Forum annuel du GDR Vision, Toulouse, 26 et 27 Janvier 2023

Measuring uncertainty in human visual segmentation
Jonathan Vacher  1@  , Claire Launay  2@  , Pascal Mamassian  3@  , Ruben Coen-Cagli  2@  
1 : MAP5
Université Paris Cité : UMR8145
2 : Department of Systems and Computational Biology, Albert Einstein College of Medicine, NY, USA
3 : Laboratoire des systèmes perceptifs
Département d’études cognitives, École normale supérieure, PSL University, CNRS

Segmenting visual inputs into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple same--different judgments and perform model--based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures, and we show that image uncertainty affects measured human variability as well as the way participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.

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