Research and Training in Vision and Computational Neuroscience

视觉和计算神经科学的研究和培训

• 批准号: 9707006
• 负责人: Amir Assadi
• 金额: $ 5万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-15 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9707006&HistoricalAwards=false
• 关键词: Research; Training; Vision; Computational; Neuroscience

Assadi 9707006 Visual perception of surfaces is crucial for 3D object recognition. All surfaces in natural scenes are endowed with texture. In this project, the investigator develops new algorithms to estimate shape (geometric characteristics of surfaces) from texture in natural and synthetic scenes. The new geometric models use piecewise Riemannian foliations, in the sense of differential topology, with additional structure. Given a 3D-textured surface, not necessarily piecewise smooth, one constructs a foliation whose leaves form a one-parameter family of 2D-textured and piecewise smooth mathematical surfaces approximating the given object. Together with an objective function defined on their leaves, such 2D-textured foliations are fundamental geometric objects that model 3D-textured surfaces in the world. One can use algorithms to recover shape from texture for the piecewise smooth leaves, e.g. curvature and slant. Several scene-based methods are explored to construct textured foliations, ranging from analytic (e.g. Hamilton-Jacobi equations) and topological techniques (e.g. integrable distributions) to statistical estimation methods. The psychophysical experiments to test the theory and compare different algorithms are explored with colleagues who are neuroscience experimentalists. In particular, the objective function can be numerically approximated based on psychophysical data. The new models are applied to perception of symmetry. The problem of modeling computational strategies employed by the visual cortex to estimate shape from texture, and their comparison with the new computational algorithms, is pursues. The investigator outlines a concrete training program and research collaboration with his senior colleagues in vision and neuroscience at UC Berkeley in order to achieve the cognitive and computational objectives of the project. How do we see? This simple question does not have a simple answer. Vision is a complex series of eve nts that begins when light enters the eyes and ends with perception. People are able to discriminate between objects of different size, contrast and color with precision. They can estimate curvature and orientation of surfaces with varying roughness and multitudes of texture, as well as describe within short time intervals properties of surfaces such as symmetry and similarity to other familiar objects. The human visual system easily outperforms any man-made machine. Decades of research in vision demonstrate the wisdom of the following approach: Key insights generally come from models that are well-suited for exploring a specific research question. Geometric models coupled with computational techniques have formed a cornerstone of modern theories of biological as well as robot vision, and of their diverse applications. In this project, the principal investigator and his colleagues establish a new link between advanced geometric theories in pure mathematics (theory of foliations from differential topology) and visual perception and estimation of shape of surfaces in natural and synthetic environments. Among applications of the theory, one could mention: robot motion planning and navigation of manless vehicles in rough terrain or unreachable environments; visual shape estimation of images of materials obtained by atomic force microscopy in scientific research and design of advanced materials; long-term computerized inspection of surfaces subject to ballistic deposition and erosion in environmental studies and ecology; and computational inspection of large databases of images from infrared radio astronomy in order to locate specific features. Just as the neurons in human visual system perform their tasks in parallel, the above-mentioned theory lends itself to parallel processing implementation.

Assadi 9707006 表面的视觉感知对于3D物体识别至关重要。 自然场景中的所有表面都具有纹理。 在这个项目中，研究人员开发新的算法来估计形状（表面的几何特征）从纹理在自然和合成场景。 新的几何模型使用分段黎曼叶理，在微分拓扑的意义上，具有额外的结构。 给定一个3D纹理表面，不一定是分段光滑的，人们构造一个叶状结构，其叶子形成一个单参数的2D纹理和分段光滑的数学表面族，近似给定的对象。 与在其叶子上定义的目标函数一起，这种2D纹理叶理是对世界上的3D纹理表面进行建模的基本几何对象。 人们可以使用算法来从分段平滑的叶子的纹理恢复形状，例如曲率和倾斜。 几种基于场景的方法进行了探索，以构建纹理叶理，从分析（如汉密尔顿-雅可比方程）和拓扑技术（如可积分布）的统计估计方法。 与神经科学实验学家的同事一起探索了测试理论和比较不同算法的心理物理实验。 特别地，目标函数可以基于心理物理数据在数值上近似。 新模型适用于感知对称性。 问题的建模计算策略所采用的视觉皮层估计形状的纹理，并与新的计算算法的比较，追求。 研究人员概述了一个具体的培训计划和研究合作与他的资深同事在视觉和神经科学在加州大学伯克利分校，以实现该项目的认知和计算目标。 我们怎么看？ 这个简单的问题没有简单的答案。 视觉是一系列复杂的事件，从光线进入眼睛开始，到感知结束。 人们能够精确地区分不同大小、对比度和颜色的物体。 它们可以估计具有不同粗糙度和大量纹理的表面的曲率和方向，以及在短时间间隔内描述表面的属性，例如对称性和与其他熟悉物体的相似性。 人类的视觉系统很容易胜过任何人造机器。 几十年的视觉研究证明了以下方法的智慧：关键的见解通常来自非常适合探索特定研究问题的模型。 几何模型与计算技术相结合，形成了现代生物学和机器人视觉理论及其各种应用的基石。 在这个项目中，首席研究员和他的同事们建立了纯数学中的高级几何理论（微分拓扑学的叶理理论）与自然和合成环境中表面形状的视觉感知和估计之间的新联系。 在该理论的应用中，可以提到：在崎岖地形或无法到达的环境中无人驾驶车辆的机器人运动规划和导航;在先进材料的科学研究和设计中对原子力显微镜获得的材料图像进行视觉形状估计;在环境研究和生态学中对受到弹道沉积和侵蚀的表面进行长期计算机化检查;以及对红外射电天文学图像的大型数据库进行计算检查，以确定具体特征。 正如人类视觉系统中的神经元并行执行其任务一样，上述理论适用于并行处理实现。