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

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独的对象，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，它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 可以帮助恢复视力受损的系统。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。一、新的实验范式 这允许对自然图像的感知分割进行良好控制的测量。这是地址 现有数据的主要限制要么限于人工刺激，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，这个项目涉及到 开发和测试包含有关图像的自下而上信息的计算框架 有关对象和行为目标的统计信息和自上而下的信息。这与 视觉处理的范例视图是特征检测器的前馈级联，这已经有很长时间了 主导了计算机视觉算法和我们对视觉处理的理解。建议的方法 相反，它建立在一种有影响力的理论之上，即感知需要概率推理来提取意义 来自模糊的感官输入。分段是对不明确的输入进行推理的主要示例： 图像的像素通常不能确定地标记为分组或分割。该项目将测试 假设人类视觉分割是一个分层的概率推理过程。特定目标 1将确定所测量的人体分割的可变性是否反映了不确定性 由模型预测，这是校准好的概率推理所需的。具体目标2 人类分割中的前馈和反馈处理如何有助于有效地集成 不同复杂程度的视觉特征，从小轮廓到物体部分。具体目标3将 确定感知分段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；以及反映环境统计的预期。 建议的方法将这些影响建模为贝叶斯先验，因此，如果 拟议的实验将提供一个统一的框架来理解自下而上和自上而下的集成 向下影响人类对自然输入的分割。 相关性(请参阅说明)： 该项目旨在提供对视觉的感知分割和分组的统一理解 在自然环境中遇到的输入，通过正确整合 具有关于对象和行为目标的自上而下信息的可视输入。这种理解是 先进的人工感知系统的核心，可以帮助恢复患者受损的视力 人口。

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.