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A component-based object detection method extended with a fuzzy inference engine

机译:基于模糊推理引擎的基于组件的目标检测方法

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In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method.
机译:在本文中,我们提出了一种利用模糊推理技术扩展了基于组件的物体检测方法。该方法检测复杂对象的组成部分而不是图像中的整个对象。对于组件检测,多级支持向量机(SVM)并行使用。每个SVM使用不同的低级图像功能对候选组件进行分类。获得的结果融合以达到组件的决定。然后,模糊对象提取器确定考虑检测到的组件及其几何配置的整个对象。模糊物体提取器是模糊推理引擎,其测试检测分量的各种组合及其模糊的方向和距离。初步测试产生了有希望的结果,并鼓励进一步研究来扩展提出的方法。

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