Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Methods for the automatic alignment of colour histograms

Colour provides important information in many image processing tasks such as object identification and tracking. Different images of the same object frequently yield different colour values due to undesired variations in lighting and the camera. In practice, controlling the source of these fluctuations is difficult, uneconomical or even impossible in a particular imaging environment. This thesis is concerned with the question of how to best align the corresponding clusters of colour histograms to reduce or remove the effect of these undesired variations. We introduce feature based histogram alignment (FBHA) algorithms that enable flexible alignment transformations to be applied. The FBHA approach has three steps, 1) feature detection in the colour histograms, 2) feature association and 3) feature alignment. We investigate the choices for these three steps on two colour databases : 1) a structured and labeled database of RGB imagery acquired under controlled camera, lighting and object variation and 2) grey-level video streams from an industrial inspection application. The design and acquisition of the RGB image and grey-level video databases are a key contribution of the thesis. The databases are used to quantitatively compare the FBHA approach against existing methodologies and show it to be effective. FBHA is intended to provide a generic method for aligning colour histograms, it only uses information from the histograms and therefore ignores spatial information in the image. Spatial information and other context sensitive cues are deliberately avoided to maintain the generic nature of the algorithm; by ignoring some of this important information we gain useful insights into the performance limits of a colour alignment algorithm that works from the colour histogram alone, this helps understand the limits of a generic approach to colour alignment