Extracting and fusing discriminative features in fingerprint matching, especially in distorted fingerprint matching, is a challenging task. In this paper, we introduce two novel features to deal with nonlinear distortion in fingerprints. One is finger placement direction which is extracted from fingerprint foreground and the other is ridge compatibility which is determined by the singular value...

Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpose-built or off-the-shelf algorithms. We present an alternative approach to SVM fitting via the majorization–minimization (MM) paradigm. Algorithms that are derived via MM algorithm constructions can be shown to monoto...

Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution. In this paper, we present a robust method for highquality restoration of a degraded depth map with the guidance of the corresponding color image. We solve the problem in an energy optimization framework that consists of a n...

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process a...

Primary restless leg syndrome (RLS) is a common sensory-motor disorder that is characterized by an irresistible urge to move the limbs and unpleasant sensations in the legs, which affects 1.9%-4.6% adults. Pramipexole, a potent dopamine D2/3 agonist, is recommended as "effective" in the short-term and "possibly effective" in the long-term treatment of primary RLS in the European guidelines on m...

In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, Lq regularization with q ∈ (0, 1)) have been demonstrated to possess capability of gaining much benefit in sparsity-inducing and efficiency. As compared with the convex regularization approaches (say, L1 regularization), however, the convergence issue of the corresponding algorithms are more difficult t...

The new emerging theory of compressive sampling demonstrates that by exploiting the structure of a signal, it is possible to sample a signal below the Nyquist rate—using random projections—and achieve perfect reconstruction. In this paper, we consider a special case of compressive sampling where the uncompressed signal is non-negative, and propose a number of sparse recovery algorithms—which ut...

The analysis of images taken of the ground from aircraft and satellites is of intense importance. This chapter describes work using the ADAM neural network that was aimed at finding way-points, roads, towns and rural areas in Infra Red Line Scan images of the ground. The network is capable of on-line, single pass training and simple computer implementation, making it particularly applicable in ...

We consider the problem of binary class probability estimation (CPE) when one class is rare compared to the other. It is well known that standard algorithms such as logistic regression do not perform well in this setting as they tend to underestimate the probability of the rare class. Common fixes include under-sampling and weighting, together with various correction schemes. Recently, Wang & D...

We present a novel sparse signal reconstruction method “ISD”, aiming to achieve fast reconstruction and a reduced requirement on the number of measurements compared to the classical `1 minimization approach. ISD addresses failed reconstructions of `1 minimization due to insufficient measurements. It estimates a support set I from a current reconstruction and obtains a new reconstruction by solv...