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

SGER 合作研究：时变自然图像的层次模型

• 批准号: 0625717
• 负责人: Bruno Olshausen
• 金额: $ 5.7万
• 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: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0625717&HistoricalAwards=false
• 关键词: SGER; Collaborative; Research; Hierarchical; Models

Title: Collaborative Research: Hierarchical Models of Time-Varying Natural ImagesPIs: Bruno Olshausen and David WarlandThe long-term goal of this research is to develop a computational model of visual perception that achieves the same degree of robust intelligence exhibited in biological vision systems. The proposed research will advance the state of the art in the analysis of time-varying images by building models that capture the robust intelligence of the mammalian visual system. These models will allow the invariant structure (form, shape) to be modeled independently of its variations (position, size, rotation) and will be composed of multiple layers that capture progressively more complex forms of scene structure in addition to modeling its transformations. Mathematically, these multi-layer models have a powerful bilinear form and their detailed structure is learned from natural time-varying 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 taken by this project extends this interdisciplinary work by learning higher-order scene structure from sequences of natural time-varying 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 representation. Modeling the higher order structure will also advance the development of practical image processing algorithms by finding good representations of the scene for the image-processing task at hand. Completion of the specific goals of this project will provide new generative models of time-varying image formation and tools with which to analyze the statistics of natural scenes.Most image processing problems are greatly simplified by finding a good representation of the data. As a result, this research has practical applications for deriving improved means for representing, indexing, and accessing digital content such as 2D images, and video. the models developed as part of this project are also broadly applicable to advancing image processing algorithms such as denoising of movies, movie compression, and scene analysis and classification. In addition, these models have a mathematical form that makes them generally applicable to research areas other than vision such as analysis of auditory signals, dynamic routing of network signals, and general data mining of complex data sets.

标题：协作研究：随时间变化的自然图像的层次结构模型：Bruno Olshausen和David Warland这项研究的长期目标是开发一种视觉感知的计算模型，该模型在生物视觉系统中表现出相同程度的稳健智能。拟议的研究将通过构建捕获哺乳动物视觉系统的稳健智能的模型来分析时变图像的现状。这些模型将允许不变结构（形式，形状）独立于其变化（位置，大小，旋转）建模，并由多层组成，这些层除了建模其转换之外，还逐渐捕获场景结构的更复杂形式的场景结构形式。 从数学上讲，这些多层模型具有强大的双线性形式，使用稀疏和有效编码的原理从自然变化的图像中学到了它们的详细结构。自然图像结构的早期测量和模型对广泛的影响产生了深远的影响各种学科包括视觉神经科学（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）。该项目采用的方法通过从自然变体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。鉴于视觉皮层上有效处理时间变化图像的进化压力，可以从有效表示的约束中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为手头的图像处理任务找到场景的良好表示来推动实际图像处理算法的开发。该项目的特定目标的完成将提供新的生成模型，以分析自然场景的统计数据，以实现时变图像形成和工具。通过找到良好的数据表示，大大简化了图像处理问题。结果，这项研究具有实用的应用，用于取得改进的方式，用于表示，索引和访问数字内容，例如2D图像和视频。 作为该项目的一部分开发的模型也广泛地适用于推进图像处理算法，例如电影，电影压缩以及场景分析和分类。 此外，这些模型具有数学形式，使其通常适用于视觉以外的研究领域，例如对听觉信号的分析，网络信号的动态路由以及复杂数据集的一般数据挖掘。