Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers.

Incremental N-mode SVD for large-scale multilinear generative models.

Tensor decomposition is frequently used in image processing and machine learning for its ability to express higher order characteristics of data. Among tensor decomposition methods, N-mode singular value decomposition (SVD) is widely used owing to its simplicity. However, the data dimension often becomes too large to perform N-mode SVD directly due to memory limitation.

Robust albedo estimation from a facial image with cast shadow under general unknown lighting.

Albedo estimation from a facial image is crucial for various computer vision tasks, such as 3-D morphable-model fitting, shape recovery, and illumination-invariant face recognition, but the currently available methods do not give good estimation results. Most methods ignore the influence of cast shadows and require a statistical model to obtain facial albedo. This paper describes a method for albedo estimation that makes combined use of image intensity and facial depth information for an image with cast shadows and general unknown light.

A New Biased Discriminant Analysis Using Composite Vectors for Eye Detection.

We propose a new biased discriminant analysis (BDA) using composite vectors for eye detection. A composite vector consists of several pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalization of the covariance of pixels.

Locating the reference point of symphysis pubis in axial CT images.

In this paper, we present an effective method to determine the reference point of symphysis pubis (SP) in an axial stack of CT images to facilitate image registration for pelvic cancer treatment. In order to reduce the computational time, the proposed method consists of two detection parts, the coarse detector, and the fine detector. The detectors check each image patch whether it contains the characteristic structure of SP.

Input feature selection for classification problems.

Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected.