Graphical models for visual object recognition and tracking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 277-301).We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Such computer vision systems must be robust to wide variations in object appearance, the often small size of training databases, and ambiguities induced by articulated or partially occluded objects. Graphical models provide a powerful framework for encoding the statistical structure of visual scenes, and developing corresponding learning and inference algorithms. In this thesis, we describe several models which integrate graphical representations with nonparametric statistical methods. This approach leads to inference algorithms which tractably recover high-dimensional, continuous object pose variations, and learning procedures which transfer knowledge among related recognition tasks. Motivated by visual tracking problems, we first develop a nonparametric extension of the belief propagation (BP) algorithm. Using Monte Carlo methods, we provide general procedures for recursively updating particle-based approximations of continuous sufficient statistics. Efficient multiscale sampling methods then allow this nonparametric BP algorithm to be flexibly adapted to many different applications.(cont.) As a particular example, we consider a graphical model describing the hand's three-dimensional (3D) structure, kinematics, and dynamics. This graph encodes global hand pose via the 3D position and orientation of several rigid components, and thus exposes local structure in a high-dimensional articulated model. Applying nonparametric BP, we recover a hand tracking algorithm which is robust to outliers and local visual ambiguities. Via a set of latent occupancy masks, we also extend our approach to consistently infer occlusion events in a distributed fashion. In the second half of this thesis, we develop methods for learning hierarchical models of objects, the parts composing them, and the scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves accuracy when learning from few examples.(cont.) Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. Adapting these transformed Dirichlet processes to images taken with a binocular stereo camera, we learn integrated, 3D models of object geometry and appearance. This leads to a Monte Carlo algorithm which automatically infers 3D scene structure from the predictable geometry of known object categories.by Erik B. Sudderth.Ph.D

Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis

Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models. The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency

Multimodal learning from visual and remotely sensed data

Autonomous vehicles are often deployed to perform exploration and monitoring missions in unseen environments. In such applications, there is often a compromise between the information richness and the acquisition cost of different sensor modalities. Visual data is usually very information-rich, but requires in-situ acquisition with the robot. In contrast, remotely sensed data has a larger range and footprint, and may be available prior to a mission. In order to effectively and efficiently explore and monitor the environment, it is critical to make use of all of the sensory information available to the robot. One important application is the use of an Autonomous Underwater Vehicle (AUV) to survey the ocean floor. AUVs can take high resolution in-situ photographs of the sea floor, which can be used to classify different regions into various habitat classes that summarise the observed physical and biological properties. This is known as benthic habitat mapping. However, since AUVs can only image a tiny fraction of the ocean floor, habitat mapping is usually performed with remotely sensed bathymetry (ocean depth) data, obtained from shipborne multibeam sonar. With the recent surge in unsupervised feature learning and deep learning techniques, a number of previous techniques have investigated the concept of multimodal learning: capturing the relationship between different sensor modalities in order to perform classification and other inference tasks. This thesis proposes related techniques for visual and remotely sensed data, applied to the task of autonomous exploration and monitoring with an AUV. Doing so enables more accurate classification of the benthic environment, and also assists autonomous survey planning. The first contribution of this thesis is to apply unsupervised feature learning techniques to marine data. The proposed techniques are used to extract features from image and bathymetric data separately, and the performance is compared to that with more traditionally used features for each sensor modality. The second contribution is the development of a multimodal learning architecture that captures the relationship between the two modalities. The model is robust to missing modalities, which means it can extract better features for large-scale benthic habitat mapping, where only bathymetry is available. The model is used to perform classification with various combinations of modalities, demonstrating that multimodal learning provides a large performance improvement over the baseline case. The third contribution is an extension of the standard learning architecture using a gated feature learning model, which enables the model to better capture the ‘one-to-many’ relationship between visual and bathymetric data. This opens up further inference capabilities, with the ability to predict visual features from bathymetric data, which allows image-based queries. Such queries are useful for AUV survey planning, especially when supervised labels are unavailable. The final contribution is the novel derivation of a number of information-theoretic measures to aid survey planning. The proposed measures predict the utility of unobserved areas, in terms of the amount of expected additional visual information. As such, they are able to produce utility maps over a large region that can be used by the AUV to determine the most informative locations from a set of candidate missions. The models proposed in this thesis are validated through extensive experiments on real marine data. Furthermore, the introduced techniques have applications in various other areas within robotics. As such, this thesis concludes with a discussion on the broader implications of these contributions, and the future research directions that arise as a result of this work

Multipath assisted positioning using machine learning

The multipath propagation of the radio signal was considered a problem for positioning systems that had to be eliminated. However, a groundbreaking new approach called multipath assisted positioning caused a paradigm shift, where multipath propagation improves the positioning performance. Moreover, the multipath assisted positioning algorithm called Channel-SLAM shows the possibility of using a single physical transmitter in a multipath environment for positioning. In this thesis, I open a discussion on some problems that have vital importance for multipath assisted positioning algorithms with a focus on pedestrian positioning. Using the idea of multipath assisted positioning, I present a single frequency network positioning algorithm. I evaluated the single frequency network-based positioning algorithm for positioning in a real scenario using a terrestrial digital video broadcasting transmission. I propose a novel pedestrian transition model utilizing the inertial measurements from a handheld inertial measurement unit. The proposed pedestrian transition model improves the precision and reliability of the Channel-SLAM. Comparing the proposed transition model with the Rician transition model previously used in Channel-SLAM quantifies the performance improvement. This thesis proposes a joint data association technique that overcomes the strong dependence on the radio channel estimation algorithm used in Channel-SLAM. The joint data association allows reusing the previously observed virtual transmitters after an outage of multipath component tracking. The evaluation based on the walking pedestrian scenario shows that the joint data association algorithm provides superior positioning precision. The virtual transmitter position estimation yields a significant computational load in Channel-SLAM. I propose a method that represents the virtual transmitter by a Gaussian mixture model and learns its parameters. The evaluation shows that the proposed method outperforms the previous approach while decreasing the computational load. Also, the current methods for radio channel estimation yield a considerable computational load that prohibits a real-time deployment. The thesis investigates the possibility of using artificial neural networks trained to estimate the number of multipath components and corresponding delays in a noisy measurement of a channel impulse response. The artificial neural network-based delay estimator provides a superresolution performance and faster runtime than the classical approaches. The precision of the trained artificial neural network architecture is evaluated and compared to the Cramer-Rao lower bound theoretical limit and classical channel estimation algorithms

Bayesian image restoration and bacteria detection in optical endomicroscopy

Optical microscopy systems can be used to obtain high-resolution microscopic images of tissue cultures and ex vivo tissue samples. This imaging technique can be translated for in vivo, in situ applications by using optical fibres and miniature optics. Fibred optical endomicroscopy (OEM) can enable optical biopsy in organs inaccessible by any other imaging systems, and hence can provide rapid and accurate diagnosis in a short time. The raw data the system produce is difficult to interpret as it is modulated by a fibre bundle pattern, producing what is called the “honeycomb effect”. Moreover, the data is further degraded due to the fibre core cross coupling problem. On the other hand, there is an unmet clinical need for automatic tools that can help the clinicians to detect fluorescently labelled bacteria in distal lung images. The aim of this thesis is to develop advanced image processing algorithms that can address the above mentioned problems. First, we provide a statistical model for the fibre core cross coupling problem and the sparse sampling by imaging fibre bundles (honeycomb artefact), which are formulated here as a restoration problem for the first time in the literature. We then introduce a non-linear interpolation method, based on Gaussian processes regression, in order to recover an interpretable scene from the deconvolved data. Second, we develop two bacteria detection algorithms, each of which provides different characteristics. The first approach considers joint formulation to the sparse coding and anomaly detection problems. The anomalies here are considered as candidate bacteria, which are annotated with the help of a trained clinician. Although this approach provides good detection performance and outperforms existing methods in the literature, the user has to carefully tune some crucial model parameters. Hence, we propose a more adaptive approach, for which a Bayesian framework is adopted. This approach not only outperforms the proposed supervised approach and existing methods in the literature but also provides computation time that competes with optimization-based methods

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Image Analysis Algorithms for Single-Cell Study in Systems Biology

With the contiguous shift of biology from a qualitative toward a quantitative field of research, digital microscopy and image-based measurements are drawing increased interest. Several methods have been developed for acquiring images of cells and intracellular organelles. Traditionally, acquired images are analyzed manually through visual inspection. The increasing volume of data is challenging the scope of manual analysis, and there is a need to develop methods for automated analysis. This thesis examines the development and application of computational methods for acquisition and analysis of images from single-cell assays. The thesis proceeds with three different aspects.First, a study evaluates several methods for focusing microscopes and proposes a novel strategy to perform focusing in time-lapse imaging. The method relies on the nature of the focus-drift and its predictability. The study shows that focus-drift is a dynamical system with a small randomness. Therefore, a prediction-based method is employed to track the focus-drift overtime. A prototype implementation of the proposed method is created by extending the Nikon EZ-C1 Version 3.30 (Tokyo, Japan) imaging platform for acquiring images with a Nikon Eclipse (TE2000-U, Nikon, Japan) microscope.Second, a novel method is formulated to segment individual cells from a dense cluster. The method incorporates multi-resolution analysis with maximum-likelihood estimation (MAMLE) for cell detection. The MAMLE performs cell segmentation in two phases. The initial phase relies on a cutting-edge filter, edge detection in multi-resolution with a morphological operator, and threshold decomposition for adaptive thresholding. It estimates morphological features from the initial results. In the next phase, the final segmentation is constructed by boosting the initial results with the estimated parameters. The MAMLE method is evaluated with de novo data sets as well as with benchmark data from public databases. An empirical evaluation of the MAMLE method confirms its accuracy.Third, a comparative study is carried out on performance evaluation of state-ofthe-art methods for the detection of subcellular organelles. This study includes eleven algorithms developed in different fields for segmentation. The evaluation procedure encompasses a broad set of samples, ranging from benchmark data to synthetic images. The result from this study suggests that there is no particular method which performs superior to others in the test samples. Next, the effect of tetracycline on transcription dynamics of tetA promoter in Escherichia coli (E. coli ) cells is studied. This study measures expressions of RNA by tagging the MS2d-GFP vector with a target gene. The RNAs are observed as intracellular spots in confocal images. The kernel density estimation (KDE) method for detecting the intracellular spots is employed to quantify the individual RNA molecules.The thesis summarizes the results from five publications. Most of the publications are associated with different methods for imaging and analysis of microscopy. Confocal images with E. coli cells are targeted as the primary area of application. However, potential applications beyond the primary target are also made evident. The findings of the research are confirmed empirically

Analyse des ondes P et T des signaux ECG à l'aide de méthodes Bayésienne

Cette thèse a pour objet l étude de méthodes Bayésiennes pour l analyse des ondes P et T des signaux ECG. Différents modèles statistiques et des méthodes Bayésiennes associées sont proposés afin de réaliser la détection des ondes P et T et leur caractérisation (détermination du sommet et des limites des ondes ainsi que l estimation des formes d onde). Ces modèles prennent en compte des lois a priori pour les paramètres inconnus (les positions des ondes, les amplitudes et les coefficients de ces formes d'onde) associés aux signaux ECG. Ces lois a priori sont ensuite combinées avec la vraisemblance des données observées pour fournir les lois a posteriori des paramètres inconnus. En raison de la complexité des lois a posteriori obtenues, des méthodes de Monte Carlo par Chaînes de Markov sont proposées pour générer des échantillons distribués asymptotiquement suivant les lois d intérêt. Ces échantillons sont ensuite utilisés pour approcher les estimateurs Bayésiens classiques (MAP ou MMSE). D'autre part, pour profiter de la nature séquentielle du signal ECG, un modèle dynamique est proposé. Une méthode d'inférence Bayésienne similaire à celle développée précédemment et des méthodes de Monte Carlo séquentielles (SMC) sont ensuite étudiées pour ce modèle dynamique. Dans la dernière partie de ce travail, deux modèles Bayésiens introduits dans cette thèse sont adaptés pour répondre à un sujet de recherche clinique spécifique appelé détection de l'alternance des ondes T. Une des approches proposées a servi comme outil d'analyse dans un projet en collaboration avec St. Jude Medical, Inc et l'hôpital de Rangueil à Toulouse, qui vise à évaluer prospectivement la faisabilité de la détection des alternances des ondes T dans les signaux intracardiaques.This thesis studies Bayesian estimation/detection algorithms for P and T wave analysis in ECG signals. In this work, different statistical models and associated Bayesian methods are proposed to solve simultaneously the P and T wave delineation task (determination of the positions of the peaks and boundaries of the individual waves) and the waveform-estimation problem. These models take into account appropriate prior distributions for the unknown parameters (wave locations and amplitudes, and waveform coefficients). These prior distributions are combined with the likelihood of the observed data to provide the posterior distribution of the unknown parameters. Due to the complexity of the resulting posterior distributions, Markov chain Monte Carlo algorithms are proposed for (sample-based) detection/estimation. On the other hand, to take full advantage of the sequential nature of the ECG, a dynamic model is proposed under a similar Bayesian framework. Sequential Monte Carlo methods (SMC) are also considered for delineation and waveform estimation. In the last part of the thesis, two Bayesian models introduced in this thesis are adapted to address a specific clinical research problem referred to as T wave alternans (TWA) detection. One of the proposed approaches has served as an efficient analysis tool in the Endocardial T wave Alternans Study (ETWAS) project in collaboration with St. Jude Medical, Inc and Toulouse Rangueil Hospital. This project was devoted to prospectively assess the feasibility of TWA detection in repolarisation on EGM stored in ICD memories.TOULOUSE-INP (315552154) / SudocSudocFranceF