Boosting is a class of machine learning methods based on the idea that a combination of
simple classifiers (obtained by a weak learner) can perform better than any of the simple …

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input
images, which is a domain-specific image restoration problem in the low-level computer …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

It is a challenging task to develop effective and efficient appearance models for robust object
tracking due to factors such as pose variation, illumination change, occlusion, and motion …

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled
Machine Vision) clearly and systematically presents the basic methodology of computer and …

We propose a method to address challenges in unconstrained face detection, such as
arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel …

Most current face recognition systems are designed for indoor, cooperative-user
applications. However, even in thus-constrained applications, most existing systems …

We investigate the problem of automatically labelling appearances of characters in TV or
film material. This is tremendously challenging due to the huge variation in imaged …

The purpose of this book is to provide an introduction to the theories and techniques of multi-
sensor data fusion. The book has been designed as a text for a one-semester graduate …

In this paper, we propose a new distinctive feature, called joint Haar-like feature, for
detecting faces in images. This is based on co-occurrence of multiple Haar-like features …