Pavan Turaga's Books

Reconstruction-Free Compressive Vision for Surveillance Applications

By: Henry Braun,Pavan Turaga,Andreas Spanias,Sameeksha Katoch,Suren Jayasuriya,Cihan Tepedelenlioglu

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Reconstruction-Free Compressive Vision for Surveillance Applications

By: Henry Braun,Pavan Turaga,Andreas Spanias,Sameeksha Katoch,Suren Jayasuriya,Cihan Tepedelenlioglu

Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.

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Springer Nature

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ISBN 10

3031025415

ISBN 13

9783031025419

Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition

By: Rama Chellappa,Aswin C. Sankaranarayanan,Ashok Veeraraghavan,Pavan Turaga

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Statistical Methods and Models for Video-based Tracking, Modeling, and Recognition

By: Rama Chellappa,Aswin C. Sankaranarayanan,Ashok Veeraraghavan,Pavan Turaga

Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms make use of statistical methods for robust inference. In this paper, we highlight the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, we illustrate the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems.

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165

Publisher

Now Publishers Inc

Published Date

ISBN 10

160198314X

ISBN 13

9781601983145

Image Understanding using Sparse Representations

By: Jayaraman J. Thiagarajan,Karthikeyan Natesan Ramamurthy,Pavan Turaga,Andreas Spanias

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Image Understanding using Sparse Representations

By: Jayaraman J. Thiagarajan,Karthikeyan Natesan Ramamurthy,Pavan Turaga,Andreas Spanias

Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.

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115

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Springer Nature

Published Date

ISBN 10

3031022505

ISBN 13

9783031022500

Machine Learning for Solar Array Monitoring, Optimization, and Control

By: Sunil Rao,Sameeksha Katoch,Vivek Narayanaswamy,Gowtham Muniraju,Cihan Tepedelenlioglu,Andreas Spanias,Pavan Turaga

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Machine Learning for Solar Array Monitoring, Optimization, and Control

By: Sunil Rao,Sameeksha Katoch,Vivek Narayanaswamy,Gowtham Muniraju,Cihan Tepedelenlioglu,Andreas Spanias,Pavan Turaga

The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of the photo-voltaic arrays under various conditions. We describe a project that includes development of machine learning and signal processing algorithms along with a solar array testbed for the purpose of PV monitoring and control. The 18kW PV array testbed consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. We develop machine learning and neural network algorithms for fault classification. In addition, we use weather camera data for cloud movement prediction using kernel regression techniques which serves as the input that guides topology reconfiguration. Camera and satellite sensing of skyline features as well as parameter sensing at each panel provides information for fault detection and power output optimization using topology reconfiguration achieved using programmable actuators (relays) in the SMDs. More specifically, a custom neural network algorithm guides the selection among four standardized topologies. Accuracy in fault detection is demonstrate at the level of 90+% and topology optimization provides increase in power by as much as 16% under shading.

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