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

摘要题目：合作研究：时变自然图像的层次模型（作者：Bruno Olshausen，加州大学伯克利分校和David Warland，加州大学戴维斯分校）该项目的目标是通过建立模型来捕捉哺乳动物视觉系统所表现出的强大智能，从而推动图像分析和计算机视觉领域的最新发展。该方法基于对时变自然图像的结构进行建模，并开发能够有效表征该结构的模型神经系统。这种方法将揭示涉及视觉感知的潜在神经机制，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。这些模型由多层组成，除了对它们的转换建模外，还可以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。该模型的参数是根据时变自然图像的统计数据，利用稀疏和高效编码的原则来学习的。自然图像结构的早期测量和模型对许多学科产生了深远的影响，包括视觉神经科学（例如视网膜神经节细胞和视觉皮层皮层简单细胞的感受野特性的预测）和图像处理（例如小波、多尺度表示、图像去噪）。本提案中概述的方法通过从时变自然图像序列中学习高阶场景结构来扩展这一跨学科工作。考虑到视觉皮层在有效处理时变图像方面的进化压力，我们有理由认为，大脑皮层执行的计算部分可以从有效处理所施加的限制中理解。高阶结构的建模也将通过为图像处理任务（如视频搜索和索引）找到良好的表示来推进实际图像处理算法的发展。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所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式增长使国家优先考虑为目标识别和搜索、导航、监视和图像分析等任务提供更好的工具。作为本建议的一部分开发的模型广泛适用于这些任务。该研究项目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，并及时在领先的同行评审期刊上发表。研究生和本科生都可以参与拟议的研究，作为该项目的一部分，PI将为神经科学和工程学的博士生提供建议。URL: http://redwood.berkeley.edu/wiki/NSF_Funded_Research

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