An object-based visual selection model combining physical features and memory

In this paper, a new visual selection model is proposed, which combines both early visual features and object-based visual selection modulations. This model integrates three main mechanisms. The first is responsible for the segmentation of the scene allowing the identification of objects. In the second one, the average of saliency of each object is calculated for each feature considered in this work, which provides the modulation of the visual attention for one or more features. Finally, the third mechanism is responsible for building the object-saliency map, which highlights the salient objects in the scene. It will be shown that top-down modulation can overcome bottom-up saliency by selecting a known object instead of the most salient (bottom-up) and is even clear in the absence of any bottom-up clue. Several experiments with synthetic and real images are conducted and the obtained results demonstrate the effectiveness of the proposed approach for visual attention

A visual attention approach for the tracking of vehicles through UAV

Thermal imaging analysis is an important tool in the study of wildlife animals. The segmentation of thermal images has not extensively explored by the Ecology and Biology communities. In this paper we propose a new approach for segmenting thermal images using the SLIC superpixel algorithm and connected component labeling. Experiments were performed on images taken over the behaviour activity of four mammal species. The results show that our approach has a great potential for partioning animals and background.FAPESP (processo 2012/14725-4)CNP

Inserção de um robô humanoide no ensino de objetos geométricos 2D sobrepostos

Robotics, introduced into educational environments, can be an interesting alternative to explore theoretical concepts covered in class, faciliting learning and engaging the student’s interest.In this paper, a computational system capable of detect objects was incorporated into the robot NAO, so that can interact with students, recognizing geometric shapes with overlap. The system consists of two models of neural networks and was evaluated through a sequence of didatic activities presented to students of the 5th year, aiming to encourage them. The robot operates autonomously, recognizing and counting the diferente objects in the image. The results show that the children felt very motivated and engaged to fulfill the tasks.A robótica, inserida em ambientes educacionais, é uma alternativa interessante para explorar conceitos teóricos abordados em sala de aula, facilitando o aprendizado e cativando o interesse dos alunos. Neste artigo, um sistema computacional capaz de detectar objetos foi incorporado ao robô NAO para que o mesmo possa interagir com alunos, reconhecendo figuras geométricas com sobreposição. O sistema é constituído por dois modelos de Redes Neurais e foi avaliado por meio de uma sequência de atividades didáticas apresentadas a alunos do 5o ano, visando estimulá-los. O robô atua autônomamente, reconhecendo e contando os diferentes objetos na imagem. Os resultados apresentados mostram que as crianças se sentiram muito motivadas para cumprir as tarefas

Attention based object recognition applied to a humanoid robot

Analysis and recognition of objects in complex scenes is a demanding task for a computer. There is a selection mechanism, named visual attention, that optimizes the visual system, in which only the important parts of the scene are considered at a time. In this work, an object-based visual attention model with both bottom-up and top-down modulation is applied to the humanoid robot NAO to allow a new attention procedure to the robot. This means that the robot, by using its cameras, can recognize geometric figures even with the competition for the attention of all the objects in the image in real time. The proposed method is validated through some tests with 13 to 14 year old kids interacting with the robot NAO that provides some tips (such as the perimeter and area calculation formulas) and recognizes the figure showed by these children. The results are very promissor and show that the proposed approach can contribute for inserting robotics in the educacional context.São Paulo State Research Foundation (FAPESP)Brazilian National Research Council (CNPq

Incorporating a humanoid robot to motivate the geometric figures learning

Technology has been introduced into educational environments to facilitate learning and engage the students interest. Robotics can be an interesting alternative to explore theoretical concepts covered in class. In this paper, a computational system capable of detecting objects was incorporated into the robot NAO, so it can Interact with students, recognizing geometric shapes with overlap. The system consists of two models of neural networks and was evaluated through a sequence of didatic activities presented to students of the 5th year, aiming to encourage them to perform the tasks. The robot operates autonomously, recognizing and counting the diferente objects in the image. The results show that the children felt very motivated and engaged to fulfill the tasks.São Paulo State Research Foundation (FAPESP)Brazilian National Research Council (CNPq

Inserção de um robô humanoide no Ensino de Objetos Geométricos 2D sobrepostos

Robotics, introduced into educational environments, can be an interesting alternative to explore theoretical concepts covered in class, faciliting learning and engaging the student’s interest.In this paper, a computational system capable of detect objects was incorporated into the robot NAO, so that can interact with students, recognizing geometric shapes with overlap. The system consists of two models of neural networks and was evaluated through a sequence of didatic activities presented to students of the 5th year, aiming to encourage them. The robot operates autonomously, recognizing and counting the diferente objects in the image. The results show that the children felt very motivated and engaged to fulfill the tasks.A robótica, inserida em ambientes educacionais, é uma alternativa interessante para explorar conceitos teóricos abordados em sala de aula, facilitando o aprendizado e cativando o interesse dos alunos. Neste artigo, um sistema computacional capaz de detectar objetos foi incorporado ao robô NAO para que o mesmo possa interagir com alunos, reconhecendo figuras geométricas com sobreposição. O sistema é constituído por dois modelos de Redes Neurais e foi avaliado por meio de uma sequência de atividades didáticas apresentadas a alunos do 5o ano, visando estimulá-los. O robô atua autônomamente, reconhecendo e contando os diferentes objetos na imagem. Os resultados apresentados mostram que as crianças se sentiram muito motivadas para cumprir as tarefas