A Visão Monocular e a Aposentadoria Especial da Pessoa com Deficiência ab 69.9 EURO Pessoa com deficiência visual
We present a visual servoing system for an amphibious legged robot. This is a monocular-vision based servoing mechanism that enables the robot to track and follow a target both underwater and on the ground. We use three different tracking algorithms to track and localize the target in the image, with color being the tracked feature. Tracking is performed based on the color of the target object, target color distribution and color distribution with a probabilistic kernel. Output from the tracker is channeled to a proportional-integral-derivative controller, which generates steering commands for the robot controller. The robot controller in turn takes the steering commands and generates motor commands for the six legs of the robot. A large class of significant applications can be addressed by allowing such a robot to follow a diver or some other moving target. The system has been evaluated in open-water environments and under natural lighting conditions, and has successfully performed tracking and following of a wide variety of target objects.
Visual perception relies on both selective andconstructive perceptual processes. For example,binocular rivalry leads to the selective perceptionof one of two competing monocular stimuli, whereasvisual phantom formation leads to perceptualfilling-in of a large gap between two collinearlyaligned gratings. This book explores the role ofperceptual and attentional mechanisms in binocularrivalry and perceptual filling-in, and investigatesthe neural interactions between rivalry andfilling-in to gain new insights into the nature ofthese perceptual phenomena. These studies providecompelling new evidence suggesting that the neuralmechanisms underlying selective perception andconstructive perception both operate at early stagesof visual processing, and that dynamic interactionscan take place between these mechanisms at these sameearly sites. Moreover, the mechanistic approach,which this book takes to study visual awareness, ismore promising to help us understand howconsciousness arises as a consequence of brainactivity than merely searching for the neuralcorrelates of consciousness.
This manuscript addresses the problem of obstacle avoidance for semi- and autonomous terrestrial platforms in dynamic and unknown environments. Based on monocular vision, it proposes a set of tools that continuously monitors the way forward, proving appropriate road information in real time. Taking into account the temporal coherence between consecutive frames, a new Dynamic Power Management methodology is proposed and applied to a robotic visual machine perception, which included a new environment observer method to optimize energy consumption used by a visual machine. A remarkable characteristic of these methodologies is its independence of the image acquiring system and of the robot itself. This real-time perception system has been evaluated from different test-banks and also from real data obtained by two intelligent platforms. In semi-autonomous tasks, tests were conducted at speeds above 100 Km/h. Autonomous displacements were also carried out successfully.
Visual scene understanding is one of the ultimate goals in computer vision and has been in the field's focus since its early beginning. Despite continuous effort over several years, applications such as autonomous driving and robotics are still subject to active research. In recent years, improved probabilistic methods became a popular tool for state-of-the-art computer vision algorithms. Additionally, high resolution digital imaging devices and increased computational power became available. By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection, tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments. This book improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety. It develops and improves approaches for object class detection and semantic scene labeling and integrates those into models for global scene reasoning which exploit context at different levels.
High Quality Content by WIKIPEDIA articles! Stereopsis (from stereo meaning solidity, and opsis meaning vision or sight) is the process in visual perception leading to the sensation of depth from the two slightly different projections of the world onto the retinas of the two eyes. The differences in the two retinal images are called horizontal disparity, retinal disparity, or binocular disparity. The differences arise from the eyes' different positions in the head. Stereopsis is commonly referred to as depth perception. This is inaccurate, as depth perception relies on many more monocular cues than stereoptical ones, and individuals with only one functional eye still have full depth perception except in artificial cases (such as stereoscopic images) where only binocular cues are present.
High Quality Content by WIKIPEDIA articles! The visual system is the part of the central nervous system which enables organisms to see. It interprets the information from visible light to build a representation of the world surrounding the body. The visual system accomplishes a number of complex tasks, including the reception of light and the formation of monocular representations, the construction of a binocular perception from a pair of two dimensional projections, the identification and categorization of visual objects, assessing distances to and between objects, and guiding body movements to visual objects. The psychological manifestation of visual information is known as visual perception.