We present a distributed vision-based surveillance system. The system acquires and processes grey level images through one or multiple camera units monitoring certain area(s) via a local area network (LAN) and is capable of combining information from multiple camera units to obtain a consensus decision. It can be trained to detect certain type of intrusions, for example pedestrians, a group of pedestrians, vehicles, pets, etc., and minimizes false alerts due to other non-interested intrusions. As a case study, we aim to detect pedestrian/vehicle in an observation area. Our vision-based intrusion detection approach consists of two main steps: background subtraction based hypothesis generation (HG) and appearance-based hypothesis verification (HV). HG hypothesizes possible threats (intrusions), and HV verifies those hypotheses using a Gabor filter for feature extraction and support vector machines (SVMs) for classification. The system has been tested in an unconstrained outdoor environment, illustrating good performance.

Investigates the application of genetic algorithms (GAs) for recognizing real 2D or 3D objects from 2D intensity images, assuming that the viewpoint is arbitrary. Our approach is model-based (i.e. we assume a pre-defined set of models), while our recognition strategy relies on the theory of algebraic functions of views. According to this theory, the variety of 2D views depicting an object can be expressed as a combination of a small number of 2D views of the object. This implies a simple and powerful strategy for object recognition: novel 2D views of an object (2D or 3D) can be recognized by simply matching them to combinations of known 2D views of the object. In other words, objects in a scene are recognized by "predicting" their appearance through the combination of known views of the objects. This is an important idea, which is also supported by psychophysical findings indicating that the human visual system works in a similar way. The main difficulty in implementing this idea is determining the parameters of the combination of views. This problem can be solved either in the space of feature matches among the views ("image space") or the space of parameters ("transformation space"). In general, both of these spaces are very large, making the search very time-consuming. In this paper, we propose using GAs to search these spaces efficiently. To improve the efficiency of genetic searching in the transformation space, we use singular value decomposition and interval arithmetic to restrict the genetic search to the most feasible regions of the transformation space. The effectiveness of the GA approaches is shown on a set of increasingly complex real scenes where exact and near-exact matches are found reliably and quickly.

Generating permutations satisfying a U-D sequence, or having a given number of runs are shown to be restricted permutation problems which can be reduced to topological sorting. Algorithms for generating the solution sets of such problems are outlined.

A new algorithm to generate all topological sorting arrangements is presented. It is compared with other such algorithms and is found to be more efficient.

Some experiences with the Johnson-Trotter permutation generation algorithm are reported. A version which generates strings containing permutations as subsequences and has the offset maintenance removed is proposed. This version is faster than any other known algorithm and has indexing properties which make it of practical use.

A subset of all the permutations of n marks arises naturally in interval exchange transformation problems. Each permutation p1p2 … pn in the set represents a transformation having a dense orbit, and has the properties pi+1≠pi+1 and p1p2 … pi is not a permutation of {1, 2, …, i} for all i, 1≤i≤n−1. A recursive formula is given for the cardinality of this subset, and two algorithms for generating in alphabetic order all the permutations in it are presented.

An efficient algorithm for generating and indexing all shapes of n-noded binary trees is described The algorithm is based on a correspondence between binary trees and the class of stack-sortable permutations.together with a representation of such permutatmns as ballot sequences Justification for the related procedures is given, and their efficiency estabhshed by comparison to other approaches

All the known methods for finding the GCD-greatest common divisor – of two polynomials are based on some variation of Euclid's algorithm, which uses repeated division of the successive divisors by the remainders. Two new algorithms for generating the remainder resulting from the pseudo-division of polynomials over a commutative ring are suggested. They are compared with respect to memory and CPU requirements. It is found that the new algorithms are more efficient, in particular, when the difference between the degrees of the polynomials is large, and/or the commutative ring is not that of the integers.

Computer acquisition is an activity which is often carried out improperly, and results in a poor selection. It is claimed that to make a good selection, one doesn't need to be specially trained. A compromise between the objective and subjective approaches is suggested. It is pointed out that benchmark design and mix design do not necessarily coincide. While numerous authors have stressed the mix design, little attention has been given to the operational design of the benchmark. It is claimed that for a good selection, the future operational mode as well as the projected load must be simulated in a benchmark. A case study involving four configurations of four major computer manufacturers, is presented at the end.