ArticlePLoS biology2017
Object segmentation controls image reconstruction from natural scenes.
Article in PLoS biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Are we ready to tackle perceptual segmentation of natural scenes?Vision research · 2026Review
- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- Line drawings as a tool to probe edge sensitivity in natural scenes.Journal of vision · 2025Article
- The visibility of Eidolon distortions in things and stuff.Journal of vision · 2025Article
- A robotics-inspired scanpath model reveals the importance of uncertainty and semantic object cues for gaze guidance in dynamic scenes.Journal of vision · 2025Article
- Modulatory feedback determines attentional object segmentation in a model of the ventral stream.PloS one · 2025Article
- V1 neurons are tuned to perceptual borders in natural scenes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Human sensory adaptation to the ecological structure of environmental statistics.Journal of vision · 2024Article
- Objects guide human gaze behavior in dynamic real-world scenes.PLoS computational biology · 2023Article
- Measuring uncertainty in human visual segmentation.PLoS computational biology · 2023Article
- Flexibly regularized mixture models and application to image segmentation.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Spatial structure, phase, and the contrast of natural images.Journal of vision · 2022Article
- Redundancy between spectral and higher-order texture statistics for natural image segmentation.Vision research · 2021Article
- Flexible contextual modulation of naturalistic texture perception in peripheral vision.Journal of vision · 2021Review
- Visual Functions of Primate Area V4.Annual review of vision science · 2020Review
- Constrained sampling from deep generative image models reveals mechanisms of human target detection.Journal of vision · 2020Article
- Forms of prediction in the nervous system.Nature reviews. Neuroscience · 2020Review
- Contrast versus identity encoding in the face image follow distinct orientation selectivity profiles.PloS one · 2020Article
- Common spatiotemporal processing of visual features shapes object representation.Scientific reports · 2019Article
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1 author.
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Abstract
The structure of the physical world projects images onto our eyes. However, those images are often poorly representative of environmental structure: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. The challenge for the visual cortex is to sort these two types of features according to their utility in ultimately reconstructing percepts and interpreting the constituents of the scene. We describe a novel paradigm that enabled us to selectively evaluate the relative role played by these two feature classes in signal reconstruction from corrupted images. Our measurements demonstrate that this process is quickly dominated by the inferred structure of the environment, and only minimally controlled by variations of raw image content. The inferential mechanism is spatially global and its impact on early visual cortex is fast. Furthermore, it retunes local visual processing for more efficient feature extraction without altering the intrinsic transduction noise. The basic properties of this process can be partially captured by a combination of small-scale circuit models and large-scale network architectures. Taken together, our results challenge compartmentalized notions of bottom-up/top-down perception and suggest instead that these two modes are best viewed as an integrated perceptual mechanism.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.