判别式
人工智能
随机森林
计算机科学
特征(语言学)
模式识别(心理学)
生成模型
匹配(统计)
特征提取
投票
计算机视觉
航程(航空)
数学
生成语法
统计
哲学
语言学
材料科学
政治
政治学
法学
复合材料
作者
Claudia Lindner,Paul A. Bromiley,Mircea C. Ionita,Timothy F. Cootes
标识
DOI:10.1109/tpami.2014.2382106
摘要
A widely used approach for locating points on deformable objects in images is to generate feature response images for each point, and then to fit a shape model to these response images. We demonstrate that Random Forest regression-voting can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position of each point. We show that this leads to fast and accurate shape model matching when applied in the Constrained Local Model framework. We evaluate the technique in detail, and compare it with a range of commonly used alternatives across application areas: the annotation of the joints of the hands in radiographs and the detection of feature points in facial images. We show that our approach outperforms alternative techniques, achieving what we believe to be the most accurate results yet published for hand joint annotation and state-of-the-art performance for facial feature point detection.
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