Abrupt darkening under high dynamic range (HDR) luminance invokes facilitation for high-contrast targets and grouping by luminance similarity

Hung, Chou P.; Callahan-Flintoft, Chloe; Fedele, Paul D.; Fluitt, Kim F.; Odoemene, Onyekachi; Walker, Anthony J.; Harrison, Andre; Vaughan, Barry D.; Jaswa, Matthew; Wei, Min

doi:10.1167/jov.20.7.9

Cited by 6 publications

(4 citation statements)

References 58 publications

(79 reference statements)

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“… 1997 ; Li 1998 ) and have already inspired computer vision algorithms for contour enhancement (Parent and Zucker 1989 ; Guy and Medioni 1996 ; Hung et al. 2020 ; White et al. 2022 ).…”

Section: Contour Grouping Boundary Detection and Texture Segregationmentioning

confidence: 99%

“…Grouping mechanisms that employ Gestalt laws of perceptual organization, such as, good continuation, proximity, and similarity, have been defined in other biologically plausible models (Grossberg and Mingolla 1985;Grossberg et al 1997;Li 1998) and have already inspired computer vision algorithms for contour enhancement (Parent and Zucker 1989;Guy and Medioni 1996;Hung et al 2020;White et al 2022). The architecture outlined here emphasizes that the proposed canonical principles of computation i.e., feedforward and feedback processing combined with competition and normalization, can achieve contextual amplification through grouping, as well as de-amplification or suppression in case of reduced information content of stimulus components.…”

Section: Perceptual Binding In Contour Grouping and Texture Suppressionmentioning

confidence: 99%

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Canonical circuit computations for computer vision

2023

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Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, technical solutions have been shaped by application and engineering constraints. This includes the training of neural networks which led to the development of feature detectors optimally suited to the application domain. However, the limitations of such approaches motivate the need to identify computational principles, or motifs, in biological vision that can enable further foundational advances in machine vision. We propose to utilize structural and functional principles of neural systems that have been largely overlooked. They potentially provide new inspirations for computer vision mechanisms and models. Recurrent feedforward, lateral, and feedback interactions characterize general principles underlying processing in mammals. We derive a formal specification of core computational motifs that utilize these principles. These are combined to define model mechanisms for visual shape and motion processing. We demonstrate how such a framework can be adopted to run on neuromorphic brain-inspired hardware platforms and can be extended to automatically adapt to environment statistics. We argue that the identified principles and their formalization inspires sophisticated computational mechanisms with improved explanatory scope. These and other elaborated, biologically inspired models can be employed to design computer vision solutions for different tasks and they can be used to advance neural network architectures of learning.

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“… 1997 ; Li 1998 ) and have already inspired computer vision algorithms for contour enhancement (Parent and Zucker 1989 ; Guy and Medioni 1996 ; Hung et al. 2020 ; White et al. 2022 ).…”

Section: Contour Grouping Boundary Detection and Texture Segregationmentioning

confidence: 99%

Section: Perceptual Binding In Contour Grouping and Texture Suppressionmentioning

confidence: 99%

Canonical circuit computations for computer vision

2023

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“…Graphically representing military decision-making strategies at all levels of operations requires new visualization approaches that can be applied to dynamic environments characterized by changing rules, cognitive states, uncertainty, and individual biases and heuristics ( Dennison et al, 2020 ; Hung et al, 2020 ; Raglin et al, 2020 ). The visual representation of a battlespace should be as accurate and realistic as technologically possible, yet remain at a cognitive level that is human understandable and interpretable ( Kase et al, 2020 ; Larkin et al, 2020 ; Hung et al, 2021 ).…”

Section: Required Advancements For Future Military Decision Making Pr...mentioning

confidence: 99%

The Future of Collaborative Human-Artificial Intelligence Decision-Making for Mission Planning

et al. 2022

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In an increasingly complex military operating environment, next generation wargaming platforms can reduce risk, decrease operating costs, and improve overall outcomes. Novel Artificial Intelligence (AI) enabled wargaming approaches, based on software platforms with multimodal interaction and visualization capacity, are essential to provide the decision-making flexibility and adaptability required to meet current and emerging realities of warfighting. We highlight three areas of development for future warfighter-machine interfaces: AI-directed decisional guidance, computationally informed decision-making, and realistic representations of decision spaces. Progress in these areas will enable development of effective human-AI collaborative decision-making, to meet the increasing scale and complexity of today’s battlespace.

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“…Another challenge is navigating through complex dynamic environments with obstacle avoidance, such as flying through treetops with wind blowing through leaves and twigs. This requires luminance normalization (Hung et al, 2020), coupled with contextual processing of higher order features, environmental and task-related visual statistics, as well as feedback and recurrent processing for visual prediction. Whereas in biology such normalization, contextualization, and prediction are interdependent processes, there still exists a major gap in computational implementation today.…”

Section: Missions and Needsmentioning

confidence: 99%

Autonomous Flying With Neuromorphic Sensing

et al. 2021

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Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.

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Abrupt darkening under high dynamic range (HDR) luminance invokes facilitation for high-contrast targets and grouping by luminance similarity

Cited by 6 publications

References 58 publications

Canonical circuit computations for computer vision

Canonical circuit computations for computer vision

The Future of Collaborative Human-Artificial Intelligence Decision-Making for Mission Planning

Autonomous Flying With Neuromorphic Sensing

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