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Object Learning And Detection Using Evolutionary Deformable Models For Mobile Robot Navigation

机译：使用进化可变形模型进行移动机器人导航的对象学习和检测

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摘要

Deformable models have been studied in image analysis over the last decade and used for recognition of flexible or rigid templates under diverse viewing conditions. This article addresses the question of how to define a deformable model for a real-time color vision system for mobile robot navigation. Instead of receiving the detailed model definition from the user, the algorithm extracts and learns the information from each object automatically. How well a model represents the template that exists in the image is measured by an energy function. Its minimum corresponds to the model that best fits with the image and it is found by a genetic algorithm that handles the model deformation. At a later stage, if there is symbolic information inside the object, it is extracted and interpreted using a neural network. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented in this article, showing the effectiveness and capacity of the system.
机译：在过去的十年中，可变形模型已经在图像分析中进行了研究，并用于识别各种查看条件下的柔性或刚性模板。本文解决了如何为移动机器人导航的实时彩色视觉系统定义可变形模型的问题。该算法没有从用户那里接收详细的模型定义，而是自动从每个对象中提取并学习信息。模型通过能量函数来衡量图像中存在的模板的表现程度。它的最小值对应于最适合图像的模型，并且可以通过处理模型变形的遗传算法找到它。在稍后的阶段，如果对象内部有符号信息，则使用神经网络对其进行提取和解释。最终的感知模块已成功集成到复杂的导航系统中。本文介绍了实际环境中的各种实验结果，显示了该系统的有效性和容量。

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