Complementary Dual Codes for Counter-measures to Side-Channel Attacks

We recall why linear codes with complementary duals (LCD codes) play a role in counter-measures to passive and active side-channel analyses on embedded cryptosystems. The rate and the minimum distance of such LCD codes must be as large as possible. We investigate primary constructions of such codes, in particular with cyclic codes, specifically with generalized residue codes, and we study their idempotents. We study those secondary constructions which preserve the LCD property, and we characterize conditions under which codes obtained by puncturing, shortening or extending codes, or obtained by the Plotkin sum, can be LCD

Material Recognition Meets 3D Reconstruction : Novel Tools for Efficient, Automatic Acquisition Systems

For decades, the accurate acquisition of geometry and reflectance properties has represented one of the major objectives in computer vision and computer graphics with many applications in industry, entertainment and cultural heritage. Reproducing even the finest details of surface geometry and surface reflectance has become a ubiquitous prerequisite in visual prototyping, advertisement or digital preservation of objects. However, today's acquisition methods are typically designed for only a rather small range of material types. Furthermore, there is still a lack of accurate reconstruction methods for objects with a more complex surface reflectance behavior beyond diffuse reflectance. In addition to accurate acquisition techniques, the demand for creating large quantities of digital contents also pushes the focus towards fully automatic and highly efficient solutions that allow for masses of objects to be acquired as fast as possible. This thesis is dedicated to the investigation of basic components that allow an efficient, automatic acquisition process. We argue that such an efficient, automatic acquisition can be realized when material recognition "meets" 3D reconstruction and we will demonstrate that reliably recognizing the materials of the considered object allows a more efficient geometry acquisition. Therefore, the main objectives of this thesis are given by the development of novel, robust geometry acquisition techniques for surface materials beyond diffuse surface reflectance, and the development of novel, robust techniques for material recognition. In the context of 3D geometry acquisition, we introduce an improvement of structured light systems, which are capable of robustly acquiring objects ranging from diffuse surface reflectance to even specular surface reflectance with a sufficient diffuse component. We demonstrate that the resolution of the reconstruction can be increased significantly for multi-camera, multi-projector structured light systems by using overlappings of patterns that have been projected under different projector poses. As the reconstructions obtained by applying such triangulation-based techniques still contain high-frequency noise due to inaccurately localized correspondences established for images acquired under different viewpoints, we furthermore introduce a novel geometry acquisition technique that complements the structured light system with additional photometric normals and results in significantly more accurate reconstructions. In addition, we also present a novel method to acquire the 3D shape of mirroring objects with complex surface geometry. The aforementioned investigations on 3D reconstruction are accompanied by the development of novel tools for reliable material recognition which can be used in an initial step to recognize the present surface materials and, hence, to efficiently select the subsequently applied appropriate acquisition techniques based on these classified materials. In the scope of this thesis, we therefore focus on material recognition for scenarios with controlled illumination as given in lab environments as well as scenarios with natural illumination that are given in photographs of typical daily life scenes. Finally, based on the techniques developed in this thesis, we provide novel concepts towards efficient, automatic acquisition systems

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

The goal of this project was to support the scientific analysis of multi-spectral astrophysical data by means of scientific visualization. Scientific visualization offers its greatest value if it is not used as a method separate or alternative to other data analysis methods but rather in addition to these methods. Together with quantitative analysis of data, such as offered by statistical analysis, image or signal processing, visualization attempts to explore all information inherent in astrophysical data in the most effective way. Data visualization is one aspect of data analysis. Our taxonomy as developed in Section 2 includes identification and access to existing information, preprocessing and quantitative analysis of data, visual representation and the user interface as major components to the software environment of astrophysical data analysis. In pursuing our goal to provide methods and tools for scientific visualization of multi-spectral astrophysical data, we therefore looked at scientific data analysis as one whole process, adding visualization tools to an already existing environment and integrating the various components that define a scientific data analysis environment. As long as the software development process of each component is separate from all other components, users of data analysis software are constantly interrupted in their scientific work in order to convert from one data format to another, or to move from one storage medium to another, or to switch from one user interface to another. We also took an in-depth look at scientific visualization and its underlying concepts, current visualization systems, their contributions, and their shortcomings. The role of data visualization is to stimulate mental processes different from quantitative data analysis, such as the perception of spatial relationships or the discovery of patterns or anomalies while browsing through large data sets. Visualization often leads to an intuitive understanding of the meaning of data values and their relationships by sacrificing accuracy in interpreting the data values. In order to be accurate in the interpretation, data values need to be measured, computed on, and compared to theoretical or empirical models (quantitative analysis). If visualization software hampers quantitative analysis (which happens with some commercial visualization products), its use is greatly diminished for astrophysical data analysis. The software system STAR (Scientific Toolkit for Astrophysical Research) was developed as a prototype during the course of the project to better understand the pragmatic concerns raised in the project. STAR led to a better understanding on the importance of collaboration between astrophysicists and computer scientists

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

Application of fMRI for action representation: decoding, aligning and modulating

Functional magnetic resonance imaging (fMRI) is an important tool for understanding neural mechanisms underlying human brain function. Understanding how the human brain responds to stimuli and how different cortical regions represent the information, and if these representational spaces are shared across brains and critical for our understanding of how the brain works. Recently, multivariate pattern analysis (MVPA) has a growing importance to predict mental states from fMRI data and to detect the coarse and fine scale neural responses. However, a major limitation of MVPA is the difficulty of aligning features across brains due to high variability in subjects’ responses and hence MVPA has been generally used as a subject specific analysis. Hyperalignment, solved this problem of feature alignment across brains by mapping neural responses into a common model to facilitate between subject classifications. Another technique of growing importance in understanding brain function is real-time fMRI Neurofeedback, which can be used to enable individuals to alter their brain activity. It facilitates people’s ability to learn control of their cognitive processes like motor control and pain by learning to modulate their brain activation in targeted regions. The aim of this PhD research is to decode and to align the motor representations of multi-joint arm actions based on different modalities of motor simulation, for instance Motor Imagery (MI) and Action Observation (AO) using functional Magnetic Resonance Imaging (fMRI) and to explore the feasibility of using a real-time fMRI neurofeedback to alter these action representations. The first experimental study of this thesis was performed on able-bodied participants to align the neural representation of multi-joint arm actions (lift, knock and throw) during MI tasks in the motor cortex using hyperalignment. Results showed that hyperalignment affords a statistically higher between-subject classification (BSC) performance compared to anatomical alignment. Also, hyperalignment is sensitive to the order in which subjects entered the hyperalignment algorithm to create the common model space. These results demonstrate the effectiveness of hyperalignment to align neural responses in motor cortex across subjects to enable BSC of motor imagery. The second study extended the use of hyperalignment to align fronto-parietal motor regions by addressing the problems of localization and cortical parcellation using cortex based alignment. Also, representational similarity analysis (RSA) was applied to investigate the shared neural code between AO+MI and MI of different actions. Results of MVPA revealed that these actions as well as their modalities can be decoded using the subject’s native or the hyperaligned neural responses. Furthermore, the RSA showed that AO+MI and MI representations formed separate clusters but that the representational organization of action types within these clusters was identical. These findings suggest that the neural representations of AO+MI and MI are neither the same nor totally distinct but exhibit a similar structural geometry with respect to different types of action. Results also showed that MI dominates in the AO+MI condition. The third study was performed on phantom limb pain (PLP) patients to explore the feasibility of using real-time fMRI neurofeedback to down-regulate the activity of premotor (PM) and anterior cingulate (ACC) cortices and whether the successful modulation will reduce the pain intensity. Results demonstrated that PLP patients were able to gain control and decrease the ACC and PM activation. Those patients reported decrease in the ongoing level of pain after training, but it was not statistically significant. The fourth study was conducted on healthy participants to study the effectiveness of fMRI neurofeedback on improving motor function by targeting Supplementary Motor Cortex (SMA). Results showed that participants learnt to up-regulate their SMA activation using MI of complex body actions as a mental strategy. In addition, behavioural changes, i.e. shortening of motor reaction time was found in those participants. These results suggest that fMRI neurofeedback can assist participants to develop greater control over motor regions involved in motor-skill learning and it can be translated into an improvement in motor function. In summary, this PhD thesis extends and validates the usefulness of hyperalignment to align the fronto-parietal motor regions and explores its ability to generalise across different levels of motor representation. Furthermore, it sheds light on the dominant role of MI in the AO+MI condition by examining the neural representational similarity of AO+MI and MI tasks. In addition, the fMRI neurofeedback studies in this thesis provide proof-of-principle of using this technology to reduce pain in clinical applications and to enhance motor functions in a healthy population, with the potential for translation into the clinical environment

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Reconstruction active et passive en vision par ordinateur

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal