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Parallel image processing for robotics applications differs in a fundamental way from parallel scientific computing applications: the problem size is fixed, and latency requirements are tight. This brings Amdhal's law in effect with full force, so that message-passing latency and bandwidth severely restrict performance. This has led to common design characteristics in machines for robotics image processing, which I will discuss. I will examine an application from robotics vision, stereo image processing, which has been implemented in Adapt, a niche language for parallel image processing implemented on the Carnegie Mellon-Intel Corporation iWarp. High performance has been achieved for this application. I will show how a I/O building block approach on iWarp achieved this, and then examine the implications of this performance for more traditional machines that do not have iWarp's rich I/O primitive set.