Matching Items (97)
Recursive Bayesian Estimation on Projective Spaces: Theoretical Foundations and Practical Algorithms
Description
This thesis develops geometrically and statistically rigorous foundations for multivariate analysis and bayesian inference posed on grassmannian manifolds. Requisite to the development of key elements of statistical theory in a geometric realm are closed-form, analytic expressions for many differential geometric objects, e.g., tangent vectors, metrics, geodesics, volume forms. The first part of this thesis is devoted to a mathematical exposition of these. In particular, it leverages the classical work of Alan James to derive the exterior calculus of differential forms on special grassmannians for invariant measures with respect to which integration is permissible. Motivated by various multi-sensor remote sensing applications, the second part of this thesis describes the problem of recursively estimating the state of a dynamical system propagating on the Grassmann manifold. Fundamental to the bayesian treatment of this problem is the choice of a suitable probability distribution to a priori model the state. Using the Method of Maximum Entropy, a derivation of maximum-entropy probability distributions on the state space that uses the developed geometric theory is characterized. Statistical analyses of these distributions, including parameter estimation, are also presented. These probability distributions and the statistical analysis thereof are original contributions. Using the bayesian framework, two recursive estimation algorithms, both of which rely on noisy measurements on (special cases of) the Grassmann manifold, are the devised and implemented numerically. The first is applied to an idealized scenario, the second to a more practically motivated scenario. The novelty of both of these algorithms lies in the use of thederived maximumentropy probability measures as models for the priors. Numerical simulations demonstrate that, under mild assumptions, both estimation algorithms produce accurate and statistically meaningful outputs. This thesis aims to chart the interface between differential geometry and statistical signal processing. It is my deepest hope that the geometric-statistical approach underlying this work facilitates and encourages the development of new theories and new computational methods in geometry. Application of these, in turn, will bring new insights and bettersolutions to a number of extant and emerging problems in signal processing.
ContributorsCrider, Lauren N (Author) / Cochran, Douglas (Thesis advisor) / Kotschwar, Brett (Committee member) / Scharf, Louis (Committee member) / Taylor, Thomas (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The field of Computer Vision has seen great accomplishments in the last decade due to the advancements in Deep Learning. With the advent of Convolutional Neural Networks, the task of image classification has achieved unimaginable success when perceived through the traditional Computer Vision lens. With that being said, the state-of-the-art results in the image classification task were produced under a closed set assumption i.e. the input samples and the target datasets have knowledge of class labels in the testing phase. When any real-world scenario is considered, the model encounters unknown instances in the data. The task of identifying these unknown instances is called Open-Set Classification. This dissertation talks about the detection of unknown classes and the classification of the known classes. The problem is approached by using a neural network architecture called Deep Hierarchical Reconstruction Nets (DHRNets). It is dealt with by leveraging the reconstruction part of the DHRNets to identify the known class labels from the data. Experiments were also conducted on Convolutional Neural Networks (CNN) on the basis of softmax probability, Autoencoders on the basis of reconstruction loss, and Mahalanobis distance on CNN's to approach this problem.
ContributorsAinala, Kalyan (Author) / Turaga, Pavan (Thesis advisor) / Moraffah, Bahman (Committee member) / Demakethepalli Venkateswara, Hemanth Kumar (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the rapidly evolving field of computer vision, propelled by advancements in deeplearning, the integration of hardware-software co-design has become crucial to overcome
the limitations of traditional imaging systems. This dissertation explores the integration
of hardware-software co-design in computational imaging, particularly in light transport
acquisition and Non-Line-of-Sight (NLOS) imaging. By leveraging projector-camera
systems and computational techniques, this thesis address critical challenges in imaging
complex environments, such as adverse weather conditions, low-light scenarios, and the
imaging of reflective or transparent objects.
The first contribution in this thesis is the theory, design, and implementation of a slope
disparity gating system, which is a vertically aligned configuration of a synchronized
raster scanning projector and rolling-shutter camera, facilitating selective imaging through
disparity-based triangulation. This system introduces a novel, hardware-oriented approach
to selective imaging, circumventing the limitations of post-capture processing.
The second contribution of this thesis is the realization of two innovative approaches
for spotlight optimization to improve localization and tracking for NLOS imaging. The
first approach utilizes radiosity-based optimization to improve 3D localization and object
identification for small-scale laboratory settings. The second approach introduces a learningbased
illumination network along with a differentiable renderer and NLOS estimation
network to optimize human 2D localization and activity recognition. This approach is
validated on a large, room-scale scene with complex line-of-sight geometries and occluders.
The third contribution of this thesis is an attention-based neural network for passive
NLOS settings where there is no controllable illumination. The thesis demonstrates realtime,
dynamic NLOS human tracking where the camera is moving on a mobile robotic platform. In addition, this thesis contains an appendix featuring temporally consistent
relighting for portrait videos with applications in computer graphics and vision.
ContributorsChandran, Sreenithy (Author) / Jayasuriya, Suren (Thesis advisor) / Turaga, Pavan (Committee member) / Dasarathy, Gautam (Committee member) / Kubo, Hiroyuki (Committee member) / Arizona State University (Publisher)
Created2024
Description
Quantum computing has the potential to revolutionize the signal-processing field by providing more efficient methods for analyzing signals. This thesis explores the application of quantum computing in signal analysis synthesis for compression applications. More specifically, the study focuses on two key approaches: quantum Fourier transform (QFT) and quantum linear prediction (QLP). The research is motivated by the potential advantages offered by quantum computing in massive signal processing tasks and presents novel quantum circuit designs for QFT, quantum autocorrelation, and QLP, enabling signal analysis synthesis using quantum algorithms. The two approaches are explained as follows. The Quantum Fourier transform (QFT) demonstrates the potential for improved speed in quantum computing compared to classical methods. This thesis focuses on quantum encoding of signals and designing quantum algorithms for signal analysis synthesis, and signal compression using QFTs. Comparative studies are conducted to evaluate quantum computations for Fourier transform applications, considering Signal-to-Noise-Ratio results. The effects of qubit precision and quantum noise are also analyzed. The QFT algorithm is also developed in the J-DSP simulation environment, providing hands-on laboratory experiences for signal-processing students. User-friendly simulation programs on QFT-based signal analysis synthesis using peak picking, and perceptual selection using psychoacoustics in the J-DSP are developed. Further, this research is extended to analyze the autocorrelation of the signal using QFTs and develop a quantum linear prediction (QLP) algorithm for speech processing applications. QFTs and IQFTs are used to compute the quantum autocorrelation of the signal, and the HHL algorithm is modified and used to compute the solutions of the linear equations using quantum computing. The performance of the QLP algorithm is evaluated for system identification, spectral estimation, and speech analysis synthesis, and comparisons are performed for QLP and CLP results. The results demonstrate the following: effective quantum circuits for accurate QFT-based speech analysis synthesis, evaluation of performance with quantum noise, design of accurate quantum autocorrelation, and development of a modified HHL algorithm for efficient QLP. Overall, this thesis contributes to the research on quantum computing for signal processing applications and provides a foundation for further exploration of quantum algorithms for signal analysis synthesis.
ContributorsSharma, Aradhita (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation presents novel solutions for improving the generalization capabilities of deep learning based computer vision models. Neural networks are known to suffer a large drop in performance when tested on samples from a different distribution than the one on which they were trained. The proposed solutions, based on latent space geometry and meta-learning, address this issue by improving the robustness of these models to distribution shifts. Through the use of geometrical alignment, state-of-the-art domain adaptation and source-free test-time adaptation strategies are developed. Additionally, geometrical alignment can allow classifiers to be progressively adapted to new, unseen test domains without requiring retraining of the feature extractors. The dissertation also presents algorithms for enabling in-the-wild generalization without needing access to any samples from the target domain. Other causes of poor generalization, such as data scarcity in critical applications and training data with high levels of noise and variance, are also explored. To address data scarcity in fine-grained computer vision tasks such as object detection, novel context-aware augmentations are suggested. While the first four chapters focus on general-purpose computer vision models, strategies are also developed to improve robustness in specific applications. The efficiency of training autonomous agents for visual navigation is improved by incorporating semantic knowledge, and the integration of domain experts' knowledge allows for the realization of a low-cost, minimally invasive generalizable automated rehabilitation system. Lastly, new tools for explainability and model introspection using counter-factual explainers trained through interval-based uncertainty calibration objectives are presented.
ContributorsThopalli, Kowshik (Author) / Turaga, Pavan (Thesis advisor) / Thiagarajan, Jayaraman J (Committee member) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
In the era of data explosion, massive data is generated from various sources at an unprecedented speed. The ever-growing amount of data reveals enormous opportunities for developing novel data-driven solutions to unsolved problems. In recent years, benefiting from numerous public datasets and advances in deep learning, data-driven approaches in the computer vision domain have demonstrated superior performance with high adaptability on various data and tasks. Meanwhile, signal processing has long been dominated by techniques derived from rigorous mathematical models built upon prior knowledge of signals. Due to the lack of adaptability to real data and applications, model-based methods often suffer from performance degradation and engineering difficulties. In this dissertation, multiple signal processing problems are studied from vision-inspired data representation and learning perspectives to address the major limitation on adaptability. Corresponding data-driven solutions are proposed to achieve significantly improved performance over conventional solutions. Specifically, in the compressive sensing domain, an open-source image compressive sensing toolbox and benchmark to standardize the implementation and evaluation of reconstruction methods are first proposed. Then a plug-and-play compression ratio adapter is proposed to enable the adaptability of end-to-end data-driven reconstruction methods to variable compression ratios. Lastly, the problem of transfer learning from images to bioelectric signals is experimentally studied to demonstrate the improved performance of data-driven reconstruction. In the image subsampling domain, task-adaptive data-driven image subsampling is studied to reduce data redundancy and retain information of interest simultaneously. In the semiconductor analysis domain, the data-driven automatic error detection problem is studied in the context of integrated circuit segmentation for the first time. In the light detection and ranging(LiDAR) camera calibration domain, the calibration accuracy degradation problem in low-resolution LiDAR scenarios is addressed with data-driven techniques.
ContributorsZhang, Zhikang (Author) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
Oftentimes, patients struggle to accurately describe their symptoms to medical professionals, which produces erroneous diagnoses, delaying and preventing treatment. My app, Augnosis, will streamline constructive communication between patient and doctor, and allow for more accurate diagnoses. The goal of this project was to create an app capable of gathering data on visual symptoms of facial acne and categorizing it to differentiate between diagnoses using image recognition and identification. “Augnosis”, is a combination of the words “Augmented Reality” and “Self-Diagnosis”, the former being the medium in which it is immersed and the latter detailing its functionality.
ContributorsGoyal, Nandika (Author) / Johnson, Mina (Thesis director) / Bryan, Chris (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
In this thesis, I explored the interconnected ways in which human experience can shape and be shaped by environments of the future, such as interactive environments and spaces, embedded with sensors, enlivened by advanced algorithms for sensor data processing. I have developed an abstract representational experience into the vast and continual journey through life that shapes how we can use sensory immersion. The experimental work was housed in the iStage: an advanced black box space in the School of Arts, Media, and Engineering, which consists of video cameras, motion capture systems, spatial audio systems, and controllable lighting and projector systems. The malleable and interactive space of the iStage transformed into a reflective tool in which to gain insight into the overall shared, but very individual, emotional odyssey. Additionally, I surveyed participants after engaging in the experience to better understand their perceptions and interpretations of the experience. With the responses of participants' experiences and collective reflection upon the project I can begin to think about future iterations and how they might contain applications in health and/or wellness.
ContributorsHaagen, Jordan (Author) / Turaga, Pavan (Thesis director) / Drummond Otten, Caitlin (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2022-05
Description
Simultaneous localization and mapping (SLAM) has traditionally relied on low-level geometric or optical features. However, these features-based SLAM methods often struggle with feature-less or repetitive scenes. Additionally, low-level features may not provide sufficient information for robot navigation and manipulation, leaving robots without a complete understanding of the 3D spatial world. Advanced information is necessary to address these limitations. Fortunately, recent developments in learning-based 3D reconstruction allow robots to not only detect semantic meanings, but also recognize the 3D structure of objects from a few images. By combining this 3D structural information, SLAM can be improved from a low-level approach to a structure-aware approach. This work propose a novel approach for multi-view 3D reconstruction using recurrent transformer. This approach allows robots to accumulate information from multiple views and encode them into a compact latent space. The resulting latent representations are then decoded to produce 3D structural landmarks, which can be used to improve robot localization and mapping.
ContributorsHuang, Chi-Yao (Author) / Yang, Yezhou (Thesis advisor) / Turaga, Pavan (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023
Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
ContributorsLiang, Chengxu (Author) / Ingalls, Todd (Thesis advisor) / Turaga, Pavan (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2023