Shahrokni Ali, Drummond Tom, Fleuret François, Fua Pascal
University of Reading, Reading, UK.
IEEE Trans Pattern Anal Mach Intell. 2009 Mar;31(3):570-6. doi: 10.1109/TPAMI.2008.236.
We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners which operate on image intensity discriminative features which are defined on small patches and fast to compute. A database which is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator which requires a ribbon like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2-D delineation and fast 3-D tracking and compare its performance with other existing methods for line search boundary detection.
我们介绍一种基于分类的方法来寻找遮挡纹理边界。该分类器由一组弱学习器组成，这些弱学习器基于在小图像块上定义且计算速度快的图像强度判别特征进行操作。一个旨在模拟自然图像中纹理物体数字化遮挡轮廓的数据库被用于训练弱学习器。然后，训练后的分类器分数被用于获得纹理过渡存在的概率模型，该模型可轻松用于在垂直于初始边界估计方向上的线搜索纹理边界检测。此方法速度快，因此适用于实时和交互式应用。它作为一种鲁棒估计器，需要一个带状搜索区域，并且能够处理复杂的纹理结构，而无需大量观测。我们在交互式二维轮廓描绘和快速三维跟踪的背景下展示了结果，并将其性能与其他现有的线搜索边界检测方法进行了比较。
