IMPROVING EFFICIENCY AND SCALABILITY IN VISUAL SURVEILLANCE APPLICATIONS

We present four contributions to visual surveillance: (a) an action recognition method based on the characteristics of human motion in image space; (b) a study of the strengths of five regression techniques for monocular pose estimation that highlights the advantages of kernel PLS; (c) a learning-based method for detecting objects carried by humans requiring minimal annotation; (d) an interactive video segmentation system that reduces supervision by using occlusion and long term spatio-temporal structure information. We propose a representation for human actions that is based solely on motion information and that leverages the characteristics of human movement in the image space. The representation is best suited to visual surveillance settings in which the actions of interest are highly constrained, but also works on more general problems if the actions are ballistic in nature. Our computationally efficient representation achieves good recognition performance on both a commonly used action recognition dataset and on a dataset we collected to simulate a checkout counter. We study discriminative methods for 3D human pose estimation from single images, which build a map from image features to pose. The main difficulty with these methods is the insufficiency of training data due to the high dimensionality of the pose space. However, real datasets can be augmented with data from character animation software, so the scalability of existing approaches becomes important. We argue that Kernel Partial Least Squares approximates Gaussian Process regression robustly, enabling the use of larger datasets, and we show in experiments that kPLS outperforms two state-of-the-art methods based on GP. The high variability in the appearance of carried objects suggests using their relation to the human silhouette to detect them. We adopt a generate-and-test approach that produces candidate regions from protrusion, color contrast and occlusion boundary cues and then filters them with a kernel SVM classifier on context features. Our method exceeds state of the art accuracy and has good generalization capability. We also propose a Multiple Instance Learning framework for the classifier that reduces annotation effort by two orders of magnitude while maintaining comparable accuracy. Finally, we present an interactive video segmentation system that trades off a small amount of segmentation quality for significantly less supervision than necessary in systems in the literature. While applications like video editing could not directly use the output of our system, reasoning about the trajectories of objects in a scene or learning coarse appearance models is still possible. The unsupervised segmentation component at the base of our system effectively employs occlusion boundary cues and achieves competitive results on an unsupervised segmentation dataset. On videos used to evaluate interactive methods, our system requires less interaction time than others, does not rely on appearance information and can extract multiple objects at the same time

Hybrid Approach to Enhance Single Image Resolution

Microscopic analysis of images is more important for detail analysis of an image, Image super resolution (SR) reconstruction technique is increasing its attention from the image processing community, in the previous techniques, noise removal and smoothing techniques are used but image resolution improvement has been widely used in many applications such as remote sensing image, medical image, video surveillance and high definition television. The essential of image SR reconstruction technique is how to produce a clearly high resolution (HR) image from the information of one or several low resolution (LR) images. This project is dealing with hybrid approach of combining SWT and DWT to improve the resolution of the image by interpolation. The performance of the algorithm is compared with the PSNR, MSE

Scalable learning for geostatistics and speaker recognition

With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying data, but also have to be scalable to handle larger data collections. This thesis focuses on developing scalable and effective methods targeted towards different domains, geostatistics and speaker recognition in particular. Initially we focus on kernel based learning methods and develop a GPU based parallel framework for this class of problems. An improved numerical algorithm that utilizes the GPU parallelization to further enhance the computational performance of kernel regression is proposed. These methods are then demonstrated on problems arising in geostatistics and speaker recognition. In geostatistics, data is often collected at scattered locations and factors like instrument malfunctioning lead to missing observations. Applications often require the ability interpolate this scattered spatiotemporal data on to a regular grid continuously over time. This problem can be formulated as a regression problem, and one of the most popular geostatistical interpolation techniques, kriging is analogous to a standard kernel method: Gaussian process regression. Kriging is computationally expensive and needs major modifications and accelerations in order to be used practically. The GPU framework developed for kernel methods is extended to kriging and further the GPU's texture memory is better utilized for enhanced computational performance. Speaker recognition deals with the task of verifying a person's identity based on samples of his/her speech - "utterances". This thesis focuses on text-independent framework and three new recognition frameworks were developed for this problem. We proposed a kernelized Renyi distance based similarity scoring for speaker recognition. While its performance is promising, it does not generalize well for limited training data and therefore does not compare well to state-of-the-art recognition systems. These systems compensate for the variability in the speech data due to the message, channel variability, noise and reverberation. State-of-the-art systems model each speaker as a mixture of Gaussians (GMM) and compensate for the variability (termed "nuisance"). We propose a novel discriminative framework using a latent variable technique, partial least squares (PLS), for improved recognition. The kernelized version of this algorithm is used to achieve a state of the art speaker ID system, that shows results competitive with the best systems reported on in NIST's 2010 Speaker Recognition Evaluation

Kernel Feature Extraction Methods for Remote Sensing Data Analysis

Technological advances in the last decades have improved our capabilities of collecting and storing high data volumes. However, this makes that in some fields, such as remote sensing several problems are generated in the data processing due to the peculiar characteristics of their data. High data volume, high dimensionality, heterogeneity and their nonlinearity, make that the analysis and extraction of relevant information from these images could be a bottleneck for many real applications. The research applying image processing and machine learning techniques along with feature extraction, allows the reduction of the data dimensionality while keeps the maximum information. Therefore, developments and applications of feature extraction methodologies using these techniques have increased exponentially in remote sensing. This improves the data visualization and the knowledge discovery. Several feature extraction methods have been addressed in the literature depending on the data availability, which can be classified in supervised, semisupervised and unsupervised. In particular, feature extraction can use in combination with kernel methods (nonlinear). The process for obtaining a space that keeps greater information content is facilitated by this combination. One of the most important properties of the combination is that can be directly used for general tasks including classification, regression, clustering, ranking, compression, or data visualization. In this Thesis, we address the problems of different nonlinear feature extraction approaches based on kernel methods for remote sensing data analysis. Several improvements to the current feature extraction methods are proposed to transform the data in order to make high dimensional data tasks easier, such as classification or biophysical parameter estimation. This Thesis focus on three main objectives to reach these improvements in the current feature extraction methods: The first objective is to include invariances into supervised kernel feature extraction methods. Throughout these invariances it is possible to generate virtual samples that help to mitigate the problem of the reduced number of samples in supervised methods. The proposed algorithm is a simple method that essentially generates new (synthetic) training samples from available labeled samples. These samples along with original samples should be used in feature extraction methods obtaining more independent features between them that without virtual samples. The introduction of prior knowledge by means of the virtual samples could obtain classification and biophysical parameter estimation methods more robust than without them. The second objective is to use the generative kernels, i.e. probabilistic kernels, that directly learn by means of clustering techniques from original data by finding local-to-global similarities along the manifold. The proposed kernel is useful for general feature extraction purposes. Furthermore, the kernel attempts to improve the current methods because the kernel not only contains labeled data information but also uses the unlabeled information of the manifold. Moreover, the proposed kernel is parameter free in contrast with the parameterized functions such as, the radial basis function (RBF). Using probabilistic kernels is sought to obtain new unsupervised and semisupervised methods in order to reduce the number and cost of labeled data in remote sensing. Third objective is to develop new kernel feature extraction methods for improving the features obtained by the current methods. Optimizing the functional could obtain improvements in new algorithm. For instance, the Optimized Kernel Entropy Component Analysis (OKECA) method. The method is based on the Independent Component Analysis (ICA) framework resulting more efficient than the standard Kernel Entropy Component Analysis (KECA) method in terms of dimensionality reduction. In this Thesis, the methods are focused on remote sensing data analysis. Nevertheless, feature extraction methods are used to analyze data of several research fields whereas data are multidimensional. For these reasons, the results are illustrated into experimental sequence. First, the projections are analyzed by means of Toy examples. The algorithms are tested through standard databases with supervised information to proceed to the last step, the analysis of remote sensing images by the proposed methods

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

Design of Machine Learning Algorithms with Applications to Breast Cancer Detection

Machine learning is concerned with the design and development of algorithms and techniques that allow computers to 'learn' from experience with respect to some class of tasks and performance measure. One application of machine learning is to improve the accuracy and efficiency of computer-aided diagnosis systems to assist physician, radiologists, cardiologists, neuroscientists, and health-care technologists. This thesis focuses on machine learning and the applications to breast cancer detection. Emphasis is laid on preprocessing of features, pattern classification, and model selection. Before the classification task, feature selection and feature transformation may be performed to reduce the dimensionality of the features and to improve the classification performance. Genetic algorithm (GA) can be employed for feature selection based on different measures of data separability or the estimated risk of a chosen classifier. A separate nonlinear transformation can be performed by applying kernel principal component analysis and kernel partial least squares. Different classifiers are proposed in this work: The SOM-RBF network combines self-organizing maps (SOMs) and radial basis function (RBF) networks, with the RBF centers set as the weight vectors of neurons from the competitive layer of a trained SaM. The pairwise Rayleigh quotient (PRQ) classifier seeks one discriminating boundary by maximizing an unconstrained optimization objective, named as the PRQ criterion, formed with a set of pairwise const~aints instead of individual training samples. The strict 2-surface proximal (S2SP) classifier seeks two proximal planes that are not necessary parallel to fit the distribution of the samples in the original feature space or a kernel-defined feature space, by ma-ximizing two strict optimization objectives with a 'square of sum' optimization factor. Two variations of the support vector data description (SVDD) with negative samples (NSVDD) are proposed by involving different forms of slack vectors, which learn a closed spherically shaped boundary, named as the supervised compact hypersphere (SCH), around a set of samples in the target class. \Ve extend the NSVDDs to solve the multi-class classification problems based on distances between the samples and the centers of the learned SCHs in a kernel-defined feature space, using a combination of linear discriminant analysis and the nearest-neighbor rule. The problem of model selection is studied to pick the best values of the hyperparameters for a parametric classifier. To choose the optimal kernel or regularization parameters of a classifier, we investigate different criteria, such as the validation error estimate and the leave-out-out bound, as well as different optimization methods, such as grid search, gradient descent, and GA. By viewing the tuning problem of the multiple parameters of an 2-norm support vector machine (SVM) as an identification problem of a nonlinear dynamic system, we design a tuning system by employing the extended Kalman filter based on cross validation. Independent kernel optimization based on different measures of data separability are a~so investigated for different kernel-based classifiers. Numerous computer experiments using the benchmark datasets verify the theoretical results, make comparisons among the techniques in measures of classification accuracy or area under the receiver operating characteristics curve. Computational requirements, such as the computing time and the number of hyper-parameters, are also discussed. All of the presented methods are applied to breast cancer detection from fine-needle aspiration and in mammograms, as well as screening of knee-joint vibroarthrographic signals and automatic monitoring of roller bearings with vibration signals. Experimental results demonstrate the excellence of these methods with improved classification performance. For breast cancer detection, instead of only providing a binary diagnostic decision of 'malignant' or 'benign', we propose methods to assign a measure of confidence of malignancy to an individual mass, by calculating probabilities of being benign and malignant with a single classifier or a set of classifiers

The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning

A diverse number of tasks in computer vision and machine learning enjoy from representations of data that are compact yet discriminative, informative and robust to critical measurements. Two notable representations are offered by Region Covariance Descriptors (RCovD) and linear subspaces which are naturally analyzed through the manifold of Symmetric Positive Definite (SPD) matrices and the Grassmann manifold, respectively, two widely used types of Riemannian manifolds in computer vision. As our first objective, we examine image and video-based recognition applications where the local descriptors have the aforementioned Riemannian structures, namely the SPD or linear subspace structure. Initially, we provide a solution to compute Riemannian version of the conventional Vector of Locally aggregated Descriptors (VLAD), using geodesic distance of the underlying manifold as the nearness measure. Next, by having a closer look at the resulting codes, we formulate a new concept which we name Local Difference Vectors (LDV). LDVs enable us to elegantly expand our Riemannian coding techniques to any arbitrary metric as well as provide intrinsic solutions to Riemannian sparse coding and its variants when local structured descriptors are considered. We then turn our attention to two special types of covariance descriptors namely infinite-dimensional RCovDs and rank-deficient covariance matrices for which the underlying Riemannian structure, i.e. the manifold of SPD matrices is out of reach to great extent. %Generally speaking, infinite-dimensional RCovDs offer better discriminatory power over their low-dimensional counterparts. To overcome this difficulty, we propose to approximate the infinite-dimensional RCovDs by making use of two feature mappings, namely random Fourier features and the Nystrom method. As for the rank-deficient covariance matrices, unlike most existing approaches that employ inference tools by predefined regularizers, we derive positive definite kernels that can be decomposed into the kernels on the cone of SPD matrices and kernels on the Grassmann manifolds and show their effectiveness for image set classification task. Furthermore, inspired by attractive properties of Riemannian optimization techniques, we extend the recently introduced Keep It Simple and Straightforward MEtric learning (KISSME) method to the scenarios where input data is non-linearly distributed. To this end, we make use of the infinite dimensional covariance matrices and propose techniques towards projecting on the positive cone in a Reproducing Kernel Hilbert Space (RKHS). We also address the sensitivity issue of the KISSME to the input dimensionality. The KISSME algorithm is greatly dependent on Principal Component Analysis (PCA) as a preprocessing step which can lead to difficulties, especially when the dimensionality is not meticulously set. To address this issue, based on the KISSME algorithm, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Lastly, in line with the recent trend in metric learning, we devise end-to-end learning of a generic deep network for metric learning using our derivation

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Portugal SB13: contribution of sustainable building to meet EU 20-20-20 targets

Proceedings of the International Conference Portugal SB13: contribution of sustainable building to meet EU 20-20-20 targetsThe international conference Portugal SB13 is organized by the University of Minho, the Technical University of Lisbon and the Portuguese Chapter of the International Initiative for a Sustainable Built Environment in Guimarães, Portugal, from the 30th of October till the 1st of November 2013. This conference is included in the Sustainable Building Conference Series 2013-2014 (SB13-14) that are being organized all over the world. The event is supported by high prestige partners, such as the International Council for Research and Innovation in Building and Construction (CIB), the United Nations Environment Programme (UNEP), the International Federation of Consulting Engineers (FIDIC) and the International Initiative for a Sustainable Built Environment (iiSBE). Portugal SB13 is focused on the theme â Sustainable Building Contribution to Achieve the European Union 20-20-20 Targetsâ . These targets, known as the â EU 20-20-20â targets, set three key objectives for 2020: - A 20% reduction in EU greenhouse gas emissions from 1990 levels; - Raising the share of EU energy consumption produced from renewable resources to 20%; - A 20% improvement in the EU's energy efficiency. Building sector uses about 40% of global energy, 25% of global water, 40% of global resources and emit approximately 1/3 of the global greenhouse gas emissions (the largest contributor). Residential and commercial buildings consume approximately 60% of the worldâ s electricity. Existing buildings represent significant energy saving opportunities because their performance level is frequently far below the current efficiency potentials. Energy consumption in buildings can be reduced by 30 to 80% using proven and commercially available technologies. Investment in building energy efficiency is accompanied by significant direct and indirect savings, which help offset incremental costs, providing a short return on investment period. Therefore, buildings offer the greatest potential for achieving significant greenhouse gas emission reductions, at least cost, in developed and developing countries. On the other hand, there are many more issues related to the sustainability of the built environment than energy. The building sector is responsible for creating, modifying and improving the living environment of the humanity. Construction and buildings have considerable environmental impacts, consuming a significant proportion of limited resources of the planet including raw material, water, land and, of course, energy. The building sector is estimated to be worth 10% of global GDP (5.5 trillion EUR) and employs 111 million people. In developing countries, new sustainable construction opens enormous opportunities because of the population growth and the increasing prosperity, which stimulate the urbanization and the construction activities representing up to 40% of GDP. Therefore, building sustainably will result in healthier and more productive environments. The sustainability of the built environment, the construction industry and the related activities are a pressing issue facing all stakeholders in order to promote the Sustainable Development. The Portugal SB13 conference topics cover a wide range of up-to-date issues and the contributions received from the delegates reflect critical research and the best available practices in the Sustainable Building field. The issues presented include: - Nearly Zero Energy Buildings - Policies for Sustainable Construction - High Performance Sustainable Building Solutions - Design and Technologies for Energy Efficiency - Innovative Construction Systems - Building Sustainability Assessment Tools - Renovation and Retrofitting - Eco-Efficient Materials and Technologies - Urban Regeneration - Design for Life Cycle and Reuse - LCA of sustainable materials and technologies All the articles selected for presentation at the conference and published in these Proceedings, went through a refereed review process and were evaluated by, at least, two reviewers. The Organizers want to thank all the authors who have contributed with papers for publication in the proceedings and to all reviewers, whose efforts and hard work secured the high quality of all contributions to this conference. A special gratitude is also addressed to Eng. José Amarílio Barbosa and to Eng. Catarina Araújo that coordinated the Secretariat of the Conference. Finally, Portugal SB13 wants to address a special thank to CIB, UNEP, FIDIC and iiSBE for their support and wish great success for all the other SB13 events that are taking place all over the world