HOPS

Biological vision processes are usually characterized by the following different phases: • Awareness: natural or artificial agents operating in dynamic environments can benefit from a, possibly rough, global description of the surroundings. In human this is referred to as peripheral vision, since it derives from stimuli coming from the edge of the retina. • Attention: once an interesting object/event has been detected, higher resolution is required to set focus on it and plan an appropriate reaction. In human this corresponds to the so-called foveal vision, since it originates from the center of the retina (fovea). • Analysis: extraction of detailed information about objects of interest, their three-dimensional structure and their spatial relationships completes the vision process. Achievement of these goals requires at least two views of the surrounding scene with known geometrical relations. In humans, this function is performed exploiting binocular (stereo) vision. Computer Vision has often tried to emulate natural systems or, at least, to take inspiration from them. In fact, different levels of resolution are useful also in machine vision. In the last decade a number of studies dealing with multiple cameras at different resolutions have appeared in literature. Furthermore, the ever-growing computer performances and the ever-decreasing cost of video equipment make it possible to develop systems which rely mostly, or even exclusively, on vision for navigating and reacting to environmental changes in real time. Moreover, using vision as the unique sensory input makes artificial perception closer to human perception, unlike systems relying on other kinds of sensors and allows for the development of more direct biologically-inspired approaches to interaction with the external environment (Trullier 1997). This article presents HOPS (Hybrid Omnidirectional Pin-hole Sensor), a class of dual camera vision sensors that try to exalt the connection between machine vision and biological vision.