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首页> 外文会议>International Workshop on Biometric Recognition Systems(IWBRS 2005); 20051022-23; Beijing(CN) >Enhance ASMs Based on AdaBoost-Based Salient Landmarks Localization and Confidence-Constraint Shape Modeling
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Enhance ASMs Based on AdaBoost-Based Salient Landmarks Localization and Confidence-Constraint Shape Modeling

机译：基于基于AdaBoost的显着地标定位和置信约束形状建模的ASM增强

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摘要

Active Shape Model (ASM) has been recognized as one of the typical methods for image understanding. Simply speaking, it iterates two steps: profile-based landmarks local searching, and statistics-based global shape modeling. We argue that the simple 1D profile matching may not localize landmarks accurately enough, and the unreliable localized landmarks will mislead the following shape matching. Considering these two problems, we propose to enhance ASM from two aspects: (1) in the landmarks local searching step, we introduce more efficient AdaBoost method to localize some salient landmarks instead of the relatively simple profile matching as in the traditional ASMs; (2) in the global shape modeling step, the confidences of the landmark localization are exploited to constrain the shape modeling and reconstruction procedure by not using those unreliably located landmarks to eliminate their negative effects. We experimentally show mat the proposed strategies can impressively improve the accuracy of the traditional ASMs.
机译：活动形状模型（ASM）已被认为是图像理解的典型方法之一。简单来说，它迭代了两个步骤：基于轮廓的地标局部搜索和基于统计的全局形状建模。我们认为简单的一维轮廓匹配可能无法足够准确地定位地标，而不可靠的本地化地标将误导以下形状匹配。考虑到这两个问题，我们建议从两个方面增强ASM：（1）在地标局部搜索步骤中，我们引入了更有效的AdaBoost方法来定位一些显着地标，而不是像传统ASM中那样相对简单的轮廓匹配； （2）在整体形状建模步骤中，通过不使用那些定位不可靠的地标来消除其负面影响，利用地标定位的置信度来约束形状建模和重建过程。我们通过实验证明了所提出的策略可以显着提高传统ASM的准确性。

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