures are applied to pattern recognition methods to improve their accuracy. This is accomplished by developing a multi-level architecture based on the defined confidence and credibility. In addition these defined measures are used to extend the binary classification of a single SVM to multi-class prediction. The benefits of the proposed architecture and the multi-class SVM are demonstrated on the following classification problems: agitation detection, the well known US postal handwritten digit recognition data and for forest fire occurrence prediction.
Face recognition is the most popular bio-metric security-identification system being used all over the world. It is a dedicated process (much more versatile than the general object-recognition process) spanning different disciplines, which include cognitive science, computer vision, image processing, neural networks, neuroscience, pattern recognition, physiology and psychology. Partial face recognition is fast growing as the technique of choice during the past few years. It is one of the most striking applications of image analysis and understanding. Two reasons may be cited for this trend — the first is a wide range of commercial and law-enforcement applications and the second is depth of these technologies in terms of foreseeable enhancements during the next half-century. In fact, machine recognition of human partial faces as a challenging problem continues to attract researchers from disciplines.
Face recognition and modeling has been an active research area over last 35 years. This research spans several disciplines such as image processing, pattern recognition, computer vision, and neural networks. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer sciences. Psychologists and neuroscientists mainly deal with the human perception part of the topic, whereas engineers studying on machine recognition of human faces deal with the computational aspects of face recognition. Face recognition has applications mainly in the fields of biometrics, access control, law enforcement, and security and surveillance systems.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.