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Computation of the depth from motion using a massively parallel neural network approach

As many early vision tasks the computation of depth-from-motion is an ill-posed problem but very useful in computer vision and rotor craft navigation. The collective computation capabilities of highly parallel neural networks provides new powerful techniques for optimization problems in high dimensional spaces. This paper reports an investigation of computation of depth from motion. As this problem is formulated as minimizing a cost or energy function, a massively parallel neural network approach is used for solving this problem by regularization techniques. This approach presents some similarities with biological visual systems. The neural solution developed here is a direct method avoiding the explicit optical flow estimation. We perform an evaluation on both synthetic and real world image sequence.