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Detecting Irregularities (such as suspicious movements) in Visual Data

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Principal Investigator



Computer Science and Applied Mathematics


The new method for detecting irregularities has many applications which include:

  1. Detecting suspicious and/or salient behaviors in video
  2. Attention and saliency in images
  3. Detecting irregular tissue in medical images
  4. Automatic visual inspection for quality assurance (e.g., detecting defects in goods)
  5. Generating a video summary/synopsis
  6. Intelligent fast forward
  7. Non-visual data

    Technology's Essence

    Researchers at the Weizmann Institute have developed a new method for detecting irregularities based only on few regular examples, without any assumed models. In the new method the validity of data is determined as a process of constructing a puzzle: one tries to compose a new observed image region or a new video segment (''the query'') using chunks of data (''pieces of puzzle'') extracted from previous visual examples (''the database''). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. The invention also includes an efficient algorithm for detecting irregularities. Moreover, the same method can also be used for detecting irregularities/anomalies within data without any prior examples, by learning the notion of regularity/irregularity directly from the query data itself.

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