Data Mining - Concepts and Techniques.

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

International Journal of Computer and Applications

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Soft biometrics-based gender classification is an interesting and a challenging area of neural networking and has potential application in visual surveillance as well as human–computer interaction. In this paper, we have investigated gender recognition from human gait in image sequence. For the above purpose, we have extracted silhouette of 15 males and 15 females from the database collected from CASIA Gait Database (Dataset B). The computer-vision-based gender classification is then carried out on the basis of standard deviation, center of mass, and height from head to toe. Experimental results demonstrate that the present gender recognition systems achieve superior recognition performance of 96.8 % on feed-forward back-propagation (FFBP) network. Data on different networks have also been trained and tested. The above study indicates that gait-based gender recognition is one of the best reliable biometric technologies that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations, and even airports need to quickly detect threats and provide differing levels of access to different user groups.

Gender Identification Using Gait Biometrics

A Distributed Cooperative Approach for Dynamic Target Search Using Particle Swarm Optimization with Limited Intercommunication

Improved Approach to Area Exploration in an Unknown Environment by Mobile Robot using Genetic Algorithm, Real time Reinforcement Learning and Co-operation among the Controllers

In the past we have seen various developments in the philosophy and application of neural networks. We today have backpropagation algorithm, Hopfield networks, perceptrons, etc All these are very precise tools which model the data very well. But unfortunately, the problem being faced these days is of training the neural network in short span of time, over the test data. All the above mentioned tools may not be useful in various situations where the neural network needs to be trained rapidly. Hence the solutions offered to the same were the Corners rule and the associated CC1 to CC4 algorithms. All these had various pros and cons. This paper uses a different type of modeling to represent data and hence solve the problem of fast learning. Here we have taken the help of distance separation of training data and an unknown input to calculate the most probable output in the neural network. This algorithm is better than the others as it does not place any special restrictions on the inputs, which was the case with CC3. Also the algorithm uses an input model very similar to the traditional model, in terms of inputs and outputs. Hence the users may find it very easy to switch between the traditional neural network style and the network proposed in this paper. The algorithm sets up a neural network. The weights are assigned by looking at the inputs. In testing, the inputs are provided and the most probable output is calculated. The neural network uses a single hidden layer. The best neurons of the hidden layer are invoked at every input. This algorithm was trained on some points of a 2 color picture. When we tried to reproduce it, the results showed the algorithm was efficient and accurate.

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Fast Learning Neural Network Using Modified Corners Algorithm

Biometric authentication techniques are more consistent and efficient than conventional authentication techniques and can be used in monitoring, transaction authentication, information retrieval, access control, forensics, etc. In this paper, we have presented a detailed comparative analysis between Principle Component Analysis (PCA) and Independent Component Analysis (ICA) which are used for feature extraction on the basis of different Artificial Neural Network (ANN) such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). In this paper, we have chosen "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English for the incorporation of above methods. The six geometric lip features i.e. height of the outer corners of the mouth, width of the outer corners of the mouth, height of the inner corners of the mouth, width of the inner corners of the mouth, height of the upper lip, and height of the lower lip which extracts the identity relevant information are considered for the research work. After the comprehensive analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is achieved using PCA and RBF and 87.36% accuracy is achieved using ICA and RBF. Speaker identification has a wide scope of applications such as access control, monitoring, transaction authentication, information retrieval, forensics, etc.

Anupam Shukla

Papers

Gender Identification Using Gait Biometrics

A Distributed Cooperative Approach for Dynamic Target Search Using Particle Swarm Optimization with Limited Intercommunication

Improved Approach to Area Exploration in an Unknown Environment by Mobile Robot using Genetic Algorithm, Real time Reinforcement Learning and Co-operation among the Controllers

Fast Learning Neural Network Using Modified Corners Algorithm

Intelligent System for Speaker Identification using Lip features with PCA and ICA