تقرير
Measuring uncertainty in human visual segmentation
العنوان: | Measuring uncertainty in human visual segmentation |
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المؤلفون: | Vacher, Jonathan, Launay, Claire, Mamassian, Pascal, Coen-Cagli, Ruben |
سنة النشر: | 2023 |
المجموعة: | Computer Science Quantitative Biology |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition |
الوصف: | Segmenting visual stimuli 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 pixel-based 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. We show that image uncertainty affects measured human variability, and it influences how 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. Comment: 32 pages, 9 figures, 5 appendix, 5 figures in appendix |
نوع الوثيقة: | Working Paper |
DOI: | 10.1371/journal.pcbi.1011483 |
URL الوصول: | http://arxiv.org/abs/2301.07807 |
رقم الأكسشن: | edsarx.2301.07807 |
قاعدة البيانات: | arXiv |
DOI: | 10.1371/journal.pcbi.1011483 |
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