RI: Collaborative Research: Hierarchical Models of Time-Varying Natural Images

RI：协作研究：时变自然图像的层次模型

• 批准号: 0705939
• 负责人: Bruno Olshausen
• 金额: $ 43.99万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705939&HistoricalAwards=false
• 关键词: RI; Collaborative; Research; Hierarchical; Models

AbstractTitle: Collaborative Research: Hierarchical Models of Time-Varying natural ImagesPIs: Bruno Olshausen, University of California-Berkeley and David Warland, University of California-DavisThe goal of this project is to advance the state of the art in image analysis and computer vision by building models that capture the robust intelligence exhibited by the mammalian visual system. The proposed approach is based on modeling the structure of time-varying natural images, and developing model neural systems capable of efficiently representing this structure. This approach will shed light on the underlying neural mechanisms involved in visual perception and will apply these mechanisms to practical problems in image analysis and computer vision.The models that are to be developed will allow the invariant structure in images (form, shape) to be described independently of its variations (position, size, rotation). The models are composed of multiple layers that capture progressively more complex forms of scene structure in addition to modeling their transformations. Mathematically, these multi-layer models have a bilinear form in which the variables representing shape and form interact multiplicatively with the variables representing position, size or other variations. The parameters of the model are learned from the statistics of time-varying natural images using the principles of sparse and efficient coding.The early measurements and models of natural image structure have had a profound impact on a wide variety of disciplines including visual neuroscience (e.g. predictions of receptive field properties of retinal ganglion cells and cortical simple cells in visual cortex) and image processing (e.g. wavelets, multi-scale representations, image denoising). The approach outlined in this proposal extends this interdisciplinary work by learning higher-order scene structure from sequences of time-varying natural images. Given the evolutionary pressures on the visual cortex to process time-varying images efficiently, it is plausible that the computations performed by the cortex can be understood in part from the constraints imposed by efficient processing. Modeling the higher order structure will also advance the development of practical image processing algorithms by finding good representations for image-processing tasks such as video search and indexing. Completion of the specific goals described in this proposal will provide (1) mathematical models that can help elucidate the underlying neural mechanisms involved in visual perception and (2) new generative models of time-varying images that better describe their structure.The explosion of digital images and video has created a national priority of providing better tools for tasks such as object recognition and search, navigation, surveillance, and image analysis. The models developed as part of this proposal are broadly applicable to these tasks. Results from this research program will be integrated into a new neural computation course at UC Berkeley, presented at national multi-disciplinary conferences, and published in a timely manner in leading peer-reviewed journals. Participation in proposed research is available to both graduate and undergraduate levels, and the PI will advise Ph.D. students in both neuroscience and engineering as part of this project.URL: http://redwood.berkeley.edu/wiki/NSF_Funded_Research

摘要标题：合作研究：时变自然图像的层次模型PI：Bruno Olshausen，加州大学伯克利分校和大卫沃兰，加州大学戴维斯分校该项目的目标是通过构建捕捉哺乳动物视觉系统所表现出的鲁棒智能的模型来推进图像分析和计算机视觉的最新技术。 所提出的方法是基于对时变自然图像的结构建模，并开发能够有效地表示这种结构的模型神经系统。这种方法将阐明视觉感知的神经机制，并将这些机制应用于图像分析和计算机视觉中的实际问题，所开发的模型将允许图像中的不变结构（形式，形状）独立于其变化（位置，大小，旋转）进行描述。 模型由多个层组成，这些层除了对其变换进行建模之外，还捕获越来越复杂的场景结构形式。 在数学上，这些多层模型具有双线性形式，其中表示形状和形式的变量与表示位置、大小或其他变化的变量相乘地相互作用。 模型的参数是利用稀疏和高效编码的原理从时变自然图像的统计中学习的。自然图像结构的早期测量和模型对包括视觉神经科学在内的各种学科产生了深远的影响（例如，预测视网膜神经节细胞和视觉皮层中的皮层简单细胞的感受野特性）和图像处理（例如小波、多尺度表示、图像去噪）。 在这个建议中概述的方法扩展了这个跨学科的工作，学习高阶场景结构的序列随时间变化的自然图像。 考虑到视觉皮层有效处理时变图像的进化压力，皮层执行的计算可以部分地从有效处理所施加的约束来理解，这是合理的。 对高阶结构进行建模还将通过为视频搜索和索引等图像处理任务找到良好的表示来推进实用图像处理算法的开发。 完成本提案中描述的具体目标将提供（1）有助于阐明视觉感知中涉及的潜在神经机制的数学模型和（2）更好地描述其结构的时变图像的新生成模型。数字图像和视频的爆炸已经创建了一个国家优先级，为对象识别和搜索，导航，监视，和图像分析。 作为本提案一部分而开发的模型广泛适用于这些任务。这项研究计划的结果将被整合到加州大学伯克利分校的一门新的神经计算课程中，在全国多学科会议上发表，并及时发表在领先的同行评审期刊上。 研究生和本科生均可参与拟议的研究，PI将向博士提供建议。作为这个项目的一部分，学生在神经科学和工程。URL： http://redwood.berkeley.edu/wiki/NSF_Funded_Research