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首页> 外文会议>Biennial Australian Pattern Recognition Society Conference(DICTA2003) v.2; 2003; Sydney; AU >Segmentation by Adaptive Prediction and Region Merging
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Segmentation by Adaptive Prediction and Region Merging

机译：通过自适应预测和区域合并进行分割

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摘要

This paper presents a segmentation technique based on prediction and adaptive region merging. While many techniques for segmentation exist, few of them are suited for the segmentation of natural images containing regular textures defined on non-rectangular segments. In this paper, we propose a description of regions based on a deconvolution algorithm whose purpose is to remove the influence of the shape on region contents. The decoupling of shape and texture information is achieved either by adapting waveforms to the segment shape, which is a time-consuming task that needs to be repeated for each segment shape, or by the extrapolation of a signal to fit a rectangular window, which is the chosen path. The deconvolution algorithm is the key of a new segmentation technique that uses extrapolation as a prediction of neighbouring regions. When the prediction of a region fits the actual content of a connected region reasonably well, both regions are merged. The segmentation process starts with an over-segmented image. It progressively merges neighbouring regions whose extrapolations fit according to an energy criterion. After each merge, the algorithm updates the values of the merging criterion for regions connected to the merged region pair. It stops when no further gain is achieved in merging regions or when mean values of adjacent regions are too different. Simulation results indicate that, although our technique is tailored for natural images containing periodic signals and flat regions, it is in fact usable for a large set of natural images.
机译：本文提出了一种基于预测和自适应区域合并的分割技术。尽管存在许多分割技术，但其中很少有一种适用于自然图像的分割，该自然图像包含在非矩形段上定义的规则纹理。在本文中，我们提出了一种基于反卷积算法的区域描述，其目的是消除形状对区域内容的影响。形状和纹理信息的解耦是通过使波形适应段形状来实现的，这是一项耗时的任务，需要针对每个段形状重复进行，或者通过对信号进行外推以适合矩形窗口来实现。选择的路径。反卷积算法是使用外推法预测邻近区域的新分割技术的关键。当区域的预测相当合理地适合连接区域的实际内容时，将两个区域合并。分割过程始于过度分割的图像。它会逐步合并根据能量标准外推适合的相邻区域。每次合并之后，该算法都会为连接到合并区域对的区域更新合并标准的值。当在合并区域中无法获得进一步的增益时，或者当相邻区域的平均值相差太大时，它将停止。仿真结果表明，尽管我们的技术是为包含周期信号和平坦区域的自然图像量身定制的，但实际上它可用于大量自然图像。

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