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局部最小自由参数化外观模型

Local Minima Free Parameterized Appearance Models
课程网址: http://videolectures.net/cmulls08_nguyen_lmf/  
主讲教师: Minh Hoai Nguyen
开课单位: 卡内基梅隆大学
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
参数化外观模型(PAM)(例如特征跟踪、活动外观模型、变形模型)通常用于对图像中对象的外观和形状变化进行建模。尽管与替代方法相比,PAM有许多优点,但它们至少有两个缺点。首先,它们在拟合过程中特别容易出现局部极小值。其次,成本函数的局部最小值通常很少与可接受的解对应。为了解决这些问题,本文提出了一种方法,通过显式优化,使局部极小值只出现在与正确拟合参数相对应的地方,从而学习成本函数。据我们所知,这是第一篇解决学习成本函数的问题的论文,明确地模拟了误差曲面的局部属性,以适应PAMS。综合实例和实际实例表明,与传统方法相比,对齐性能有所改善。
课程简介: Parameterized Appearance Models (PAMs) (e.g. Eigen-tracking, Active Appearance Models, Morphable Models) are commonly used to model the appearance and shape variation of objects in images. While PAMs have numerous advantages relative to alternate approaches, they have at least two drawbacks. First, they are especially prone to local minima in the fitting process. Second, often few if any of the local minima of the cost function correspond to acceptable solutions. To solve these problems, this paper proposes a method to learn a cost function by explicitly optimizing that the local minima occur at and only at the places corresponding to the correct fitting parameters. To the best of our knowledge, this is the first paper to address the problem of learning a cost function to explicitly model local properties of the error surface to fit PAMs. Synthetic and real examples show improvement in alignment performance in comparison with traditional approaches.
关 键 词: 参数化外观模型; 图像模拟; 机械学习
课程来源: 视频讲座网
最后编审: 2020-06-22:chenxin
阅读次数: 53