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

职务名称： 合作研究：时变自然图像PI的层次模型：Bruno Olshausen和大卫Warland这项研究的长期目标是开发一个视觉感知的计算模型，实现生物视觉系统中表现出的相同程度的鲁棒智能。拟议的研究将通过构建捕捉哺乳动物视觉系统的鲁棒智能的模型来推进时变图像分析的最新技术水平。这些模型将允许不变量结构（形式，形状）独立于其变化（位置，大小，旋转）进行建模，并且将由多个层组成，这些层除了对其变换进行建模之外，还可以捕获越来越复杂的场景结构形式。 从数学上说，这些多层模型具有强大的双线性形式，它们的详细结构是使用稀疏和有效编码的原理从自然时变图像中学习的。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，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的预测）和图像处理（例如，小波、多尺度表示、图像去噪）。该项目所采取的方法通过从自然时变图像序列中学习高阶场景结构来扩展这一跨学科的工作。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表示所施加的约束中理解，这是合理的。高阶结构的建模也将通过为手头的图像处理任务找到场景的良好表示来推进实际图像处理算法的发展。该项目的具体目标的完成将提供新的生成模型的时变图像形成和工具，以分析自然场景的统计。大多数图像处理问题大大简化通过找到一个良好的数据表示。因此，这项研究具有实际应用，用于获得改进的手段，用于表示，索引和访问数字内容，如2D图像和视频。 作为该项目的一部分开发的模型也广泛适用于先进的图像处理算法，例如电影去噪、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，例如听觉信号分析，网络信号的动态路由以及复杂数据集的一般数据挖掘。

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.