Throughout much of machine vision’s early years the infrared imagery has suffered from return on investment despite its advantages over visual counterparts. Recently, the ?scal momentum has switched in favor of both manufacturers and practitioners of infrared technology as a result of today’s rising security and safety challenges and advances in thermographic sensors and their continuous drop in costs. This yielded a great impetus in achieving ever better performance in remote surveillance, object recognition, guidance, noncontact medical measurements, and more. The purpose of this book is to draw attention to recent successful efforts made on merging computer vision applications (nonmilitary only) and nonvisual imagery, as well as to ?ll in the need in the literature for an up-to-date convenient reference on machine vision and infrared technologies. Augmented Perception in Infrared provides a comprehensive review of recent deployment of infrared sensors in modern applications of computer vision, along with in-depth description of the world’s best machine vision algorithms and intel- gent analytics. Its topics encompass many disciplines of machine vision, including remote sensing, automatic target detection and recognition, background modeling and image segmentation, object tracking, face and facial expression recognition, - variant shape characterization, disparate sensors fusion, noncontact physiological measurements, night vision, and target classi?cation. Its application scope includes homeland security, public transportation, surveillance, medical, and military. Mo- over, this book emphasizes the merging of the aforementioned machine perception applications and nonvisual imaging in intensi?ed, near infrared, thermal infrared, laser, polarimetric, and hyperspectral bands.

Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges.

The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material on development of computers for gaining understanding about videos and digital images. Highlighting a range of topics, such as computational models, machine learning, and image processing, this multi-volume book is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field.

Although the technological boom has resulted in many advancements in modern society, it has also come with a few downfalls; most notably, the failure of new machines and equipment. This has prompted engineers to find suitable diagnostics tools to stop impending malfunctions and make working environments more efficient. Recent Advances in Applied Thermal Imaging for Industrial Applications is a critical reference source that outlines innovative analysis tools to combat systems failure in thermal imaging. Highlighting pertinent topics such as fuzzy c- means technique, human health diagnosis system, multidimensional processing, and optical analysis, this is an ideal resource for all engineers, practitioners, industry leaders, and researchers who are interested in staying up-to-date with advances in thermal imaging which prevents industrial system malfunctions.

This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition.

The two volume set LNCS 10072 and LNCS 10073 constitutes the refereed proceedings of the 12th International Symposium on Visual Computing, ISVC 2016, held in Las Vegas, NV, USA in December 2016. The 102 revised full papers and 34 poster papers presented in this book were carefully reviewed and selected from 220 submissions. The papers are organized in topical sections: Part I (LNCS 10072) comprises computational bioimaging; computer graphics; motion and tracking; segmentation; pattern recognition; visualization; 3D mapping; modeling and surface reconstruction; advancing autonomy for aerial robotics; medical imaging; virtual reality; computer vision as a service; visual perception and robotic systems; and biometrics. Part II (LNCS 9475): applications; visual surveillance; computer graphics; and virtual reality.

Deception is a ubiquitous phenomenon in social interactions and has attracted a significant amount of research during the last decades. The majority of studies in this field focused on how deception modulates behavioral, autonomic, and brain responses and whether these changes can be used to validly identify lies. Especially the latter question, which historically gave rise to the development of psychophysiological “lie detection” techniques, has been driving research on deception and its detection until today. The detection of deception and concealed information in forensic examinations currently constitutes one of the most frequent applications of psychophysiological methods in the field. With the increasing use of such methods, the techniques for detecting deception have been controversially discussed in the scientific community. It has been proposed to shift from the original idea of detecting deception per se to a more indirect approach that allows for determining whether a suspect has specific knowledge of crime-related details. This so-called Concealed Information Test is strongly linked to basic psychological concepts concerning memory, attention, orienting, and response monitoring. Although research in this field has intensified with the advancement of neuroimaging techniques such as PET and fMRI in the last decade, basic questions on the psychological mechanisms underlying modulatory effects of deception and information concealment on behavioral, autonomic, and brain responses are still poorly understood. This Research Topic brings together contributions from researchers in experimental psychology, psychophysiology, and neuroscience focusing on the understanding of the broad concept of deception including the detection of concealed information, with respect to basic research questions as well as applied issues. This Research Topic is mainly composed of originalresearch articles but reviews and papers elaborating on novel methodological approaches have also been included. Experimental methods include, but are not limited to, behavioral, autonomic, electroencephalographic or brain imaging techniques that allow for revealing relevant facets of deception on a multimodal level. While this Research Topic primarily includes laboratory work, relevant issues for the field use of such methods are also discussed.