Uma arquitetura distribuída de hardware e software para controle de um robô móvel autônomo

In this work, we present a hardware-software architecture for controlling the autonomous mobile robot Kapeck. The hardware of the robot is composed of a set of sensors and actuators organized in a CAN bus. Two embedded computers and eigth microcontroller based boards are used in the system. One of the computers hosts the vision system, due to the significant processing needs of this kind of system. The other computer is used to coordinate and access the CAN bus and to accomplish the other activities of the robot. The microcontroller-based boards are used with the sensors and actuators. The robot has this distributed configuration in order to exhibit a good real-time behavior, where the response time and the temporal predictability of the system is important. We adopted the hybrid deliberative-reactive paradigm in the proposed architecture to conciliate the reactive behavior of the sensors-actuators net and the deliberative activities required to accomplish more complex tasks

Seleção de features guiada por atenção visual em imagens com fóvea

Visual attention is a very important task in autonomous robotics, but, because of its complexity, the processing time required is significant. We propose an architecture for feature selection using foveated images that is guided by visual attention tasks and that reduces the processing time required to perform these tasks. Our system can be applied in bottom-up or top-down visual attention. The foveated model determines which scales are to be used on the feature extraction algorithm. The system is able to discard features that are not extremely necessary for the tasks, thus, reducing the processing time. If the fovea is correctly placed, then it is possible to reduce the processing time without compromising the quality of the tasks outputs. The distance of the fovea from the object is also analyzed. If the visual system loses the tracking in top-down attention, basic strategies of fovea placement can be applied. Experiments have shown that it is possible to reduce up to 60% the processing time with this approach. To validate the method, we tested it with the feature algorithm known as Speeded Up Robust Features (SURF), one of the most efficient approaches for feature extraction. With the proposed architecture, we can accomplish real time requirements of robotics vision, mainly to be applied in autonomous robotics

Odometria visual baseada em técnicas de structure from motion

Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform

Identificação Visual de Caixas de Medicamentos Usando Features Correspondentes

This work uses computer vision algorithms related to features in the identification of medicine boxes for the visually impaired. The system is for people who have a disease that compromises his vision, hindering the identification of the correct medicine to be ingested. We use the camera, available in several popular devices such as computers, televisions and phones, to identify the box of the correct medicine and audio through the image, showing the poor information about the medication, such: as the dosage, indication and contraindications of the medication. We utilize a model of object detection using algorithms to identify the features in the boxes of drugs and playing the audio at the time of detection of feauteres in those boxes. Experiments carried out with 15 people show that where 93 % think that the system is useful and very helpful in identifying drugs for boxes. So, it is necessary to make use of this technology to help several people with visual impairments to take the right medicine, at the time indicated in advance by the physician

Acelerando algoritmos de baixo nível em visão robótica com hardware reconfigurável

A challenge that remains in the robotics field is how to make a robot to react in real time to visual stimulus. Traditional computer vision algorithms used to overcome this problem are still very expensive taking too long when using common computer processors. Very simple algorithms like image filtering or even mathematical morphology operations may take too long. Researchers have implemented image processing algorithms in high parallelism hardware devices in order to cut down the time spent in the algorithms processing, with good results. By using hardware implemented image processing techniques and a platform oriented system that uses the Nios II Processor we propose an approach that uses the hardware processing and event based programming to simplify the vision based systems while at the same time accelerating some parts of the used algorithms

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

Some experiments with high order compact methods using a computer algebra software - Part I

We consider a procedure for obtaining a compact fourth order method to the steady 2D Navier-Stokes equations in the streamfunction formulation using the computer algebra system Maple. The resulting code is short and from it we obtain the Fortran program for the method. To test the procedure we have solved many cavity-type problems which include one with an analytical solution and the results are compared with results obtained by second order central differences to moderate Reynolds numbers. (c) 2005 Elsevier B.V. All rights reserved.

Some experiments with high order compact methods using a computer algebra software - Part II (non-uniform grid)

We generalize a procedure proposed by Mancera and Hunt [P.F.A. Mancera, R. Hunt, Some experiments with high order compact methods using a computer algebra software-Part 1, Appl. Math. Comput., in press, doi: 10.1016/j.amc.2005.05.015] for obtaining a compact fourth-order method to the steady 2D Navier-Stokes equations in the streamfunction formulation-vorticity using the computer algebra system Maple, which includes conformal mappings and non-uniform grids. To analyse the procedure we have solved a constricted stepped channel problem, where a fine grid is placed near the re-entrant corner by transformation of the independent variables. (c) 2006 Elsevier B.V. All rights reserved.

Computer-assisted analysis of cell proliferation markers in oral lesions

Abnormalities in any component of the cell cycle regulatory machine may result in oral. cancer, and markers of cell proliferation have been used to determine the prognosis of tumor progression. The aim of this study was to determine whether silver-stained nucleolar organizer region (AgNOR) and Ki-67 measurements could improve the assessment of growth rates in oral lesions. Eighty-three oral biopsies were studied, 20 of which were classified as fibrous inflammatory hyperplasia (FIH), 40 as leukoplakia (LKP) and 23 as oral. squamous cell carcinoma (OSCC). Within the LKP group, 22 out of 29 biopsies were diagnosed as non-dysplastic leukoplakia (LK) and 18 as dysplastic teukoptakia (DLK), presenting discrete, moderate and severe dysplasia. Ki-67 immunotabeting of the lesions increased steadily in the following order: FIH, DLK, LK and OSCC, indicating that Ki-67 is a good marker for predicting the protiferative fraction among benign, premalignant and malignant oral lesions. The median values of AgNOR parameters indicate that the morphometric index gives better results regarding the proliferative rate than the numerical one. A series of linear regressions between AgNOR parameters and Ki-67 showed positive associations. We conclude that a combination of Ki-67 and morphometric AgNOR analyses could be used as an aid in the determination of the protiferative status of oral epithelial. cells in oral cancer. (C) 2007 Elsevier GmbH. All rights reserved.

Galectin-3 Overexpression in Invasive Laryngeal Carcinoma, Assessed by Computer-assisted Analysis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis of the distances covered by first division Brazilian soccer players obtained with an automatic tracking method

Methods based on visual estimation still is the most widely used analysis of the distances that is covered by soccer players during matches, and most description available in the literature were obtained using such an approach. Recently, systems based on computer vision techniques have appeared and the very first results are available for comparisons. The aim of the present study was to analyse the distances covered by Brazilian soccer players and compare the results to the European players', both data measured by automatic tracking system. Four regular Brazilian First Division Championship matches between different teams were filmed. Applying a previously developed automatic tracking system (DVideo, Campinas, Brazil), the results of 55 outline players participated in the whole game (n = 55) are presented. The results of mean distances covered, standard deviations (s) and coefficient of variation (cv) after 90 minutes were 10,012 m, s = 1,024 m and cv = 10.2%, respectively. The results of three-way ANOVA according to playing positions, showed that the distances covered by external defender (10642 ± 663 m), central midfielders (10476 ± 702 m) and external midfielders (10598 ± 890 m) were greater than forwards (9612 ± 772 m) and forwards covered greater distances than central defenders (9029 ± 860 m). The greater distances were covered in standing, walking, or jogging, 5537 ± 263 m, followed by moderate-speed running, 1731 ± 399 m; low speed running, 1615 ± 351 m; high-speed running, 691 ± 190 m and sprinting, 437 ± 171 m. Mean distance covered in the first half was 5,173 m (s = 394 m, cv = 7.6%) highly significant greater (p < 0.001) than the mean value 4,808 m (s = 375 m, cv = 7.8%) in the second half. A minute-by-minute analysis revealed that after eight minutes of the second half, player performance has already decreased and this reduction is maintained throughout the second half. ©Journal of Sports Science and Medicine (2007).

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Estudo de métodos para classificação e localização precisa de padrões usando um sistema de luz estruturada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Machine vision to identify broiler breeder behavior

Animal behavioral parameters can be used to assess welfare status in commercial broiler breeders. Behavioral parameters can be monitored with a variety of sensing devices, for instance, the use of video cameras allows comprehensive assessment of animal behavioral expressions. Nevertheless, the development of efficient methods and algorithms to continuously identify and differentiate animal behavior patterns is needed. The objective this study was to provide a methodology to identify hen white broiler breeder behavior using combined techniques of image processing and computer vision. These techniques were applied to differentiate body shapes from a sequence of frames as the birds expressed their behaviors. The method was comprised of four stages: (1) identification of body positions and their relationship with typical behaviors. For this stage, the number of frames required to identify each behavior was determined; (2) collection of image samples, with the isolation of the birds that expressed a behavior of interest; (3) image processing and analysis using a filter developed to separate white birds from the dark background; and finally (4) construction and validation of a behavioral classification tree, using the software tool Weka (model 148). The constructed tree was structured in 8 levels and 27 leaves, and it was validated using two modes: the set training mode with an overall rate of success of 96.7%, and the cross validation mode with an overall rate of success of 70.3%. The results presented here confirmed the feasibility of the method developed to identify white broiler breeder behavior for a particular group of study. Nevertheless, more improvements in the method can be made in order to increase the validation overall rate of success. (C) 2013 Elsevier B.V. All rights reserved.