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Image Understanding using Sparse Representations
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 prese...
Intelligent Software Defect Prediction
With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it ...
Dictionary Learning in Visual Computing
The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dict...
Combating Bad Weather Part I
Current vision systems are designed to perform in normal weather condition. However, no one can escape from severe weather conditions. Bad weather reduces scene contrast and visibility, which results in degradation in the performance of various computer vision algorithms such as object tracking, segmentation and recognition. Thus, current vision systems must include some mechanisms that enable them to perform up to the mark in bad weather conditions such as rain and fog. Rain causes the spatial and temporal intensity variations in images or video frames. These intensity changes are due to the random distribution and high velocities of the raindrops. Fog causes low contrast and whiteness in t...
Combating Bad Weather Part II
Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss...
Feminist AI
Chapters 5, 12, and 18 of this work are available under the terms of a CC BY-NC-ND 4.0 International open access licence. These parts of the work are free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Feminist AI: Critical Perspectives on Algorithms, Data and Intelligent Machines is the first volume to bring together leading feminist thinkers from across the disciplines to explore the impact of artificial intelligence (AI) and related data-driven technologies on human society. Recent years have seen both an explosion in AI systems and a corresponding rise in important critical analyses of these technologies. Central to...
Handbook of Variational Methods for Nonlinear Geometric Data
This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability measures. Applications can be found in biology, medicine, product engineering, geography and computer vision for instance. Variational methods on the other hand...
Embedded, Cyber-Physical, and IoT Systems
This Festschrift is in honor of Marilyn Wolf, on the occasion of her 60th birthday. Prof. Wolf is a renowned researcher and educator in Electrical and Computer Engineering, who has made pioneering contributions in all of the major areas in Embedded, Cyber-Physical, and Internet of Things (IoT) Systems. This book provides a timely collection of contributions that cover important topics related to Smart Cameras, Hardware/Software Co-Design, and Multimedia applications. Embedded systems are everywhere; cyber-physical systems enable monitoring and control of complex physical processes with computers; and IoT technology is of increasing relevance in major application areas, including factory auto...
Trusted Data, revised and expanded edition
How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data. Trusted Data describes a data architecture that places humans and their societal values at the center of the discussion. By involving people from all parts of the ecosystem of information, this new approach allows us to realize the benefits of data-driven algorithmic decision making while minimizing the risks and unintended consequences. It proposes a software architecture and legal framework for an Internet of Trusted Data that provides safe, secure access for everyone and protects against bias, unfairness, and other unintended effects. This a...
Reconstruction-Free Compressive Vision for Surveillance Applications
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, reco...
