Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Unmanned aerial vehicle video-based target tracking algorithm Using sparse representation

Target tracking based on unmanned aerial vehicle (UAV) video is a significant technique in intelligent urban surveillance systems for smart city applications, such as smart transportation, road traffic monitoring, inspection of stolen vehicle, etc. In this paper, a vision-based target tracking algorithm aiming at locating UAV-captured targets, like pedestrian and vehicle, is proposed using sparse representation theory. First of all, each target candidate is sparsely represented in the subspace spanned by a joint dictionary. Then, the sparse representation coefficient is further constrained by an L2 regularization based on the temporal consistency. To cope with the partial occlusion appearing in UAV videos, a Markov Random Field (MRF)-based binary support vector with contiguous occlusion constraint is introduced to our sparse representation model. For long-term tracking, the particle filter framework along with a dynamic template update scheme is designed. Both qualitative and quantitative experiments implemented on visible (Vis) and infrared (IR) UAV videos prove that the presented tracker can achieve better performances in terms of precision rate and success rate when compared with other state-of-the-art tracker

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

The development of optical projection tomography instrumentation and its application to in vivo three dimensional imaging of zebrafish

OPT is a three dimensional (3D) imaging technique that can produce 3D reconstructions of transparent samples, requiring only a widefield imaging system and sample rotation. OPT can be readily applied to chemically cleared samples, or to live transparent organisms such as nematodes or zebrafish. For preclinical imaging, there is a trade-off between optical accessibility and biological relevance to humans. Adult Danio rerio (zebrafish) represent a promising compromise, with greater homology to humans than smaller animals, and superior optical accessibility than mice. However, their size and physiology present a number of imaging challenges including non-negligible absorption and optical scattering, and limited time for image data acquisition if the fish are to be recovered for longitudinal studies. A key goal of this PhD thesis research was to develop OPT to address these challenges and improve in vivo imaging capabilities for this model organism. This thesis builds on previous work at Imperial where angularly multiplexed OPT using compressed sensing was developed and applied to in vivo imaging of a cancer-burdened adult zebrafish, with a sufficiently short OPT data acquisition time to allow recovery of the fish after anaesthesia. The previous cross-sectional study of this work was extended to a longitudinal study of cancer progression that I undertook. The volume and quality of data acquired in the longitudinal study presented a number of data processing challenges, which I addressed with improved automation of the data processing pipeline and with the demonstration that convolutional neural networks (CNN) could replace the iterative compressed sensing algorithm previously used to suppress artifacts when reconstructing undersampled OPT data sets. To address the issue of high attenuation through the centre of an adult zebrafish, I developed conformal-high-dynamic-range (C-HDR) OPT and demonstrated that it could provide sufficient dynamic range for brightfield imaging of such optically thick samples, noting that transmitted light images can provide anatomical context for fluorescence image data. To reduce the impact of optical scattering in OPT, I developed a parallelised quasi-confocal version of OPT called slice-illuminated OPT (slice-OPT) to reject scattered photons and demonstrated this with live zebrafish. To enable 3D imaging with short wave infrared (SWIR) light, without the requirement of an expensive Ge or InGaAs camera, I implemented a single pixel camera and demonstrated single-pixel OPT (SP-OPT) for the first time.Open Acces

A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence

Programa Oficial de Doctorado en Computación. 5009P01[Abstract] Despite the scientific and technological developments achieved during the last two decades in the hyperspectral field, some methodological, operational and conceptual issues have restricted the progress, promotion and popular dissemination of this technology. These shortcomings include the specialized knowledge required for the acquisition of hyperspectral images, the shortage of publicly accessible hyperspectral image repositories with reliable ground truth images or the lack of methodologies that allow for the adaptation of algorithms to particular user or application processing needs. The work presented here has the objective of contributing to the hyperspectral field with procedures for the automatic acquisition of hyperspectral scenes, including the hardware adaptation of our own imagers and the development of methods for the calibration and correction of the hyperspectral datacubes, the creation of a publicly available hyperspectral repository of well categorized and labeled images and the design and implementation of novel computational intelligence based processing techniques that solve typical issues related to the segmentation and denoising of hyperspectral images as well as sequences of them taking into account their temporal evolution.[Resumen] A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico, operacional y conceptual han restringido el progreso, difusión y popularización de esta tecnología, entre ellas, el conocimiento especializado requerido en la adquisición de imágenes hiperespectrales, la carencia de repositorios de imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades de procesamiento concretas. Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral con procedimientos para la adquisición automática de escenas hiperespectrales, incluyendo la adaptación hardware de cámaras hiperespectrales propias y el desarrollo de métodos para la calibración y corrección de cubos de datos hiperespectrales; la creación de un repositorio hiperespectral de acceso público con imágenes categorizadas y con verdades de terreno fiables; y el diseño e implementación de técnicas de procesamiento basadas en inteligencia computacional para la resolución de problemas típicamente relacionados con las tareas de segmentación y eliminación de ruido en imágenes estáticas y secuencias de imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo] A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización desta tecnoloxía, entre elas, o coñecemento especializado requirido na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías que posibiliten a adaptación de algoritmos a usuarios ou necesidades de procesamento concretas. Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral con procedementos para a adquisición automática de eicenas hiperespectrais, incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento de métodos para a calibración e corrección de cubos de datos hiperespectrais; a creación dun repositorio hiperespectral de acceso público con imaxes categorizadas e con verdades de terreo fiables; e o deseño e implementación de técnicas de procesamento baseadas en intelixencia computacional para a resolución de problemas tipicamente relacionado~ coas tarefas de segmentación e eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~ tendo en consideración a súa evolución temporal

Methods for Improving Signal to Noise Ratio in Raman Spectra

Raman microspectroscopy is an optoelectronic technique based on the inelastic scattering of light. This technique has been demonstrated to have potential to identify different materials based on subtle differences in the Raman spectral profile using various multivariate statistical classification tools. However, Raman scattering is an inherently weak process. Low photon counts coupled with non-ideal collection efficiencies means that Raman spectroscopy is vulnerable to noise. This makes system optimisations, as well as efficient and reliable noise removal, a necessity in sensitive applications such as chemical classification or diagnostics. Provided in this thesis are software and experimental methodologies to evaluate system performance, predict system performance under various conditions, and to identify the optimal system configuration/set-up in order to achieve the highest possible signal to noise ratio. Modelling methodologies presented in this thesis allow the user to systematically evaluate minimum acquisition times, optimise camera read-out modes, and predict system behaviour with alternative optical elements in order to maximise signal to noise ratio. The denosing algorithms presented in this thesis have been shown to provide superior signal to noise ratio when compared with their traditional counterparts. When compared with the double acquisition method, the proposed cosmic ray removal algorithm resulted in a 10% improvement. An algorithm that enhances Savitzky-Golay smoothing with maximum likelihood estimation produced spectra with up to double the signal to noise ratio when compared to the raw spectra and consistently outperformed the algorithms it was compared to. The use of reflective substrates is also investigated and was shown to approximately triple the collected Raman scatter when compared with transparent substrates. By utilising the methodologies detailed in this thesis it is possible to improve the efficiency of the Raman system in question

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing

Visual analysis and synthesis with physically grounded constraints

The past decade has witnessed remarkable progress in image-based, data-driven vision and graphics. However, existing approaches often treat the images as pure 2D signals and not as a 2D projection of the physical 3D world. As a result, a lot of training examples are required to cover sufficiently diverse appearances and inevitably suffer from limited generalization capability. In this thesis, I propose "inference-by-composition" approaches to overcome these limitations by modeling and interpreting visual signals in terms of physical surface, object, and scene. I show how we can incorporate physically grounded constraints such as scene-specific geometry in a non-parametric optimization framework for (1) revealing the missing parts of an image due to removal of a foreground or background element, (2) recovering high spatial frequency details that are not resolvable in low-resolution observations. I then extend the framework from 2D images to handle spatio-temporal visual data (videos). I demonstrate that we can convincingly fill spatio-temporal holes in a temporally coherent fashion by jointly reconstructing the appearance and motion. Compared to existing approaches, our technique can synthesize physically plausible contents even in challenging videos. For visual analysis, I apply stereo camera constraints for discovering multiple approximately linear structures in extremely noisy videos with an ecological application to bird migration monitoring at night. The resulting algorithms are simple and intuitive while achieving state-of-the-art performance without the need of training on an exhaustive set of visual examples

Reports on industrial information technology. Vol. 12

The 12th volume of Reports on Industrial Information Technology presents some selected results of research achieved at the Institute of Industrial Information Technology during the last two years.These results have contributed to many cooperative projects with partners from academia and industry and cover current research interests including signal and image processing, pattern recognition, distributed systems, powerline communications, automotive applications, and robotics