Abstract
The human visual environment is comprised of different surfaces that are distributed in space. The parts of a scene that are visible at any one time are governed by the occlusion of overlapping objects. In this work we consider "dead leaves" models, which replicate these occlusions when generating images by layering objects on top of each other. A dead leaves model is a generative model comprised of distributions for object position, shape, color and texture. An image is generated from a dead leaves model by sampling objects ("leaves") from these distributions until a stopping criterion is reached, usually when the image is fully covered or until a given number of leaves was sampled. Here, we describe a theoretical approach, based on previous work, to derive a Bayesian ideal observer for the partition of a given set of pixels based on independent dead leaves model distributions. Extending previous work, we provide step-by-step explanations for the computation of the posterior probability as well as describe factors that determine the feasibility of practically applying this computation. The dead leaves image model and the associated ideal observer can be applied to study segmentation decisions in a limited number of pixels, providing a principled upper-bound on performance, to which humans and vision algorithms could be compared.
Abstract (translated)
人类的视觉环境由分布在空间中的不同表面组成。在任何时候，场景中可见的部分受到重叠对象遮挡的影响。在这项工作中，我们考虑了“枯叶”模型，这种模型通过将物体一层层叠加来模拟这些遮挡效果，在生成图像时复制这些现象。“枯叶”模型是一种生成模型，由物体的位置、形状、颜色和纹理的分布组成。从“枯叶”模型中生成一张图像是通过从这些分布中抽样物体（“叶子”），直到达到停止标准为止，这个标准通常是当整张图片完全覆盖或达到了指定数量的“叶子”。在这篇文章中，我们基于先前的工作描述了一种理论方法，用于推导出一种贝叶斯理想观察者模型，该模型根据独立的“枯叶”模型分布来划分一组给定像素。扩展了以前的研究成果，我们提供了逐步解释后验概率计算的方法，并且还详细说明了决定实际应用这种计算可行性的因素。“枯叶”图像模型以及相关的理想观察者可以用于研究在有限数量的像素中的分割决策问题，这为性能提供了一个理论上的上限，人类和视觉算法可以与之进行比较。
URL
https://arxiv.org/abs/2512.05539
