An integrated circuit that computes the velocity vector of a visual stimulus in one dimension is presented. The circuit combines optical sensors and associated electronics on a single silicon chip, processed with standard CMOS technology. The velocity is inferred from the time delay of the appearance of an image feature at two fixed locations on the chip. The circuit operates quite robustly for high-contrast stimuli over considerable irradiance and velocity ranges. With lower-contrast stimuli the output signal for a given velocity tends to decrease, while the direction selectivity is still maintained. The individual motion-sensing cells are compact, and they are therefore suited for use in dense 1D or 2D imaging arrays.

\Gamma\GammaAn integrated circuit with on-chip photoreceptors is described, that computes the bi-directional velocity of a visual stimulus moving along a given axis in the focal plane by measuring the time delay of its detection at two positions. Due to the compactness of the circuit, a dense array of such motion-sensing elements can be monolithically integrated to estimate the velocity field of an image and to extract higher-level image features through local or global interaction. Index Terms\Gamma\Gammamotion estimation, velocity sensor, optical flow, analog VLSI, robot vision. Compact Integrated Motion Sensor with Three-Pixel Interaction 3 I. Introduction A variety of image-processing tasks, such as segmentation and estimation of depth, can be considerably simplified in dynamic scenes if motion data is available. Furthermore, mobile systems rely on motion information for the computation of important parameters of ego-motion, such as time to contact and focus of expansion. Since...

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Although artificial and biological systems face similar sensorimotor control problems, until today only a few attempts have been made to implement specific biological control structures on robots. Nevertheless, the process of designing the sensorimotor control of a robot can contribute to our understanding of these mechanisms and can provide the basis of a critical evaluation of existing biological models. Flies have developed a specialized visuomotor control for tasks such as course stabilization, fixation and approach towards stationary objects, tracking of moving objects and landing, which are based on the analysis of visual motion information. Theoretical and experimental results suggest that in flies the visuomotor control for course stabilization as well as fixation and approach towards stationary objects may be implemented at least partially by one common sensory circuit. We present agents with a visuomotor controller that regulates the two behaviors of course stabilization and object fixation. To test this controller under real world conditions, we implemented it on a miniature robot. We have been able to show that in addition to course stabilization and object fixation, the robot also approaches stationary

Flies orientate themselves quickly in an unstructured environment through motion information computed from their low-resolution compound eyes. The fly visual system is an example of a robust motion system that works in a natural environment. In this paper, we describe a low-power analog very large scale integration chip that models motion computation in the fly. The architecture of this motion chip closely follows the anatomical layout of of the fly visual layers. The output of the chip corresponds to the responses of the wide-field direction-selective cells in the final layer of the visual system. The silicon chip has a one-dimensional array of 37 elementary motion detectors (EMDs) each of which provides local motion information. The EMD outputs are aggregated in a nonlinear way to produce a motion output that is independent of the stimulus size and contrast. We employed various circuit techniques to ensure robust motion computation in each processing stage. Results from the circuit fabricated in a 1.2- m CMOS technology are compared with the responses of the direction-selective cells.

We present three di#erent architectures that make use of analog VLSI velocity sensors for detecting the focus of expansion, time to contact and motion discontinuities respectively. For each of the architectures proposed we describe the functionality of their component modules and their principles of operation. Data measurements obtainedfrom the VLSI chips developed demonstrate their correct performance and their limits of operation. 1: Introduction Analog velocity sensor circuits have been thoroughly investigated in the past years #19,2,1,7,4,17,5#. Nonetheless, researchers were unable to obtain a device that would simultaneously be compact, robust to background brightness level, insensitivetostimulus contrast and have a wide, unambiguous, range of speed selectivity. Recently novel velocity sensors that are sensitive to low contrast stimuli, independentofcontrast #for intermediate and high contrast values#, selectivetoover 3 orders of magnitude of velocityandover 2 orders of magnitu...

We introduce a biologically inspired computational architecture for small-field detection and wide-field spatial integration of visual motion based on the general organizing principles of visual motion processing common to organisms from insects to primates. This highly parallel architecture begins with two-dimensional (2-D) image transduction and signal conditioning, performs small-field motion detection with a number of parallel motion arrays, and then spatially integrates the small-field motion units to synthesize units sensitive to complex wide-field patterns of visual motion. We present a theoretical analysis demonstrating the architecture’s potential in discrimination of wide-field motion patterns such as those which might be generated by self-motion. A custom VLSI hardware implementation of this architecture is also described, incorporating both analog and digital circuitry. The individual custom VLSI elements are analyzed and characterized, and system-level test results demonstrate the ability of the system to selectively respond to certain motion patterns, such as those that might be encountered in self-motion, at the exclusion of others.

Optical flow estimation is a critical mechanism for autonomous mobile robots as it provides a range of useful information. As real-time processing is mandatory in this case, an efficient solution is the use of specific VLSI analog circuits. This paper presents a simple and regular architecture based on analog circuits which implements the entire processing line from photoreceptor to accurate and reliable optical flow estimation. The algorithm we propose, is an energy-based method using a novel wideband velocitytuned filter whichproves to be an efficient alternativetothe well known Gabor filters. Our approach shows that a high level of accuracy can be obtained from a small number of loosely tuned filters. It exhibits similar or improved performance to that of other existing algorithms, but with a much lower complexity.

We present two compact CMOS integrated circuits for computing the 2D local direction of motion of an image focused directly onto the chip. These circuits incorporate onboard photoreceptors and focal plane motion processing. With fully functional 14 x 13 and 12 x 13 implementations consuming less than 50 W per pixel, we conclude that practical pixel resolutions of at least 64 x 64 are easily achievable. Measurements characterizing the elementary 1D motion detectors are presented along with a discussion of 2D performance and example 2D motion vector fields. As an example application of the sensor, it is shown that the array as fabricated can directly compute the focus of expansion of a 2D motion vector field.

Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land safely. Mobile robots are ideal candidates for using this sensory modality to enhance their performance, but so far have been limited by the computational expense of processing video in realtime. Also, the complex structure of natural visual scenes poses an algorithmic challenge for extracting useful information in a robust manner. We address both issues by creating a small, low-power visual sensor with integrated analog parallel processing to extract motion in real-time. Because our architecture is based on biological motion detectors, we gain the advantages of this highly evolved system: A design that contains an implicit model of the statistical structure of natural dynamic scenes. We show that this sensor is suitable for use in the real world, and suggest robotic applic...