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Accelerated detection of image objects and their orientations with distance transforms

机译:随着距离变换加速检测图像对象及其方向

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A new approach is developed for detection of image objects and their orientations, based on distance transforms of intermediate level edge information(i.e., edge segments and vertices). Objects are modeled with edge segments and these edges are matched to edges extracted from an image by correlating spatially transformed versions of one with a distance transform of the other. Upper bounds on change in cross- correlation between edge maps and distance transforms are shown to be simple functions of change in translation and rotation. The process of computing the optimal object rotation at each possible translation can be accelerated by one to two orders of magnitude when these bounds are applied in conjunction with an object translation-rotation traversal strategy. Examples with detection and acceleration results demonstrate the robustness and high discriminatory power of the algorithm.
机译:基于中间级边缘信息的距离变换(即边缘段和顶点),开发了一种用于检测图像对象及其方向的新方法。对象用边缘段建模,并且这些边缘与从图像中提取的边缘匹配,通过将具有另一个距离变换的空间变换的版本相关联。边缘映射和距离变换之间的互相关变化的上限被认为是翻译和旋转变化的简单功能。计算每个可能的平移时的最佳对象旋转的过程可以在与对象转换旋转遍历策略结合应用这些边界时,通过一到两个级别加速。具有检测和加速度结果的示例展示了算法的鲁棒性和高辨别力。

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