A principal challenge for both biological and machine vision systems is to integrate and organize the diversity of cues received from the environment into the coherent global representations we experience and require to make good decisions and take effective actions. Early psychological investigations date back more than 100 years to the seminal work of the Gestalt school. Yet in the last 50 years, neuroscientific and computational approaches to understanding perceptual organization have become equally important, and a full understanding requires integration of all three approaches.
This highly interdisciplinary Research Topic welcomes contributions spanning Computer Science, Psychology, and Neuroscience, with the aim of presenting a single, unified collection that will encourage integration and cross-fertilization across disciplines.
Perceptual organization can be defined as the process of establishing meaningful relational structures over raw visual data, where the extracted relations correspond to the physical structure and semantics of the scene. The relational structure may be simple, e.g., set membership for image segmentation, or more complex, for example, sequence representations of contours, hierarchical representations of surfaces, layered representations of scenes, etc. These representations support higher-level visual tasks such as object detection, object recognition, activity recognition or 3D scene understanding.
This Research Topic invites contributions on any aspect of perceptual organization of vision, including both computer vision and biological vision, using psychological, neuroscientific and/or computational approaches. The Editors especially encourage papers that integrate across these approaches to achieve a more complete understanding. Topics of interest include (but are not limited to):
* Object and scene segmentation
* Contour grouping
* Figure/ground & border ownership
* Role of feedback and recurrence
* Relationship to scene and image statistics
* Perceptual organization in depth
* Spatiotemporal grouping
* Modal and amodal completion
* Deep learning models of perceptual grouping
A principal challenge for both biological and machine vision systems is to integrate and organize the diversity of cues received from the environment into the coherent global representations we experience and require to make good decisions and take effective actions. Early psychological investigations date back more than 100 years to the seminal work of the Gestalt school. Yet in the last 50 years, neuroscientific and computational approaches to understanding perceptual organization have become equally important, and a full understanding requires integration of all three approaches.
This highly interdisciplinary Research Topic welcomes contributions spanning Computer Science, Psychology, and Neuroscience, with the aim of presenting a single, unified collection that will encourage integration and cross-fertilization across disciplines.
Perceptual organization can be defined as the process of establishing meaningful relational structures over raw visual data, where the extracted relations correspond to the physical structure and semantics of the scene. The relational structure may be simple, e.g., set membership for image segmentation, or more complex, for example, sequence representations of contours, hierarchical representations of surfaces, layered representations of scenes, etc. These representations support higher-level visual tasks such as object detection, object recognition, activity recognition or 3D scene understanding.
This Research Topic invites contributions on any aspect of perceptual organization of vision, including both computer vision and biological vision, using psychological, neuroscientific and/or computational approaches. The Editors especially encourage papers that integrate across these approaches to achieve a more complete understanding. Topics of interest include (but are not limited to):
* Object and scene segmentation
* Contour grouping
* Figure/ground & border ownership
* Role of feedback and recurrence
* Relationship to scene and image statistics
* Perceptual organization in depth
* Spatiotemporal grouping
* Modal and amodal completion
* Deep learning models of perceptual grouping