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

摘要标题：协作研究：时变自然图像的分层模型PI：加州大学伯克利分校的Bruno Olshausen和加州大学戴维斯分校的David Warland这个项目的目标是通过建立捕捉哺乳动物视觉系统所展示的强大智能的模型来推动图像分析和计算机视觉的发展。该方法基于对时变自然图像的结构进行建模，并开发能够有效地表示这种结构的模型神经系统。这一方法将阐明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的实际问题。将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。这些模型由多个层组成，这些层除了对场景结构的变换进行建模外，还捕获了逐渐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，其中表示形状和形状的变量与表示位置、大小或其他变化的变量相乘地相互作用。该模型的参数利用稀疏和高效编码的原理从时变的自然图像的统计中学习。自然图像结构的早期测量和模型对包括视觉神经科学(例如，预测视网膜神经节细胞和视觉皮质简单细胞的感受场特性)和图像处理(例如，小波、多尺度表示、图像去噪)等多个学科产生了深远的影响。本文提出的方法通过从时变的自然图像序列中学习高阶场景结构，扩展了这种跨学科的工作。考虑到视皮层面临着高效处理时变图像的进化压力，从有效处理所带来的限制中可以部分地理解大脑皮层进行的计算。通过为视频搜索和索引等图像处理任务找到良好的表示，对高阶结构进行建模也将推动实际图像处理算法的发展。完成这项提案中描述的具体目标将提供(1)数学模型，有助于阐明视觉感知所涉及的潜在神经机制；(2)新的时变图像生成模型，更好地描述其结构。数字图像和视频的爆炸性增长已成为国家优先事项，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。这一研究计划的成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，在全国性的多学科会议上展示，并及时发表在领先的同行评议期刊上。研究生和本科生都可以参与建议的研究，作为这个项目的一部分，PI将为神经科学和工程学的博士生提供建议。网址：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