Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Temporally Varying Weight Regression for Speech Recognition

Ph.DDOCTOR OF PHILOSOPH

Robot Learning from Demonstration in Robotic Assembly: A Survey

Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper

An integrated approach to feature compensation combining particle filters and Hidden Markov Models for robust speech recognition

The performance of automatic speech recognition systems often degrades in adverse conditions where there is a mismatch between training and testing conditions. This is true for most modern systems which employ Hidden Markov Models (HMMs) to decode speech utterances. One strategy is to map the distorted features back to clean speech features that correspond well to the features used for training of HMMs. This can be achieved by treating the noisy speech as the distorted version of the clean speech of interest. Under this framework, we can track and consequently extract the underlying clean speech from the noisy signal and use this derived signal to perform utterance recognition. Particle filter is a versatile tracking technique that can be used where often conventional techniques such as Kalman filter fall short. We propose a particle filters based algorithm to compensate the corrupted features according to an additive noise model incorporating both the statistics from clean speech HMMs and observed background noise to map noisy features back to clean speech features. Instead of using specific knowledge at the model and state levels from HMMs which is hard to estimate, we pool model states into clusters as side information. Since each cluster encompasses more statistics when compared to the original HMM states, there is a higher possibility that the newly formed probability density function at the cluster level can cover the underlying speech variation to generate appropriate particle filter samples for feature compensation. Additionally, a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously is also introduced to obtain good noise statistics. In this approach, the information available from clean speech tracking can be effectively used for noise estimation. The availability of dynamic noise information can enhance the robustness of the algorithm in case of large fluctuations in noise parameters within an utterance. Testing the proposed PF-based compensation scheme on the Aurora 2 connected digit recognition task, we achieve an error reduction of 12.15% from the best multi-condition trained models using this integrated PF-HMM framework to estimate the cluster-based HMM state sequence information. Finally, we extended the PFC framework and evaluated it on a large-vocabulary recognition task, and showed that PFC works well for large-vocabulary systems also.Ph.D

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica

Exploiting Structural Regularities and Beyond: Vision-based Localization and Mapping in Man-Made Environments

Image-based estimation of camera motion, known as visual odometry (VO), plays a very important role in many robotic applications such as control and navigation of unmanned mobile robots, especially when no external navigation reference signal is available. The core problem of VO is the estimation of the camera’s ego-motion (i.e. tracking) either between successive frames, namely relative pose estimation, or with respect to a global map, namely absolute pose estimation. This thesis aims to develop efficient, accurate and robust VO solutions by taking advantage of structural regularities in man-made environments, such as piece-wise planar structures, Manhattan World and more generally, contours and edges. Furthermore, to handle challenging scenarios that are beyond the limits of classical sensor based VO solutions, we investigate a recently emerging sensor — the event camera and study on event-based mapping — one of the key problems in the event-based VO/SLAM. The main achievements are summarized as follows. First, we revisit an old topic on relative pose estimation: accurately and robustly estimating the fundamental matrix given a collection of independently estimated homograhies. Three classical methods are reviewed and then we show a simple but nontrivial two-step normalization within the direct linear method that achieves similar performance to the less attractive and more computationally intensive hallucinated points based method. Second, an efficient 3D rotation estimation algorithm for depth cameras in piece-wise planar environments is presented. It shows that by using surface normal vectors as an input, planar modes in the corresponding density distribution function can be discovered and continuously tracked using efficient non-parametric estimation techniques. The relative rotation can be estimated by registering entire bundles of planar modes by using robust L1-norm minimization. Third, an efficient alternative to the iterative closest point algorithm for real-time tracking of modern depth cameras in ManhattanWorlds is developed. We exploit the common orthogonal structure of man-made environments in order to decouple the estimation of the rotation and the three degrees of freedom of the translation. The derived camera orientation is absolute and thus free of long-term drift, which in turn benefits the accuracy of the translation estimation as well. Fourth, we look into a more general structural regularity—edges. A real-time VO system that uses Canny edges is proposed for RGB-D cameras. Two novel alternatives to classical distance transforms are developed with great properties that significantly improve the classical Euclidean distance field based methods in terms of efficiency, accuracy and robustness. Finally, to deal with challenging scenarios that go beyond what standard RGB/RGB-D cameras can handle, we investigate the recently emerging event camera and focus on the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping