Computational geometry

Computational geometry.

An introduction to computer vision illumination and fixturing sensors image acquisition and representation fundamentals of digital image processing image analysis the segmentation problem 2-D shape description and recognition 3-D object representations robot programming and robot vision bin picking trends and aspirations.

Machine vision: automated visual inspection and robot vision

Image Analysis and Computer Vision

The past decade has seen the emergence of Vehicular Clouds as a topic of research in its own right. Vehicular clouds were inspired by the reality of present-day vehicles featuring an impressive array of on-board compute, storage and sensing capabilities and by the insight that these on-board capabilities are a vast untapped resource that, at the moment, is wasted. Harvesting these vehicular resources and putting them to work in a meaningful and productive way promises to have a significant and lasting societal impact. One of the defining ways in which vehicular clouds differ from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where reasoning about fundamental performance metrics becomes very challenging. Given that vehicular clouds and their numerous variants have becomes a very active field of research, this seems to be a good moment to survey the state of the art of vehicular cloud research. In this survey, we adopt an inclusive rather than an exclusive definition of various flavors of vehicular clouds. Vehicular clouds, in their many incarnations, have a huge array of potential applications. In order to bring this potential to fruition, significant research challenges have to be overcome. With this in mind, this survey identifies promising applications and draws attention to a number of research challenges facing the vehicular clouds community at large.

A Survey of Vehicular Cloud Research: Trends, Applications and Challenges

A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of intervals have been proposed in the literature. The authors develop computational tools and show how they can be used for the purpose of devising cost-optimal parallel algorithms for a number of interval-related problems, including finding a largest subset of pairwise nonoverlapping intervals, a minimum dominating subset of intervals, along with algorithms to compute the shortest path between a pair of intervals and, based on the shortest path, a parallel algorithm to find the center of the family of intervals. More precisely, with an arbitrary family of n intervals as input, all the algorithms run in O(log n) time using O(n) processors in the EREW-PRAM model of computation. >

Optimal parallel algorithms for problems modeled by a family of intervals

We propose time-optimal algorithms for a number of convex polygon problems on meshes with multiple broadcasting. Specifically, we show that on a mesh with multiple broadcasting of size n × n, the t...

Convex Polygon Problems on Meshes with Multiple Broadcasting

Recently it has been noticed that for semigroup computations and for selection, rectangular meshes with multiple broadcasting yield faster algorithms than their square counterparts. The contribution of the paper is to provide yet another example of a fundamental problem for which this phenomenon occurs. Specifically, we show that the problem of computing the convex hull of a set of n sorted points in the plane can be solved in O(n/sup 1/8/ log /sup 3/4/) time on a rectangular mesh with multiple broadcasting of size n/sup 3/8/ log/sup 1/4/ n/spl times/n/sup 5/8//log/sup 1/4/n. The fastest previously known algorithms on a square mesh of size /spl radic/n/spl times//spl radic/n run in O(n/sup 1/6/) time in case the n points are pixels in a binary image, and in O(n/sup 1/6/log/sup 3/2/ n) time for sorted points in the plane.

Square meshes are not optimal for convex hull computation

A number of data movement algorithms for the two-dimensional reconfigurable mesh are presented. These include computing the prefix sum of a binary sequence and computing the prefix maxima of a sequence of real numbers. These algorithms lead to a fast algorithm to sort a sequence of n reals in O(log n/log m) time on a reconfigurable mesh of size mn*n with 3 >

J.L. Schwing

Papers

Optimal parallel algorithms for problems modeled by a family of intervals

Convex Polygon Problems on Meshes with Multiple Broadcasting

Square meshes are not optimal for convex hull computation

Fundamental data movement algorithms for reconfigurable meshes

Convexity problems on meshes with multiple broadcasting