Leaf vein shape description and discrimination using Hermite cubic polynomial representation

This paper describes a method for leaf vein shape characterization using Hermite polynomial cubic representation. The elements associated with this representation are used as secondary vein descriptors and their discriminatory potential are analyzed based on the identification of two legume species (Lonchocarpus muehlbergianus Hassl. and L. subglaucescens Mart, ex Benth.). The elements of Hermite geometry influence a curve along all its extension allowing a global description of the secondary vein course by a descriptor of low dimensionality. The obtained results shown the analyzed species can be discriminated by this method and it can be used in addition to commonly considered elements in the taxonomic process.Este artigo descreve um método para caracterização de nervuras utilizando a representação polinomial cúbica de Hermite. Os elementos associados a esta representação são usados como descritores das nervuras e o potencial discriminatório é analisado com base na identificação de duas espécies de leguminosas (Lonchocarpus muehlbergianus Hassl. e L. subglaucescens Mart, ex Benth.). Os vetores de geometria de Hermite exercem influência sobre toda a extensão da curva modelada, permitindo a descrição global da nervura por um descritor de baixa dimensionalidade. Os resultados obtidos mostram que o método foi eficaz na separação das espécies analisadas e apresenta-se como uma opção adicional aos métodos para caracterização de nervuras usualmente utilizados em taxonomia vegetal.16518

Prediction of binding hot spot residues by using structural and evolutionary parameters

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set

Estudo do desempenho de metodos sequenciais de filtragem não linear usando aproximações iteradas de primeira ordem

Orientador: Manuel de Jesus MendesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de CampinasResumo: Não informado.Abstract: Not informed.MestradoMestre em Automaçã

Line based camera calibration in machine vision dynamic applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface

LINE BASED CAMERA CALIBRATION IN MACHINE VISION DYNAMIC APPLICATIONS

Abstract: The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.

A recursive approach to space resection using straight lines

An approach using straight lines as features to solve the photogrammetric space resection problem is presented. An explicit mathematical model relating straight lines, in both object and image space, is used. Based on this model, Kalman Filtering is applied to solve the space resection problem. The recursive property of the filter is used in an iterative process which uses the sequentially estimated camera location parameters to feedback to the feature extraction process in the image. This feedback process leads to a gradual reduction of the image space for feature searching, and consequently eliminates the bottleneck due to the high computational cost of the image segmentation phase. It also enables feature extraction and the determination of feature correspondence in image and object space in an automatic way, i.e., without operator interference. Results obtained from simulated and real data show that highly accurate space resection parameters are obtained as well as a progressive processing time reduction. The obtained accuracy, the automatic correspondence process, and the short related processing time show that the proposed approach can be used in many real-time machine vision systems, making possible the implementation of applications not feasible until now

Sistema de Vigilância Inteligente para Estacionamento de Veículos

This paper presents a semi-supervised intelligent surveillance systemthat detects anomalies in a parking lot. The proposed methodology extractsspeed characteristics and change of direction from the trajectories to representand classify the behavior of objects. The training phase aims to createa model of the objects normal behavior from a set of manually labeled trajectories.Then, the classification of the behavior is performed through similaritymeasure between the extracted features and the model. The results showed thatthe proposed system presented fast response and good accuracy in the anomalydetection