Phase Processing Machines


Although images can be represented by their global phase alone, phase information has largely been ignored in the field of linear signal processing and for good reason. Phase-based information processing is intrinsically non-linear. Recent research, however, showed that phase information can be smartly employed in speech processing and visual processing. For example, spatial phase in an image is indicative of local features such as edges when considering phase congruency. We have devised a motion detection algorithm based on local phase information and constructed a fast, parallel algorithm for its real-time implementation. Our results suggest that local spatial phase information may provide an efficient alternative to perform many visual tasks in silico as well as in vivo biological vision systems.

  1. Aurel A. Lazar, Nikul H. Ukani and Yiyin Zhou, A Motion Detection Algorithm Using Local Phase Information, Computational Intelligence and Neuroscience, Volume 2016, January 2016.

The following video shows motion detected in the "train station video" by the phase-based motion detection algorithm, and compares the result with that of the Reichardt motion detector and the Barlow-Levick motion detector.

The top row shows motion detected by the phase-based motion detection algorithm (left), the Reichardt motion detector (middle) and the Barlow-Levick motion detector (right). Red arrows indicate detected motion and its direction. On the bottom row, the contrast of the video was artificially reduced 5 fold and the mean was reduced to 3/5 of the original. The red arrows shown are duplicated from the output of the motion detectors on the original video as on the top row. Blue arrows are the result of motion detection with reduced contrast. If motion is detected both in the original video and in the video with reduced contrast, then the corresponding arrow is shown in magenta (as a mix of blue and red).


The Bionet Group is supported by grants from



  NSF   NIH   AFOSR



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