Michael T. Goodrich

Bio: Michael T. Goodrich is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Planar graph & Parallel algorithm. The author has an hindex of 61, co-authored 430 publications receiving 14045 citations. Previous affiliations of Michael T. Goodrich include New York University & Technion – Israel Institute of Technology.

Optimal parallel algorithms for point-set and polygon problems

Abstract: In this paper we give parallel algorithms for a number of problems defined on point sets and polygons. All our algorithms have optimalT(n) * P(n) products, whereT(n) is the time complexity andP(n) is the number of processors used, and are for the EREW PRAM or CREW PRAM models. Our algorithms provide parallel analogues to well-known phenomena from sequential computational geometry, such as the fact that problems for polygons can oftentimes be solved more efficiently than point-set problems, and that nearest-neighbor problems can be solved without explicitly constructing a Voronoi diagram.

In-place techniques for parallel convex hull algorithms (preliminary version)

Abstract: We present a number of efficient parallel algorithms for constructing 2and 3-dimensional convex hulls on a randomized CRCW PRAM. Specifically, we show how to build the convex hull of n pre-sorted points in the plane almost surely in O(1) time using O(n log n) processors, or, alternately, almost surely in O(log” n) time using an optimal number of processors. We also show how to find the convex hull of n unsorted points in R2 (resp., 3?s) in O(log n) time using O(n log h) work (resp., 0(log2 n) time using O(min{n logz h, n log n}) work), with very high probability, where h is the number of edges in the convex hull (h is O(n), but can be as small as O(l)). Our algorithms for unsorted input depend on the use of new in-place procedures, that is, procedures that are defined on a subset of elements in the input and that work without re-ordering the input. For the pre-sorted case we also exploit a technique that allows one to modify an algorithm that assumes it is given points so that it can be used on hulls; we call such algorithms point-loll invariant. * This research supported in part by NSF and DARPA under Grant CCR-8908092. Email: ghouse@cs.jhu.edu. t ‘lhis rese~ch supported in part by the National Science FOUIIdation under Grant CCR-9003299, and by NSF and DARPA under Grant CCR-8908092. Email: goodrich@cs.jhu. edu. Permission to copy without fee all or part of this matertial is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.

Efficient perspective-accurate silhouette computation and applications

Abstract: Silhouettes are perceptually and geometrically salient features of geo metric models. Hence a number of graphics and visualization applications need to find them to aid further processing. The efficient computation of silhouettes, especially in the context of perspective projection, is known to be difficult. This paper presents a novel efficient and practical algorithm to compute silhouettes from a sequence of viewpoints under perspective projection. Parallel projection is a special case of this algorithm. Our approach is based on a point-plane duality in three dimensions, which allows an efficient computation of the \emph{changes} in the silhouette of a polygonal model between consecutive frames. In addition, we present several applications of our technique to problems from computer graphics and medical visualization. We also provide experimental data that show the efficiency of our approach.

Confluent drawings: Visualizing non-planar diagrams in a planar way

Abstract: We introduce a new approach for drawing diagrams. Our approach is to use a technique we call confluent drawing for visualizing non-planar graphs in a planar way. This approach allows us to draw, in a crossing-free manner, graphs-such as software interaction diagrams-that would normally have many crossings. The main idea of this approach is quite simple: we allow groups of edges to be merged together and drawn as tracks (similar to train tracks). Producing such confluent diagrams automatically from a graph with many crossings is quite challenging, however, so we offer two heuristic algorithms to test if a non-planar graph can be drawn efficiently in a confluent way. In addition, we identify several large classes of graphs that can be completely categorized as being either confluently drawable or confluently non-drawable.

Geometric Pattern Matching Under Euclidean Motion.

Abstract: Given two planar sets A and B, we examine the problem of determining the smallest ϵ such that there is a Euclidean motion (rotation and translation) of A that brings each member of A within distance ϵ of some member of B. We establish upper bounds on the combinatorial complexity of this subproblem in model-based computer vision, when the sets A and B contain points, line segments, or (filled-in) polygons. We also show how to use our methods to substantially improve on existing algorithms for finding the minimum Hausdorff distance under Euclidean motion.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Planning Algorithms: Introductory Material

Abstract: 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.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

Abstract: This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

An efficient k-means clustering algorithm: analysis and implementation

Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.