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.

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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

The tomographic reconstruction for cone-beam geometries is a computationally intensive task requiring large memory and computational power to investigate interesting objects. The analysis of its parallel implementation on widely available clusters of SMPs requires an extension of the original LogP model to account for the various communication channels, called LogSMP. The LogSMP model is used in analyzing this algorithm, which predicts speedup on a cluster of 4 SMPs using 10 Mbps, 100Mbps, and ATM networks. We detail the measurement of the LogSMP parameters and assess the applicability of LogSMP modeling to the cone-beam tomography problem. This methodology can be applied to similar problems involving clusters of SMPs.

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Modeling Cone-Beam Tomographic Reconstruction Using LogSMP: An Extenced LogP Model for Clusters of SMPs

Cone beam geometries are increasingly of interest for X-ray CT applications to improve imaging efficiency. In this paper, we describe our practical experience implementing circular orbit cone beam backprojection on workstation clusters. The reconstruction problem is computationally intensive, particularly for arrays of 512 voxels in each direction. A voxel driven approach is described where the reconstruction volume is partitioned into variable width slabs and each slab given to a workstation. Each projection is filtered by one workstation and then sent to the others for backprojection. While most computation is done in the backprojection step, a significant amount of time must be spent in sending projectional data. A method is detailed to further reduce the communication overhead by restricting the amount of projection sent to only what is required by each backprojecting workstation. Furthermore, if the shape of the backprojection slabs is made as square as possible, the total communication requirement can be minimized. By reduction of the communication requirement, an overall improvement in processor utilization was observed, and the crossover point where communication dominates was improved.

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Parallel computing methods for X-ray cone beam tomography with large array sizes

Many target tracking methods rely on the use of centroids as features points. In several applications such as the tracking of vehicles on roadways, the appropriate target paths are known a priori. Based on this observation, an efficient centroid computation method is presented and analyzed in this paper.

Efficient algorithm for centroid calculation for multiple-target tracking

A method for extracting road boundaries using the monochrome image of a visual road scene is presented. The statistical information regarding the intensity levels present in the image along with some geometrical constraints concerning the road are the basics of this approach. Results and advantages of this technique compared to others are discussed. The major advantages of this technique, when compared to others, are its ability to process the image in only one pass, to limit the area searched in the image using only knowledge concerning the road geometry and previous boundary information, and dynamically adjust for inconsistencies in the located boundary information, all of which helps to increase the efficacy of this technique.

Road boundary detection

A radial basis function (RBF) neural network is considered as one of the universal input-output functional mapping learning systems. Important issues in designing an efficient RBF neural network are the number of neurons and the shape and location of neurons to define local receptive fields in feature space. This paper presents a solution to these problems using decision tree partitioning. A conversion algorithm from decision tree to RBF neural network is described. Two examples are presented to illustrate the proposed approach.

Ishwar K. Sethi

Papers

Modeling Cone-Beam Tomographic Reconstruction Using LogSMP: An Extenced LogP Model for Clusters of SMPs

Parallel computing methods for X-ray cone beam tomography with large array sizes

Efficient algorithm for centroid calculation for multiple-target tracking

Road boundary detection

Design of radial basis function networks using decision trees