SUMMARYIn this paper, a new algorithm for the cellular active contour technique called pixel-level snakes is proposed. The motivation is twofold: on the one hand, a higher e ciency and exibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip-set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of in ating=de ating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and e ciency of the new scheme several examples are discussed including real applications from medical imaging.

FeatureThe Bi-i standalone cellular vision system is introduced and discussed. In the first part, the underlying sensor and system level architectures are presented and various implementations are overviewed. This computing platform consists of state-of-the-art sensing, cellular sensing-processing, digital signal processing and communication devices that make it possible to use the system as an ideal computing platform for combined topographic and non-topographic calculations in sensing-processing-actuation scenarios. In the second part of the paper, ultra-high frame rate laboratory experiments are shown and discussed to highlight the most peculiar features of the system and its applicability in various industrial quality control areas. The overview underlines the potentials of the Bi-i vision system for unmanned intelligent vehicle applications in visual exploration, identification, tracking and navigation.

A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 /spl mu/m CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.

SUMMARYMixed-signal cellular visual microprocessor architecture with digital processors is described. An Application Specific Integrated Circuit (ASIC) implementation is also demonstrated. The architecture is composed of a regular sensor readout circuit array, prepared for 3D face-to-face-type integration, and one or several cascaded array of mainly identical (single instruction multiple data, SIMD) processing elements. The individual array elements were derived from the same general Hardware Description Language (HDL) description and could be of different sizes, aspect ratio, and computing resources.