Visual SensorsOscar Reinoso, Luis Payá Visual sensors are able to capture a large quantity of information from the environment around them. A wide variety of visual systems can be found, from the classical monocular systems to omnidirectional, RGB-D, and more sophisticated 3D systems. Every configuration presents some specific characteristics that make them useful for solving different problems. Their range of applications is wide and varied, including robotics, industry, agriculture, quality control, visual inspection, surveillance, autonomous driving, and navigation aid systems. In this book, several problems that employ visual sensors are presented. Among them, we highlight visual SLAM, image retrieval, manipulation, calibration, object recognition, navigation, etc. |
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Common terms and phrases
accuracy achieved adaptive aircraft algorithm angle applications approach average background calculated calibration camera classification CLOSIB color combination compared Computer Vision Conference on Computer considered coordinate corresponding CrossRef database dataset depth descriptors detection direction distance distribution Equation error estimation evaluation experiments extracted Figure filter follows frame function fusion histogram IEEE improved International Conference iris joint layer learning light line segment localization matching matrix mean measure method motion object observed obtained optimization orientation parallel parameters Pattern Pattern Recognition performance pixels planes point cloud pose pose estimation position prediction presented probability Proceedings proposed proposed method recognition reference region represents respectively retrieval RGB-D robot robust rotation salient scale scene semantic Sensors sequence shown in Figure shows SLAM stereo structure Table texture tracking trajectory transformation vector visual visual odometry