Such capacities will call upon both integrated multimode sensory perception – e. G. To orient to important tumuli – and cognitive skills – e. G. , to learn and memorize a given situation or event, or to plan a trajectory, with obstacles avoidance by optical flow: The VA tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. Moreover, when heading into a frontal obstacle, the understanding is able to generate a tight U-turn that ensures the Java survival.
Key words: optic flow, obstacle-avoidance, GAP, In its current stage of development, the biometric design of GAP chip produced by BEE affords it the unique feature of exhibiting our of the main adaptive capacities that characterize natural vision. Real-time information processing: through circuits dedicated to movement, speed, color, hue and luminance, oriented edges, corners, the system monitors visual inputs on-line, thus Page 1 making quick reactions and efficient predictions possible. Successive images are not recorded but processed on-line according to previous-step anticipations.
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Detection of objects or events through temporal coincidences and unsupervised collective decisions: to detect a landmark or an object within an ever-changing flow of images entails being able of unsurprising sets of coherent data within such flow. Interesting structures within an image are characterized by temporal coincidences within different circuits (e. G. , the simultaneous occurrence of colored and moving zones). Through successive images, different modules of the chip take collective decisions about these structures, notably about their positions or movements.
Object pursuit and anticipation: the detection of an object within an image is difficult because of permanent changes caused by displacements or light variations. The activation of the different adapted to changes in the appearance of an object. Moreover, these modules cooperate to anticipate the content of future images and to track in parallel several moving objects – thanks to dedicated circuits, like those that detect movements. Learning of visual characteristics: adaptive and real-time tracking of coherent information sets makes it possible to characterize and record the intrinsic properties of given objects.
Later on, these objects may be retrieved or recognized, even in case of temporary disappearance from one or several successive images. 2. Why GAP? 3. GAP model To perform a vision application, it is necessary to use a generic technology to receive, understand and act in real time. Instead of the understanding power concentration of Imaging Processing (P) we prefer to vocalism on the computation on perception; the more you perceive more the understanding is easy. Even today, we solve the application with a small Generic Visual Perception Processor when the IP needs a strong Digital Signal Processor.
The global GAP size, power, and time developments are effectively lower with a better result From Buses and Imbiber  physiological description, we have developed an electronic concept using: sequences, the sample rate determine the dynamic, and each sequence is sampled in sub-sequences as rows and columns in visual perception. The sequences describe the temporal information and the sub-sequences the spatial information. Two main electronic blocks: Temporal Domain Computation associated to Spatial Domain Computation extract features. A duality between a low physiological neuron speed function including a huge synaptic connections and a high speed silicon electronic computer with only one multiplexing connection. These electronic circuits are directly inspired by the properties of the human visual system including the retina, the low and high-level visual cortical areas. The basic principle of these circuits is to cumulate within the same chip the set of adaptive properties existing in the human visual system.
This includes adapting the sensitivity to the background light, tracking relative movements of objects or people, anticipating their movements, and learning to detect objects or people, whatever their position, size, orientation, and whatever their backgrounds. The processing principles in such electronic circuits are inspired from processing principles observed in populations of retinal and cortical neurons. They do not aim at a complete neuronal implementation, which s very costly in electronic circuits, but rather capture simply as possible the adaptive properties of the neural processes.