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Recently, the number of digital imaging systems incorporated into information display applications, such as military and industrial aerial reconnaissance, has increased rapidly. These imaging systems provide considerable flexibility for the processing and enhancement of information that otherwise might be unnoticed in conventional imaging systems. Many of the digital enhancement techniques, however, have not been subjected to systematic evaluations to examine their influence upon operationally relevant human task performance. This paper reports the findings of a segment of an ongoing research program designed to establish a digital image database, to standardize a set of experimental procedures of obtaining human performance data, and to relate these performance measures to various image display conditions. The image database consists of low-altitude aerial scenes of various military and. civilian installations. Original transparencies were digitized with a microdensitometer to generate the image database for magnetic tape storage. The digitized images were then degraded by blur and noise to simulate various levels of system resolution and system signal-to-noise ratio, respectively. Two experimental tasks were developed to assess the effects of digital image quality upon human performance characteristics of interest to the military reconnaissance community. An information extraction task required the human observers to answer a series of questions pertaining to the essential elements of information with each image. A subjective rating task required observers to estimate the extent of image interpretability. Using military photointerpreters as subjects, studies were conducted to assess the effects of image degradations (blur and noise) and image enhancement/restoration processing on human performance. The studies employed high-resolution, black-and-white CRT monitors to display the digital images. Results indicated that both blur and noise image degradations impaired interpretability of the imagery and that several enhancement/restoration processing techniques substantially improved interpretability of the imagery. These results provide useful information for users of digital imaging systems and for researchers to aid future developments of digital image processes. ; Ph. D.

Intro -- Copyright -- Contributors -- Table of Contents -- Preface -- Color keys -- Chapter 1: Photoshop Elements Features Overview -- What's New in Photoshop Elements 2023? -- How Elements is designed to work -- A typical workflow example -- How I like to edit images -- The five faces of Adobe Photoshop Elements 2023 -- The Home screen -- The Organizer -- The Catalog -- Photo Editor: Quick Edit mode -- Photo Editor: Guided Edit mode -- Photo Editor: Expert Edit mode -- Working with panels and the Panel Bin -- Panel Functions -- The Create and Share menus -- The Enhance Menu -- Working with video and Premiere Elements -- Create menu features -- Summary -- Chapter 2: Setting Up Photoshop Elements from Scratch -- Setting up a photo editing computer -- Importing media into the Elements Organizer -- Reviewing the media -- Media backup -- Organizing your work: Star rating photos -- Organizing your work: Picture search using Metadata -- Organizing your work: Keyword Tagging -- Organizing your work: Places -- Organizing your work: Events -- Organizing your work: People -- Understanding JPEG and RAW file formats -- Saving Files -- Saving Version Sets -- Managing catalogs -- Digitizing Photos with a Scanner -- Digitizing Photos with a Camera -- Color management: Understanding color spaces -- Color management: Color settings -- Color Management: Screen and printer calibration -- Organizer keyboard shortcuts -- Summary -- Chapter 3: The Basics of Image Editing -- The editing workflow and best practices -- Overview: editing RAW files -- Editing Raw files: the Basic tab -- Editing RAW files: the Detail (sharpening) tab -- Editing Raw files: Noise Reduction -- Editing Raw files: Other Features -- 10 ways to open a photo for editing -- Understanding picture resolution -- Cropping for better composition -- The Straighten tool.

Digital Image Processing in the United States -- Digital Image Processing in Japan -- An Automated Microscope for Digital Image Processing — Part I: Hardware -- An Automated Microscope for Digital Image Processing — Part II: Software -- Clinical Use of Automated Microscopes for Cell Analysis -- Multiband Microscanning Sensor -- Computer Synthesis of High Resolution Electron Micrographs -- Computer Processing of Electron Micrographs of DNA -- Significance Probability Mappings and Automated Interpretation of Complex Pictorial Scenes -- Intracavitary Beta-Ray Scanner and Image Processing for Localization of Early Uterine Cancer -- New Vistas in Medical Reconstruction Imagery -- Digital Image Processing for Medical Diagnoses Using Gamma Radionuclides and Heavy Ions from Cyclotrons -- Processing of RI-Angiocardiographic Images -- Bioimage Synthesis and Analysis from X-Ray, Gamma, Optical and Ultrasound Energy -- A Pap Smear Prescreening System: CYBEST -- Automatic Analysis and Interpretation of Cell Micrographs -- Multi-Layer Tomography Based on Three Stationary X-Ray Images -- Texture Analysis in Diagnostic Radiology -- Automated Diagnosis of the Congenital Dislocation of the Hip-Joint -- Boundary Detection in Medical Radiographs -- Feature Extraction and Quantitative Diagnosis of Gastric Roentgenograms -- Computer Processing of Chest X-Ray Images -- MINISCR-V2 — The Software System for Automated Interpretation of Chest Photofluorograms -- Automatic Recognition of Color Fundus Photographs -- Image Processing in Television Ophthalmoscopy -- Attendees -- Author Index.

Reconnaissance missions and satellites collect hundreds of images loaded with valuable information to be utilized by the Air Force. Intelligence operations must analyze these images to extract the information needed to help commanders make important decisions. No matter how obtained, images of this type are often degraded by noise due to disruptions such as atmospheric disturbances, optical system variations, motion, and large distance from the sensor to the source. This noise must be removed effectively to improve the quality of these images and ensure that the information contained in them can be correctly extracted. The Air Force relies on the critical information contained in these images and therefore effective noise removal is critical. Limitations in current image restoration techniques cause visually disturbing effects in reconstructed images. Therefore, a more effective algorithm for noise removal is required. This thesis explores the use of redundant wavelet-based multiresolution image processing and a priori information to enhance current image restoration techniques. Often on an airplane, tank or other military vehicle or building there may be a known symbol, which can be used as perfect a priori information to restore the entire image. An adaptive redundant discrete wavelet transform is trained using this a priori information. Constant and adaptive thresholds are used on the noisy image while in the wavelet domain. The quality of the reconstructed image is improved over non-adaptive techniques.

Intro -- Preface -- Organization -- Contents -- Stereotypes and Biases in HRI -- Women Are Funny: Influence of Apparent Gender and Embodiment in Robot Comedy -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Results -- 5 Discussion -- References -- Cross-Cultural Timeline of the History of Thought of the Artificial -- 1 Introduction -- 2 The Part Ways - West to East -- 2.1 Latin America -- 2.2 Europe and Western Culture -- 2.3 Middle East -- 2.4 India -- 2.5 East Asia -- 3 Discussion and Conclusion -- 3.1 The Four Criteria -- 3.2 Overall View -- References -- People's Perceptions of Gendered Robots Performing Gender Stereotypical Tasks -- 1 Introduction -- 1.1 Social Categorization and Stereotypes -- 1.2 Gender Stereotypes in HRI Research -- 2 Method -- 2.1 Stimuli -- 2.2 Procedure -- 2.3 Dependent Variables -- 2.4 Participants -- 3 Results -- 3.1 Trust -- 3.2 Social Perception -- 3.3 Humanness -- 4 General Discussion -- References -- Gender Revealed: Evaluating the Genderedness of Furhat's Predefined Faces -- 1 Introduction -- 2 Design -- 2.1 Measures and Procedure -- 2.2 Participants -- 3 Results -- 3.1 Perceived Femininity and Masculinity of Furhat's Faces -- 3.2 Facial Gender Cues -- 3.3 Perceived Communion and Agency of Gendered Robots -- 4 Discussion -- 5 Conclusions -- References -- Cultural Values, but not Nationality, Predict Social Inclusion of Robots -- 1 Introduction -- 1.1 Aim -- 2 Materials and Methods -- 2.1 Sample -- 2.2 Procedure -- 3 Results -- 3.1 Cyberball: Data Pre-Processing -- 3.2 The Effect of Cultural Differences on Social Inclusion of the Robot -- 3.3 The effect of Intentionality Attribution on Social Inclusion of the Robot -- 3.4 OSIO results -- 4 Discussion -- 5 Conclusions -- References -- Socially Intelligent Robots -- From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness.

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in ...

In the framework of remote sensing, infrared (IR) cameras allow the user to sense IR radiation, similar to common cameras that sense visible light. The radiation acquired from IR cameras is related to the temperature of the observed scene through the Planck's law, in fact IR cameras are also known as "thermal cameras" for their capability of detecting information concerning the heat of the observed scene. Thermal cameras were originally developed for military purposes to detect and track targets on complex scenarios as an alternative to radar systems, in that optical systems - due to their passive nature - are more robust to countermeasures for interception. Nevertheless, over the years IR cameras have been employed also in several civilian fields. In a military context, the images acquired from IR devices are processed to reveal the presence of targets that typically cannot be easily distinguished from both clutter, spatial and temporal noise sources. The aim of automatic target detection (ATD) techniques is to furnish detections to consider reliable in a statistical approach, i.e. in terms of probability of false-alarm and probability of detection. In order to improve ATD performance, appropriate de-noising techniques and clutter removal algorithms have to be developed as a pre-processing step, since the mitigation of both noise sources (spatial and temporal) and the accurate estimation of background clutter improve the performance of ATD. In this framework, this PhD thesis is focused on pre-processing techniques developed to improve the performance of ATD techniques. The first part of the thesis is focused on a de-noising problem known in the literature as non-uniformity correction (NUC) for last generation focal-plane arrays (FPAs) IR devices. Particular emphasis has been posed on scene-based techniques since NUC is operated with the only employment of the sensed radiation. A detailed overview of scene-based NUC techniques presented in the literature is carried out highlighting advantages and drawbacks. Focusing on the performance of the NUC scene-based techniques, the Scribner's algorithm has been deeply analyzed revealing high NUC performance combined with small computational load. In relation to this NUC technique, it has been introduced the problem of ghosting artifacts which is considered as a collateral effect emerging from scene-based NUC techniques based on a statistical approach. Novel methods to mitigate ghosting artifacts are presented in this PhD thesis: (i) Bilateral filter (BF)- based de-ghosting. The bilateral filter has been employed to operate accurate spatial estimates of the FPN; (ii) Temporal statistics (TS)-based de-ghosting. Temporal statistics are computed to predict the trend of an accurate estimate of the NUC parameters. The proposed techniques have been tested and evaluated in terms of de-ghosting capability and global NUC effectiveness by means of IR experimental data sets with simulated FPN. The second part of the PhD thesis concerns a complementary topic of data processing for ATD applications: background estimation and removal in IR images. It has been adopted a well-established scheme for target detection in IR surveillance systems, that is the cascade of background clutter removal plus a strategy of target detection. In such an ATD scheme, the overall detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this framework, it is proposed a novel method for the choice and setting of the best performing BEA in its best performing configuration for the detection of dim point targets in IR images. The proposed procedure relies on a simulation-based off-line approach. The effectiveness of the proposed BEA selection procedure is evaluated in two case studies typical of IR video-surveillance: maritime and airborne surveillance scenarios. Experimental image sequences have been acquired by MWIR cameras in order to test the proposed BEA selection criterion (BEA-SC).

How can you learn to live more sustainably, without giving up your lifestyle, and why should you care? We often hear the terms sustainability and sustainable development, but how much do we really understand about what they mean? Is sustainability just climate change in a different package? What's the difference between global warming and climate change? And, most importantly, what can be done about it? As well as explaining some of the most pressing problems, this book answers these questions and looks at how we are progressing. While many of these problems can seem overwhelming when viewed i

The policies of the United States government regarding illegal drug use have created controversy for decades. Policymakers have long disagreed over the costs of illegal drug use, the best ways to combat its impact on society, and even whether the ""war"" is worth fighting anymore. This new book provides historical context to the war on drugs and examines, among other related issues, three major sources of friction in the war: the disparity between criminal sentences for crack and powder cocaine offenses, a topic which the U.S. Congress has examined in detail for years; the move toward the use

This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image.

Radar imaging is a tool used by our military to provide information to enhance situational awareness for both war fighters on the front lines and military leaders planning and forming strategies from afar. Noise radar technology is especially exciting as it has properties of covertness as well as the ability to see through walls, foliage, and other types of cover. In this thesis, AFIT's NoNet was used to generate images utilizing a random noise radar waveform as the transmission signal. The NoNet was arranged in four configurations: arc, line, cluster, and surround. Images were formed using three algorithms: multilateration and the SAR imaging techniques, convolution backprojection, and polar format algorithm. Each configuration was assessed based on image quality, in terms of its resolution, and computational complexity, in terms of its execution time. Experiments revealed tradeoffs between computational complexity and achieving fine resolutions. Depending on image size, the multilateration algorithm was approximately 6 to 35 faster than polar format and 16 to 26 times faster than convolution backprojection. Backprojection yielded images with resolutions up to approximately 11 times finer in range and 18 times finer in cross-range for the surround configuration, over multilateration images. Pixel size in polar format images made comparisons of resolution unusable. This thesis provides information on the performance of imaging algorithms given a configuration of nodes. The information will provide groundwork for future use of the AFIT NoNet as a covertly operating imaging radar in dynamic applications.