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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Decoupled Active Contour (DAC) for Boundary Detection
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Decoupled Active Contour (DAC) for Boundary Detection

机译：用于边界检测的去耦有源轮廓（DAC）

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摘要

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.
机译：通过活动轮廓精确检测对象边界是计算机视觉中一个持续的研究主题。通过最小化内部（先前）和外部（图像测量）能量项的总和，大多数活动轮廓会收敛到某些所需轮廓。这种方法是优雅的，但是收敛速度慢，并且在存在噪声或复杂轮廓的情况下经常失收敛。为了解决这些限制，开发了一种去耦有源轮廓（DAC），它分别应用了两个能量项。从本质上讲，DAC包括测量更新步骤，采用隐马尔可夫模型（HMM）和Viterbi搜索，然后是单独的先验步骤，该步骤根据测量不确定性和非平稳先验的相对强度修改更新后的曲线。通过将测量和先前步骤分开，该算法不太可能失收敛；此外，与基于能量的迭代求解器相比，使用维特比优化器可使该方法收敛更快。结果清楚地表明，所提出的方法对噪声具有鲁棒性，可以捕获非常高的曲率区域，并且对轮廓初始化或参数设置的依赖有限。与其他五种已发布方法相比，并且在许多图像集中，发现DAC的速度更快，分割精度更高或相当。

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