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Early representation of 3D volumetric shape in visual object processing

视觉对象处理中 3D 体积形状的早期表示

基本信息

批准号：
10412966
负责人：
CHARLES E CONNOR
金额：
$ 48.43万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10412966
关键词：
3-Dimensional 3D world Area Biological Brain Calcium Signaling Code Computer Vision Systems Cues Data Electrodes Functional Imaging Future Genetic Programming Glass Goals Human Image Implant Lead Learning Measures Medial Microelectrodes Microscopic Microscopy Modeling Monkeys Network-based Neurons Ocular Prosthesis Pathway interactions Patients Pattern Performance Population Prevalence Prosthesis Rehabilitation therapy Scientist Series Shapes Signal Transduction Stimulus Structure Surface Synapses System Testing Texture Three-dimensional analysis Time V4 neuron Vision Visual Cortex Visual Perception Visual impairment Work area V4 area striata base convolutional neural network experimental study individual response network models neurotransmission nonhuman primate novel object perception object recognition operation relating to nervous system response three dimensional structure two-photon virtual reality visual object processing

项目摘要

Project Summary The goal of this project is to test a novel theoretical framework for understanding how the ventral pathway subserves object vision. In the standard framework, a series of neural operations on 2D image data through many intermediate cortical stages, including area V4, leads to high-level perceptual representations, including representation of object identity, at the final stages of the ventral pathway. However, our preliminary microelectrode data from a fixating monkey show that many neurons in V4 represent volumetric (volume- enclosing) 3D shape, not 2D image patterns. These neurons respond to many different 2D images that convey the same 3D shape with different shape-in-depth cues, including shading, reflection, and refraction. They even respond preferentially to random dot stereograms that convey 3D volumetric shape with no 2D cues whatsoever. Moreover, our preliminary results with 2-photon functional imaging in anesthetized monkey V4 show that 3D shape signals are grouped by their similarity, and also group with isomorphic (same outline) 2D shape signals (which could contribute evidence to corresponding 3D shape inferences). We propose to capitalize on these preliminary data by demonstrating the prevalence of 3D shape tuning in area V4, analyzing the 3D shape coding strategies used by these neurons, and measuring how 2D and 3D shape signals are arranged at a microscopic level across the surface of V4. We expect these results to provide strong evidence that extraction of 3D shape fragments is a primary goal of V4 processing. This early extraction of 3D shape information, just two synapses beyond primary visual cortex, would suggest a competing framework for understanding the ventral pathway, in which the initial goal is to represent 3D physical structure, independent of the various 2D image cues used to infer it. In this framework, object recognition would be based on preceding information about 3D physical structure, which would explain why human object recognition is so robust to image changes, in a way that the best computational vision systems are not. The scientific impact of this work would be to divert vision experiments toward understanding representation of real world 3D structure (rather than 2D planar stimuli) and to encourage computational vision scientists to incorporate early 3D shape processing into the deep convolutional network models that are the current state of the art.

项目概要该项目的目标是测试一种新颖的理论框架，以了解腹侧通路如何促进物体视觉。在标准框架中，通过对二维图像数据进行一系列的神经操作许多中间皮质阶段，包括 V4 区，导致高级感知表征，包括在腹侧通路的最后阶段，物体身份的表示。然而，我们的初步来自固定猴子的微电极数据表明，V4 中的许多神经元代表体积（体积- 封闭）3D 形状，而不是 2D 图像图案。这些神经元对许多不同的 2D 图像做出反应，这些图像传达具有不同形状深度线索的相同 3D 形状，包括阴影、反射和折射。他们甚至优先响应在没有 2D 提示的情况下传达 3D 体积形状的随机点立体图任何。此外，我们在麻醉猴 V4 中进行 2 光子功能成像的初步结果表明 3D 形状信号按其相似性进行分组，并且还与同构（相同轮廓）2D 分组形状信号（可以为相应的 3D 形状推断提供证据）。我们建议利用这些初步数据，展示 V4 区域 3D 形状调整的普遍性，分析这些神经元使用的 3D 形状编码策略，并测量 2D 和 3D 形状信号的情况排列在 V4 表面的微观水平上。我们期望这些结果能够提供强有力的证据提取 3D 形状片段是 V4 处理的主要目标。这种早期的 3D 形状提取信息，仅在初级视觉皮层之外的两个突触，就表明了一个竞争框架了解腹侧通路，其中最初的目标是表示独立的 3D 物理结构用于推断它的各种 2D 图像线索。在此框架中，物体识别将基于前面关于 3D 物理结构的信息，这可以解释为什么人体物体识别如此之多对图像变化的鲁棒性是最好的计算视觉系统所不具备的。的科学影响这项工作将把视觉实验转向理解现实世界 3D 结构的表示（而不是 2D 平面刺激）并鼓励计算视觉科学家纳入早期 3D 形状处理成当前最先进的深度卷积网络模型。