Manifold learning in computer vision.
详细信息
- 作者：Park ; JinHyeong.
- 学历：Doctor
- 年：2005
- 导师：Kasturi, Rangachar
- 毕业院校：The Pennsylvania State University
- 专业：Computer Science.
- ISBN：0542359731
- CBH：3193223
- Country：USA
- 语种：English
- FileSize：5505637
- Pages：145
文摘
Appearance based learning has become very popular in the field of computer vision. In a particular system, a visual datum such as an image is usually treated as a vector by concatenating each row or column. The dimension of the image vector is very high, equal to the number of pixels of the image. When we consider a sequence of images, such video sequences or images capturing an object from different view points, it typically lies on a non-linear dimensional manifold, whose dimension is much lower than that of the original data. When we know the structure of the non-linear manifold, it can be very helpful in the field of computer vision for various applications such as dimensionality reduction, noise handling, etc.;In the first part of this thesis, we propose a method for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. This method can be used in conjunction with various manifold learning methods such as Isomap (Isometric Feature Map), LLE (Local Linear Embedding) and LTSA (Local Tangent Space Alignment) as a preprocessing step to obtain a more accurate reconstruction of the underlying nonlinear manifolds.;The second part of this thesis focuses on image occlusion handling utilizing manifold learning. We propose an algorithm to handle the problem of image occlusion using the Least Angle Regression (LARS) algorithm. LARS, which was proposed recently in the area of statistics, is known as a less greedy version of the traditional forward model selection algorithm. In other words, the LARS algorithm provides a family of image denoising results from one updated pixel to all of the updated pixels.;In the third part of this thesis, we propose a robust motion segmentation method using the techniques of matrix factorization, subspace separation and spectral graph partitioning. We first show that the shape interaction matrix can be derived using QR decomposition rather than Singular Value Decomposition (SVD) which also leads to a simple proof of the shape subspace separation theorem.