Recurrent computations for the perceptual organization of shape

想象一个由颜色和纹理随机斑点组成的视觉世界，就像一幅抽象画。这是一个没有形状的世界，它说明了我们看到结构和识别物体的能力是如何由我们感知形状的能力决定的。在提议的项目中，我们将采用心理物理和计算方法的新颖组合来确定人类大脑如何从复杂图像的轮廓中提取和表示2D和3D形状信息，并将基于这些见解开发改进的计算机视觉算法，用于对象分割和形状处理。******我们感知形状的方式看似毫不费力，但背后却隐藏着令人生畏的复杂性。当你环顾四周时，你的视觉世界很可能是由部分遮挡的物体、光线和阴影组成的复杂混乱。这些复杂性将物体分割成感知碎片，大脑必须将这些碎片正确地组合在一起，才能计算出精确的形状表征。这种感知组织的过程是一个指数级复杂的组合问题，然而大脑可靠而有效地解决了这个问题，大大优于当前的计算机视觉算法。这种令人印象深刻的表现似乎源于大脑在循环分层神经结构中融合多个局部和全局分组线索的能力。这项工作的主要成果是一个详细的、可测试的神经回路计算模型，该模型由一组关键的新的心理物理实验提供信息。******自然形状通常会在高维形状空间中扫出低维弯曲流形。虽然计算机视觉的大部分工作都集中在分离这些流形的判别方法上，但需要一个完全生成的模型来支持感知分组和许多其他任务。因此，我们面临的挑战是如何确定有效但又能充分生成的形状模型。先前的工作受到拓扑不稳定性的挑战，但我们最近的形状形式理论解决了这个问题。提出的工作的第二个交付成果是二维形状的全概率图形形式模型，作为人类形状表示的心理物理学模型进行评估。******虽然大多数关于3D形状感知的研究都集中在形状的深度线索（如立体视觉）或表面线索（如阴影）上，但已知边界图像轮廓的2D形状也会强烈影响3D形状感知。该项目的第三个成果是一个新的概率模型，用于从边界轮廓估计3D形状，使用我们的形式理论的3D扩展来表示。******这些结果将大大促进我们对人类视觉皮层形状处理的理解。这里开发的算法将成为交通监控和2D到3D电影转换的自动视频分析应用的基础。

Imagine a visual world of random blotches of colour and texture, like an abstract painting. This is a world without shape, and it illustrates how our capacity to see structure and recognize objects is determined by our ability to perceive shape. In the proposed project we will employ a novel combination of psychophysical and computational methods to determine how the human brain extracts and represents 2D and 3D shape information from contours in complex imagery, and will develop improved computer vision algorithms for object segmentation and shape processing based upon these insights.******The seemingly effortless way in which we perceive shape belies a daunting complexity. As you gaze around you now your visual world is likely a complex clutter of partially occluded objects, variegated lighting and shadows. These complexities fragment objects into perceptual shards that the brain must correctly group together in order to compute accurate representations of shape. This process of perceptual organization is a combinatorial problem of exponential complexity, yet the brain solves it reliably and efficiently, vastly outperforming current computer vision algorithms. This impressive performance appears to derive from the brain's ability to fuse multiple local and global grouping cues within a recurrent hierarchical neural architecture. A major deliverable of the proposed work is a detailed and testable computational model of this neural circuit informed by a key set of new psychophysical experiments.******Natural shapes generally sweep out low-dimensional curved manifolds in a high dimensional shape space. While most effort in computer vision has focused on discriminative methods for separating these manifolds, a fully generative model is required to support perceptual grouping and many other tasks. The challenge is thus to identify efficient but fully generative models of shape. Prior work has been challenged by topological instability, but our recent formlet theory of shape solves this problem. A second deliverable of the proposed work is a fully probabilistic graphical formlet model of 2D shape, evaluated psychophysically as a model for human shape representation.******While most studies of 3D shape perception focus on depth cues to shape such as stereopsis, or surface cues such as shading, the 2D shape of the bounding image contour is also known to strongly influence 3D shape perception. A third deliverable of the project is a new probabilistic model for estimation of 3D shape from the bounding contour, represented using a 3D extension of our formlet theory.******These results will substantially advance our understanding of shape processing in human visual cortex. The algorithms developed here will form the basis for applications in automatic video analytics for traffic surveillance and for 2D to 3D film conversion.