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

Three-dimensional Photoacoustic Tomography System Design Analysis and Optimization

Photoacoustic tomography (PAT) is an emerging imaging modality capable of mapping optical absorption in tissues. It is a hybrid technique that combines the high spatial resolution of ultrasound imaging with the high contrast of optical imaging, and has demonstrated much potential in biomedical applications. Conventional PAT systems employ raster scanning to capture a large number of projections, thus improving image reconstruction at the cost of temporal resolution. Arising from the desire for real-time 3D PA imaging, several groups have begun to design PAT systems with staring arrays, where image acquisition is only limited by the repetition rate of the laser. However, there has been little emphasis on staring array design analysis and optimization. We have developed objective figures of merit for PAT system performance and applied these metrics to improve system design. The results suggested that the developed approach could be used to objectively characterize and improve any PAT system design

Semi-automatic Solving of "Jigsaw puzzles" for Material Reconstruction of Dead Sea Scrolls

Digital solving of jigsaw puzzles have been well researched throughout the years and multiple approaches to solve them have been proposed. But these approaches have not been applied to reconstructing ancient manuscripts out of transient material such as leather or parchment. The literature describes ways to reconstruct ancient artefacts but they describe the process for more durable objects like pottery. In this thesis we explore the usability of the existing state-of-the-art methods for the purpose of aiding reconstructing of the Dead Sea Scrolls, also known as Qumran scrolls. Our experiments show that the existing methods as such do not provide good results in this domain, but with modifications provide help through a semi-automated reconstruction process. We expect these modifications and the software that was created as a by-product of this thesis to ease the researchers' work by automating the previously laborious manual work

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Matching 3D face scans using interest points and local histogram descriptors

n/