Exact feature probabilities in images with occlusion.
Exact feature probabilities in images with occlusion.
复制标题
具有遮挡的图像中的精确特征概率。
DOI：
10.1167/10.14.42
复制
发表时间：
2010
影响因子：
1.8
通讯作者：
Pitkow,Xaq
中科院分区：
文献类型：
--
作者：
Pitkow,Xaq
为了理解我们视觉系统的计算，了解它进化来解释的自然环境也很重要。不幸的是，现有的视觉环境模型要么不现实，要么对于数学描述来说太复杂。在这里，我们描述了一个自然图像模型，并提出了图像特征和模型变量之间统计关系的数学解决方案。该模型描述的世界是由相互遮挡的独立、不透明、有纹理的对象组成的。这种简单的结构使我们能够计算在多个任意位置的点采样的图像值的联合概率分布，而无需近似。该结果可以转换为可观察图像特征之间以及导致这些特征的不可观察属性之间的概率关系，包括对象边界和相对深度。我们证明图像模型足以解释广泛的自然场景属性。最后，我们讨论这种自然场景的描述对视觉研究的影响。
To understand the computations of our visual system, it is important to understand also the natural environment it evolved to interpret. Unfortunately, existing models of the visual environment are either unrealistic or too complex for mathematical description. Here we describe a naturalistic image model and present a mathematical solution for the statistical relationships between the image features and model variables. The world described by this model is composed of independent, opaque, textured objects, which occlude each other. This simple structure allows us to calculate the joint probability distribution of image values sampled at multiple arbitrarily located points, without approximation. This result can be converted into probabilistic relationships between observable image features as well as between the unobservable properties that caused these features, including object boundaries and relative depth. We show that the image model is sufficient to explain a wide range of natural scene properties. Finally, we discuss the implications of this description of natural scenes for the study of vision.