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Using the neuromorphic approach, we propose an analog very large-scale integration (VLSI) implementation of an oscillatory segmentation algorithm based on local excitatory couplings and global inhibition. The original model has been simplified and adapted for its efficient VLSI implementation while preserving its segmentation properties. To demonstrate the feasibility of the approach, a 16×16-pixel testchip has been manufactured. Extensive experimental results demonstrate that it can properly segment binary images. Power consumption, segmentation time per cell, and system complexity are very low compared to other hardware and software implementation schemes. We also show two main differences between the original algorithm and the analog approach. First, the network is noise tolerant without the need of additional elements and second, delays between oscillators due to the combination of mismatch and output capacitances have to be accounted for network performance.