Kimia Benjamin B
LEMS, Division of Engineering, Brown University, Providence, RI 02912, USA.
J Physiol Paris. 2003 Mar-May;97(2-3):155-90. doi: 10.1016/j.jphysparis.2003.09.003.
A key challenge underlying theories of vision is how the spatially restricted, retinotopically represented feature analysis can be integrated to form abstract, coordinate-free object models. A resolution likely depends on the use of intermediate-level representations which can on the one hand be populated by local features and on the other hand be used as atomic units underlying the formation of, and interaction with, object hypotheses. The precise structure of this intermediate representation derives from the varied requirements of a range of visual tasks which motivate a significant role for incorporating a geometry of visual form. The need to integrate input from features capturing surface properties such as texture, shading, motion, color, etc., as well as from features capturing surface discontinuities such as silhouettes, T-junctions, etc., implies a geometry which captures both regional and boundary aspects. Curves, as a geometric model of boundaries, have been extensively used as an intermediate representation in computational, perceptual, and physiological studies, while the use of the medial axis (MA) has been popular mainly in computer vision as a geometric region-based model of the interior of closed boundaries. We extend the traditional model of the MA to represent images, where each MA segment represents a region of the image which we call a visual fragment. We present a unified theory of perceptual grouping and object recognition where through various sequences of transformations of the MA representation, visual fragments are grouped in various configurations to form object hypotheses, and are related to stored models. The mechanisms underlying both the computation and the transformation of the MA is a lateral wave propagation model. Recent psychophysical experiments depicting contrast sensitivity map peaks at the medial axes of stimuli, and experiments on perceptual filling-in, and brightness induction and modulation, are consistent with both the use of an MA representation and a propagation-based scheme. Also, recent neurophysiological recordings in V1 correlate with the MA hypothesis and a horizontal propagation scheme. This evidence supports a geometric computational paradigm for processing sensory data where both dynamic in-plane propagation and feedforward-feedback connections play an integral role.
视觉理论的一个关键挑战在于，如何将空间受限的、视网膜拓扑表示的特征分析进行整合，以形成抽象的、无坐标的对象模型。一种解决方案可能依赖于使用中间层表示，这种表示一方面可以由局部特征填充，另一方面可以用作形成对象假设并与对象假设进行交互的原子单元。这种中间表示的精确结构源于一系列视觉任务的不同要求，这些要求促使在纳入视觉形式的几何结构方面发挥重要作用。整合来自捕捉表面属性（如纹理、阴影、运动、颜色等）的特征以及来自捕捉表面不连续性（如轮廓、T 型连接等）的特征的输入需求，意味着一种既捕捉区域又捕捉边界方面的几何结构。曲线作为边界的几何模型，在计算、感知和生理研究中被广泛用作中间表示，而中轴线（MA）的使用主要在计算机视觉中作为基于几何区域的封闭边界内部模型而流行。我们扩展了传统的中轴线模型来表示图像，其中每个中轴线段表示图像的一个区域，我们称之为视觉片段。我们提出了一种统一的感知分组和对象识别理论，通过中轴线表示的各种变换序列，视觉片段以各种配置进行分组以形成对象假设，并与存储的模型相关联。中轴线计算和变换的基础机制是横向波传播模型。最近描述刺激中轴线处对比敏感度图峰值的心理物理学实验、感知填充实验以及亮度诱导和调制实验，都与使用中轴线表示和基于传播的方案一致。此外，最近在 V1 区的神经生理学记录与中轴线假设和水平传播方案相关。这一证据支持了一种用于处理感官数据的几何计算范式，其中动态平面内传播和前馈 - 反馈连接都起着不可或缺的作用。
