This paper proposes an algorithm for rejecting false matches (known as outliers) in image pairs acquired with automobile-mounted cameras. Many intelligent vehicle applications req...
Jae Kyu Suhr, Ho Gi Jung, Kwanghyuk Bae, Jaihie Ki...
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
Outlier detection techniques are widely used in many applications such as credit card fraud detection, monitoring criminal activities in electronic commerce, etc. These application...
Standard median filters preserve abrupt shifts (edges) and remove impulsive noise (outliers) from a constant signal but they deteriorate in trend periods. Finite impulse response ...
Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads ...
Let P be a set of n points in Rd . A subset S of P is called a (k, )-kernel if for every direction, the direction width of S -approximates that of P, when k "outliers" c...
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from th...
The problem of noise in Monte-Carlo rendering arising from estimator variance is well-known and well-studied. In this work, we concentrate on identifying individual light paths as...
Christopher DeCoro, Tim Weyrich, Szymon Rusinkiewi...
Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA which is based on the estimation of the sample mean and covariance...
This paper presents a new dynamic generating graphical model for point-sets matching. The existing algorithms on graphical models proved to be quite robust to noise but are suscep...