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

AbstractTitle：合作研究：随时间变化的自然图像的等级模型：Bruno Olshausen，加利福尼亚大学 - 伯克利分校和戴维·沃兰德，加利福尼亚大学 - 戴维斯大学，该项目的目标是推动图像分析和计算机愿景的状态。捕获哺乳动物视觉系统展示的强大智能的建筑模型。 所提出的方法是基于建模时间变化的自然图像的结构，并开发了能够有效代表该结构的模型神经系统。这种方法将阐明视觉感知所涉及的潜在神经机制，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。要开发的模型将使图像（形式，形状）中的不变结构成为独立于其变化（位置，大小，旋转）的描述。 这些模型由多层组成，除了建模其转换外，这些层逐渐捕获场景结构的更复杂形式。 从数学上讲，这些多层模型具有双线性形式，其中代表形状和形式的变量与代表位置，大小或其他变化的变量倍增。 该模型的参数是使用稀疏有效编码的原理从随时间变化的自然图像的统计数据中学到的。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，。视觉皮层中视网膜神经节细胞和皮质简单细胞的接受场特性的预测）和图像处理（例如小波，多尺度表示，图像denoising）。 该提案中概述的方法通过从随时间变化的自然图像的序列中学习高阶场景结构来扩展这项跨学科的工作。 考虑到视觉皮层上有效处理时间变化图像的进化压力，可以从有效的处理中施加的约束来部分理解皮质执行的计算是合理的。 对高阶结构进行建模还将通过找到用于图像处理任务（例如视频搜索和索引）的良好表示形式来推动实际图像处理算法的开发。 该提案中描述的特定目标的完成将提供（1）数学模型，这些模型可以帮助阐明视觉感知中涉及的潜在神经机制以及（2）（2）时间变化图像的新生成模型，以更好地描述其结构。图像和视频已创建了全国优先权，即为对象识别和搜索，导航，监视和图像分析等任务提供更好的工具。 作为本提案的一部分开发的模型广泛适用于这些任务。该研究计划的结果将纳入加州大学伯克利分校的新神经计算课程，并在国家多学科会议上介绍，并及时出版在领先的PEER评论期刊上。 研究生和本科级别都可以参与拟议的研究，PI将为博士提供建议。神经科学和工程的学生作为此项目的一部分：http：//redwood.berkeley.edu/wiki/nsf_funded_research