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Recognition of geons by parametric deformable contour models

机译:通过参数可变形轮廓模型识别泡碑

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This paper presents a novel approach to the detection and recognition of qualitative parts like geons from real 2D intensity images. Previous works relied on semi-local properties of either line drawings or good region segmentation. Here, in the framework of Model-Based Optimisation, whole geons or substrantial sub-parts are recognised by fitting parametric deformable contour models to the edge image by means of a Maximum A Posteriori estimation performed by Adaptive Simulated Anneai]ing, accounting for image clutter and limited occlusions. A number of experiments, carried out both on synthetic and real edge images, are presented.
机译:本文介绍了一种新颖的检测和识别来自真实2D强度图像的格子的定性部位的方法。 以前的作品依赖于线路图纸或良好区域分割的半本地属性。 这里,在基于模型的优化框架中,通过通过自适应模拟的Anneai的最大后验估计将参数变形轮廓模型拟合到边缘图像,通过Adaptive Simulated Anneai的最大估计来识别整个GeOns或副部件,占据图像杂乱 和有限的闭塞。 呈现了许多实验,在合成和实际边缘图像上进行。

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