Conformal anomaly detection for visual reconstruction using gestalt principles

Ilia Nouretdinov, Alexander Balinsky, Alex Gammerman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we combine a modern machine learning technique called conformal predictors (CP) with elements of gestalt detection and apply them to the problem of visual perception in digital images. Our main task is to quantify several gestalt principles of visual reconstruction. We interpret an image/shape as being perceivable (meaningful) if it sufficiently deviates from randomness - in other words, the image could hardly happen by chance. These deviations from randomness are measured by using conformal prediction technique that can guarantee the validity under certain assumptions. The technique describes the detection of perceivable images that allows to bound the number of false alarms, i.e. the proportion of non-perceivable images wrongly detected as perceivable.
Original languageEnglish
Title of host publicationProceedings of the Ninth Symposium on Conformal and Probabilistic Prediction and Applications
Number of pages20
Publication statusPublished - 21 Aug 2020

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