11 谦言万语

尚未进行身份认证

研究总结规律,发现简单唯美!

等级
TA的下载次数 397

ruijie锐捷客户端

校园网必备武器,包括32位和64位的锐捷客户端安装程序

2014-05-27

java电子书集合

java电子书集合 java标准 编程指南 入门教程等

2014-03-12

bookmarks_14-3-10.html

bookmarks_14-3-10.html

2014-03-10

人脸检测相关文章

26篇人脸检测相关论文集合,SVM Adaboost等详尽解析。

2014-02-07

Supervised Descent Method and its Applications to Face Alignment

Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2 nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2 nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-ofthe-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface. 1. Introduction Mathematical optimization has a fundamental impact in solving many problems in computer vision. This fact is apparent by having a quick look into any major conference in computer vision, where a significant number of papers use optimization techniques. Many important problems in computer vision such as structure from motion, image alignment, optical flow, or camera calibration can be posed as solving a nonlinear optimization problem. There are a large number of different approaches to solve these continuous nonlinear optimization problems based on first and second order methods, such as gradient descent [1] for dimensionality reduction, Gauss-Newton for image alignment [22, 5, 14] or Levenberg-Marquardt for structure from motion [8]. “I am hungry. Where is the apple? Gotta do Gradient descent”

2014-01-14

查看更多

勋章 我的勋章
    暂无奖章