Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities ill images. This estimation is crucial for the problem of crowd monitoring. and control. The assessment is carried out oil a set of nearly 300 real images captured from Liverpool Street Train Station. London, UK using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight lille segments. Fourier analysis. and fractal dimensions. The estimations of dowel densities are given in terms of the classification of the input images ill five classes of densities (very low, low. moderate. high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model). Bayesian. and an approach based on fitting functions. The results obtained by these three classifiers. using the four texture measures. allowed the conclusion that, for the problem of crowd density estimation. texture analysis is very effective.

Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: Pattern-recognition analyses for screening quality control purposes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

GC Fingerprints Coupled to Pattern-Recognition Multivariate SIMCA Chemometric Analysis for Brazilian Gasoline Quality Studies

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

(1)H NMR Fingerprinting of Brazilian Commercial Gasoline: Pattern-Recognition Analyses for Origin Authentication Purposes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Recognition and distribution of Leptodactylus mystaceus (Anura; Leptodactylidae) in the State of São Paulo, Southeastern Brazil

Leptodactylus mystaceus, uma espécie com ampla distribuição geográfica pela América do Sul, é diagnosticada com base em exemplares do Estado de São Paulo, seu limite meridional de distribuição geográfica. Apresentamos aqui o primeiro registro da espécie para o Sudeste do Brasil, ampliando sua distribuição conhecida em cerca de 1.300 km ao sudeste. Também incluímos a descrição da vocalização de anúncio, informações sobre história natural, fotografia em vida e desenhos de caracteres morfológicos que auxiliam na identificação desta espécie.

Recognition of altered segments in Brazilian Sign Language

The purpose of this study was to verify discriminative control by segments of signs in adolescents with deafness who use Brazilian Sign Language (BSL). Four adolescent with bilateral deafness, with 3 years of BSL teaching, saw a video presenting a children's tale in BSL. After showing accurate understanding of the story, participants saw another video of the same story with 12 signs altered in one of their segments (hand configuration, place of articulation, or movement). They apparently did not detect the alterations. However, when the signs were presented in isolation in a matching-to-sample test, they virtually always selected the picture corresponding to the unaltered signs. Three participants selected an unfamiliar picture in 50% or more trials with an altered sign as a sample, showing that they could detect the majority of the altered signs.

Chemical communication, aggression, and conspecific recognition in the fish Nile tilapia

The chemical modulation of agonistic behavior and conspecific recognition were tested in juveniles of the fish Nile tilapia, Oreochromis niloticus (L.). After a 7-day isolation period, the fish were grouped (four individuals per aquarium) for 7 days. Then fish of alpha and beta ranks (previously matched for similar size) were paired in a neutral territory for analysis of their agonistic interaction. Pairs composed of alpha and beta fish were established with either fish from the same group (familiar) or from two different groups (unfamiliar). The pairs were tested in contiguous compartments, either with water exchange between the compartments or in the absence of water exchange. In each condition the fish were separated by a transparent glass partition. Twelve pairs were tested in each experimental condition. Fish behavior was videotaped and the following variables were analyzed: (a) frequency of and time spent in agonistic patterns, (b) latency to start fighting, and (c) duration of swimming. Water exchange between compartments decreased agonistic interactions. This effect, however, was more pronounced in pairs of fish coming from the same group (in this case, subordinate fish spent less time in confrontations than dominant ones). We conclude that chemical communication decreases aggression in this species by (1) inducing an alarm reaction and (2) increasing conspecific recognition (thus stabilizing the dominance hierarchy). (C) 1997 Elsevier B.V.

NATURAL PRODUCT SUBSTITUTION PATTERN AND SKELETAL RECOGNITION METHOD

This work describes a methodology for identification of skeletal types of diterpenes based on data base with 1500 compounds isolated from Asteraceae. One program named BOTOCSYS was built with the codification of the compounds and their botanical sources. An example of identification of a new substance is given.

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.