The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Classification and regression trees

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Data Mining - Concepts and Techniques.

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.

/pdf/statistical-pattern-recognition-a-review-1kz1b0tte4.pdf

Statistical pattern recognition: a review

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Feature point matching in image sequences

A model based two-dimensional object recognition system capable of performing under occlusion and geometric transformation is described in this paper. The system is based on the concept of associative search using overlapping local features. During the training phase, the local features are hashed to set up the associations between the features and models. In the recognition phase, the same hashing procedure is used to retrieve associations that participate in a voting process to determine the identity of the shape. Two associative retrieval techniques for discrete and continuous features, respectively, are described in the paper. The performance of the system is studied using a test set of 1,000 shapes that are corrupted versions of 100 models in the shape database. It is shown that the incorporation of a verification phase to confirm the retrieved associations can provide zero error performance with a small reject rate.

Local association based recognition of two-dimensional objects

All mobile carriers these days have provided 3G and 4G services to their customers. The evolution of cellular networks from 3G to 4G has improved several performance metrics of the data communications. The focus of this paper is to evaluate two of the most important performance metrics: throughput and delay. We consider the cellular network as an integrate infrastructure that includes mobile and fixed nodes. Based on this model we calculate and analyze the throughput and delay. Our results illustrate that the throughput is increased while the delay is decreased in 4G data network compared to the previous 3G architecture. In addition, we evaluate how the delay affects the security of the network.

/pdf/evaluating-throughput-and-delay-in-3g-and-4g-mobile-c23sdy0dug.pdf

Evaluating Throughput and Delay in 3G and 4G Mobile Architectures

In this paper we present an accurate technique for automatic detection of fiducial markers in 3d brain images so that fully automatic landmark-based coregistration can be implemented. In our tests, our approach detected successfully 429 out of 430 fiducial markers that can be recognized by human eyes. Thus, in landmarkbased image-registrations, our template-based technique can eliminate the procedure that waits for users to manually pick fiducial landmarks in images.

A template based technique for automatic detection of fiducial markers in 3D brain images

Dept of Computer Science and Engineering, Oakland University, USA Institute for Scientific Computing , Wayne State University, USA Abstract. Point-based registration is one of the most popular registration methods in practice. During registration, we use external fiducial markers that are rigidly attached through the skin to the skull. Determining the coordinates of the fiducials without human selection is crucial for a fully automatic point-based image registration. In this paper, an image processing technique for automatic fiducial detection is presented. The technique, which is fast, automatic and accurate, has two major steps: Edge map construction and curvature-based object detection. Experimental results are promising compared to the manul selection by experts with no error for 56 CT images and 3% error for 66 MR images.

Ishwar K. Sethi

Papers

Feature point matching in image sequences

Local association based recognition of two-dimensional objects

Evaluating Throughput and Delay in 3G and 4G Mobile Architectures

A template based technique for automatic detection of fiducial markers in 3D brain images

Automatic fiducial localization in brain images