CRCNS: Probabilistic models of perceptual grouping/segmentation in natural vision

CRCNS：自然视觉中感知分组/分割的概率模型

• 批准号: 9916219
• 负责人: Ruben Coen-Cagli
• 金额: $ 19.52万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9916219
• 关键词: Address; Algorithms; Behavioral; Cognitive; Computer Vision Systems; Cues; Data; Data Set; Discrimination; Environment; Experimental Designs; Feedback; Goals; Grouping; Human; Image; Impairment; Individual; Influentials; Instruction; Label; Machine Learning; Manuals; Maps; Measurement; Measures; Mental disorders; Modeling; Motor; Neurodevelopmental Disorder; Neurons; Participant; Perception; Process; Protocols documentation; Recurrence; Semantics; Sensory; Statistical Models; Stimulus; System; Testing; Texture; Uncertainty; Vision; Visual; Visual Cortex; Visual impairment; Work; base; behavior influence; computer framework; deep learning; detector; expectation; experimental study; flexibility; imaging Segmentation; improved; object recognition; patient population; predictive modeling; sensory input; sensory integration; sensory neuroscience; sensory system; statistics; theories; vision science; visual processing

To understand and navigate the environment, sensory systems must solve simultaneously two competing and challenging tasks: the segmentation of a sensory scene into individual objects and the grouping of elementary sensory features to build these objects. Understanding perceptual grouping and segmentation is therefore a major goal of sensory neuroscience, and it is central to advancing artificial perceptual systems that can help restore impaired vision. To make progress in understanding image segmentation and improving algorithms, this project combines two key components. First, a new experimental paradigm that allows for well-controlled measurements of perceptual segmentation of natural images. This addresses a major limitation of existing data that are either restricted to artificial stimuli, or, for natural images, rely on manual labeling and conflate perceptual, motor, and cognitive factors. Second, this project involves developing and testing a computational framework that accommodates bottom-up information about image statistics and top-down information about objects and behavioral goals. This is in contrast with the paradigmatic view of visual processing as a feedforward cascade of feature detectors, that has long dominated computer vision algorithms and our understanding of visual processing. The proposed approach builds instead on the influential theory that perception requires probabilistic inference to extract meaning from ambiguous sensory inputs. Segmentation is a prime example of inference on ambiguous inputs: the pixels of an image often cannot be labeled with certainty as grouped or segmented. This project will test the hypothesis that human visual segmentation is a process of hierarchical probabilistic inference. Specific Aim 1 will determine whether the measured variability of human segmentations reflects the uncertainty predicted by the model, as required for well-calibrated probabilistic inference. Specific Aim 2 addresses how feedforward and feedback processing in human segmentation contribute to efficient integration of visual features across different levels of complexity, from small contours to object parts. Specific Aim 3 will determine reciprocal interactions between perceptual segmentation and top-down influences including: semantic scene content; visual texture discrimination; and expectations reflecting environmental statistics. The proposed approach models these influences as Bayesian priors, and thus, if supported by the proposed experiments, will offer a unified framework to understand the integration of bottom-up and top- down influences in human segmentation of natural inputs. RELEVANCE (See instructions): This project aims to provide a unified understanding of perceptual segmentation and grouping of visual inputs encountered in the natural environment, through correct integration of the information contained in the visual inputs with top-down information about objects and behavioral goals. This understanding is central to advancing artificial perceptual systems that can help restore impaired vision in patient populations.

为了理解和导航环境，感觉系统必须同时解决两个相互竞争的问题。 具有挑战性的任务：将感官场景分割成单个对象，并将 基本的感官特征来建造这些物体。理解感知分组和分割 因此，这是感觉神经科学的一个主要目标，也是推进人工感知的核心。 可以帮助恢复受损视力的系统。在理解图像分割方面取得进展 和改进算法，这个项目结合了两个关键组成部分。首先，一个新的实验范式 其允许对自然图像的感知分割的良好控制的测量。这解决 现有数据的一个主要限制是，要么局限于人工刺激，要么对于自然图像，依赖于 手动标记和合并感知，运动和认知因素。第二，该项目涉及 开发和测试一个计算框架，该框架容纳关于图像的自下而上的信息， 关于对象和行为目标的统计数据和自上而下的信息。这是在对比 视觉处理作为特征检测器的前馈级联的范式观点，长期以来 主导了计算机视觉算法和我们对视觉处理的理解。所提出的方法 相反，它建立在有影响力的理论之上，即感知需要概率推理来提取意义。 模糊的感官输入分段是对模糊输入进行推理的一个主要例子： 图像的像素通常不能被确定地标记为分组或分段。该项目将测试 假设人类视觉分割是一个分层概率推理的过程。具体目标 1将确定人类分割的测量可变性是否反映了不确定性 由模型预测，如良好校准的概率推断所需。具体目标2地址 人类分割中的前馈和反馈处理如何有助于有效整合 不同复杂程度的视觉特征，从小轮廓到对象部分。第3章将 确定感知分割和自上而下影响之间的相互作用，包括： 语义场景内容;视觉纹理辨别;和反映环境统计的期望。 所提出的方法将这些影响建模为贝叶斯先验，因此，如果得到 提出的实验，将提供一个统一的框架，以了解自下而上和自上而下的整合， 在人类对自然输入的分割中的影响。 相关性（参见说明）： 本项目旨在提供一个统一的理解知觉分割和分组的视觉 在自然环境中遇到的输入，通过正确整合所包含的信息， 视觉输入与自上而下的信息有关的对象和行为目标。这种理解是 推进人工感知系统的核心，可以帮助患者恢复受损的视力 人口。