A panoramic imaging system, for monitoring a space, comprises a single camera and a dome-like convex mirror. The camera is mounted relative to the mirror so that at least most of the surface of the mirror is within the field of view of the camera. The mirror has an elevation gain, ଱, greater than 3 and a profile which (a) ensures that radiation from at least most of the space is reflected by the mirror onto the image plane of the camera, and (b) satisfied the relationship, in polar co-ordinates,Sin›A+0.5(1+଱)θ!=C.r.›-0.5(1+଱)!where C is a constant and A specifies the inclination of the profile to the optical axis of the camera at the point on the mirror which is closest to the aperture lens of the camera. The camera may be an electronic camera having a charge coupled device (CCD) sensor at its image plane, and the processed image of the space produced by the camera may be displayed on a monitor screen as a dewarped image. A motion sensing algorithm may be included in the image processing system of the electronic camera to generate an alarm when motion is sensed in the space.

The neural architecture, neurophysiology and behavioral abilities of insect vision are described, and compared with that of mammals. Insects have a hardwired neural architecture of highly differentiated neurons, quite different from the cerebral cortex, yet their behavioral abilities are in important respects similar to those of mammals. These observations challenge the view that the key to the power of biological neural computation is distributed processing by a plastic, highly interconnected, network of individually undifferentiated and unreliable neurons that has been a dominant picture of biological computation since Pitts and McCulloch's seminal work in the 1940's.

A multi-purpose hardware system for processing images at video rates is described. Image sequence hardware for temporal analysis in realtime (ISHTAR) uses 18 TI TMS320c40 (c40) DSPs to process input from a CCD camera or VCR source. The hardware architecture consists of a pipeline of nine processor boards, each with two c40 processors, the whole system being synchronized by the vertical sync of the input device. This enables the calculation of a number of two dimensional convolutions to be achieved at video frame rates with a delay between the input and the output dictated by the length of the pipeline. The system is fully reconfigurable in software and partially reconfigurable in hardware so that many different types of image processing algorithms can be implemented. The specific application of a generalized gradient model to measure image motion is described, outlining the particular program structure dictated by the hardware design. The SUN 4 host has access to each processor and has the ability to change parameters and program control while the system is running. In this way active control feedback loops can be employed, particularly when the motion of the camera is under the host control, forming an active vision system. Simulations using real image sequences are presented.

Adopting principles learnt from insect vision we have constructed model of a general-purpose front-end visual system for motion detection that is designed to operate in parallel along each photoreceptor axis with only local connections. The model is also designed to assist electrophysiological analysis of visual processing because it puts the response to a moving scene into sets of template responses similar to the distribution of activity among different neurons. An earlier template model divided the visual image into the fields of adjacent receptors, measured as intensity or receptor modulation at small increments of time. As soon as we used this model with natural scenes, however, we found that we had to look at changes in intensity, not intensity itself. Running the new model also generated new insights into the effects of very fast motion, of blurring the image, and the value of lateral inhibition. We also experimented with ways of measuring the angular velocity of the image moving across the eye. The camera eye is moved at a known speed and the range to objects is calculated from the angular velocity of contrasts moving across the receptor array. The original template model is modified so that contrast is saturated in a new representation of the original image data. This reduces the 8-bit grey-scale image to a log, 3 = 1.6-bit image, which becomes the input to a look-up table of templates. The output consists of groups of responding templates in specific ratios that define the input features, and these ratios lead into types of invariance at a higher level of further logic. At any stage, there can be persistent parallel inputs from all earlier stages. This design would enable groups of templates to be tuned to different expected situations, such as different velocities, different directions and different types of edges.