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Cooperative Spatial Reasoning for Image Understanding

机译:图像理解的合作空间推理

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Spatial Reasoning, reasoning about spatial information (i.e. shape and spatial relations), is a crucial function of image understanding and computer vision systems. This paper proposes a novel spatial reasoning scheme for image understanding and demonstrates its utility and effectiveness in two different systems: region segmentation and aerial image understanding systems. The scheme is designed based on a so-called Multi-Agent/Cooperative Distributed Problem Solving Paradigm, where a group of intelligent agents cooperate with each other to fulfill a complicated task. The first part of the paper describes a cooperative distributed region segmentation system, where each region in an image is regarded as an agent. Starting from seed regions given at the initial stage, region agents deform their shapes dynamically so that the image is partitioned into mutually disjoint regions. The deformation of each individual region agent is realized by the snake algorithm14 and neighboring region agents cooperate with each other to find common region boundaries between them. In the latter part of the paper, we first give a brief description of the cooperative spatial reasoning method used in our aerial image understanding system SIGMA. In SIGMA, each recognized object such as a house and a road is regarded as an agent. Each agent generates hypotheses about its neighboring objects to establish spatial relations and to detect missing objects. Then, we compare its reasoning method with that used in the region segmentation system. We conclude the paper by showing further utilities of the Multi-Agent/Cooperative Distributed Problem Solving Paradigm for image understanding.
机译:空间推理,即有关空间信息(即形状和空间关系)的推理,是图像理解和计算机视觉系统的关键功能。本文提出了一种用于图像理解的新颖的空间推理方案,并展示了其在两种不同系统中的效用和有效性:区域分割和航空图像理解系统。该方案是基于所谓的多代理/协作分布式问题解决范例而设计的,其中一组智能代理相互协作以完成复杂的任务。本文的第一部分描述了一种协作式分布式区域分割系统,其中图像中的每个区域都被视为一个主体。从初始阶段指定的种子区域开始,区域代理会动态变形其形状,以便将图像划分为相互不相交的区域。每个单独的区域代理的变形是通过蛇形算法实现的,相邻的区域代理相互协作以找到它们之间的公共区域边界。在本文的后半部分,我们首先简要介绍在我们的航空影像理解系统SIGMA中使用的合作空间推理方法。在SIGMA中,每个识别的对象(例如房屋和道路)都被视为代理。每个代理生成关于其相邻对象的假设,以建立空间关系并检测丢失的对象。然后,我们将其推理方法与区域分割系统中使用的推理方法进行比较。最后,我们通过展示用于图像理解的多代理商/合作分布式问题解决范例的更多实用程序来结束本文。

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