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

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

Reqsys-MDD: uma ferramenta para mapeamento entre modelos de features e requisitos em linhas de produto de software

The approach Software Product Line (SPL) has become very promising these days, since it allows the production of customized systems on large scale through product families. For the modeling of these families the Features Model is being widely used, however, it is a model that has low level of detail and not may be sufficient to guide the development team of LPS. Thus, it is recommended add the Features Model to other models representing the system from other perspectives. The goals model PL-AOVgraph can assume this role complementary to the Features Model, since it has a to context oriented language of LPS's, which allows the requirements modeling in detail and identification of crosscutting concerns that may arise as result of variability. In order to insert PL-AOVgraph in development of LPS's, this paper proposes a bi-directional mapping between PL-AOVgraph and Features Model, which will be automated by tool ReqSys-MDD. This tool uses the approach of Model-Driven Development (MDD), which allows the construction of systems from high level models through successive transformations. This enables the integration of ReqSys-MDD with other tools MDD that use their output models as input to other transformations. So it is possible keep consistency among the models involved, avoiding loss of informations on transitions between stages of development