JOURNALISTS’ PERCEPTION AND ATTITUDE TO SOCIAL MEDIA IMAGE USE DURING THE 2015 PRESIDENTIAL ELECTION CAMPAIGNS IN NIGERIA

This study examined journalists’ perception and attitude to social media image use by Scannews and NewsRescue Online media outfits during the 2015 presidential election campaigns in Nigeria. The study objectives centered on the volume of digital image usage, forms, context, intended purpose as well as the implications of displayed images for professional photojournalism. The study is anchored on Consequentialism ethical theory, Kant Deontological ethical theory and Virtue ethical theory. Three research designs namely; Content analysis, Experimental design and Survey method were adopted as procedures that elicited information for the study. Thus, Coding Sheet and Questionnaire were used as instruments for data collection. A sample of 57 photographs and 395 journalists were used. The study found a competitive usage of digital images by NewsRescue (29 images - 51%) and Scannews (28 images, 49%). The study further found the forms of digital image techniques used by Scannews and NewsRescue to include; toning, flatting, changing costumes, cloning and retouching. In addition, images displayed were found to be triggered by corruption, security consciousness of the aspirants and on health ground. Other motivations were driven by experience and competence of the candidates. Findings also show that the contexts of digital image usage by Scannews and NewsResue impede professional virtues of objectivity, accuracy, truthfulness, fairness and balance (r = 842 > P = .000). On the denotation of displayed images, the study found that, images were symbolic of a guy (8.8%), fighter (12.3%), hooligan (17.5%) and of animal (24.6%). The study further found that images were used to infer that the candidate is a saint, competent, dependable, experienced, capable and dangerous. Findings also show that the intended purposes of digital image usage by Scannews and NewsResue impede professional virtues of objectivity, accuracy, truthfulness, fairness and balance. There was significant difference between the intended purpose of digital image displayed and professional journalism practice (t = -3.388 > P = .001; P < .01). This study also found several problems for the utilization of digital images by Scannews and NewsRescue. The study finally established several techniques that conform to journalism professional standards. The research reached a conclusion that, journalism is a profession and every profession has its norms for responsible practice which must be upheld at all times. The study thus, recommends among others that; journalism as a profession is anchored on five key principles of objectivity, accuracy, truthfulness, fairness and balance which must be upheld in all published news photograph; and that news photographers should employ journalism canons as yardstick for ethical decisions regarding the use of photographs

JOURNALISTS’ PERCEPTION AND ATTITUDE TO SOCIAL MEDIA IMAGE USE DURING THE 2015 PRESIDENTIAL ELECTION CAMPAIGNS IN NIGERIA

This study examined journalists’ perception and attitude to social media image use by Scannews and NewsRescue Online media outfits during the 2015 presidential election campaigns in Nigeria. The study objectives centered on the volume of digital image usage, forms, context, intended purpose as well as the implications of displayed images for professional photojournalism. The study is anchored on Consequentialism ethical theory, Kant Deontological ethical theory and Virtue ethical theory. Three research designs namely; Content analysis, Experimental design and Survey method were adopted as procedures that elicited information for the study. Thus, Coding Sheet and Questionnaire were used as instruments for data collection. A sample of 57 photographs and 395 journalists were used. The study found a competitive usage of digital images by NewsRescue (29 images - 51%) and Scannews (28 images, 49%). The study further found the forms of digital image techniques used by Scannews and NewsRescue to include; toning, flatting, changing costumes, cloning and retouching. In addition, images displayed were found to be triggered by corruption, security consciousness of the aspirants and on health ground. Other motivations were driven by experience and competence of the candidates. Findings also show that the contexts of digital image usage by Scannews and NewsResue impede professional virtues of objectivity, accuracy, truthfulness, fairness and balance (r = 842 > P = .000). On the denotation of displayed images, the study found that, images were symbolic of a guy (8.8%), fighter (12.3%), hooligan (17.5%) and of animal (24.6%). The study further found that images were used to infer that the candidate is a saint, competent, dependable, experienced, capable and dangerous. Findings also show that the intended purposes of digital image usage by Scannews and NewsResue impede professional virtues of objectivity, accuracy, truthfulness, fairness and balance. There was significant difference between the intended purpose of digital image displayed and professional journalism practice (t = -3.388 > P = .001; P < .01). This study also found several problems for the utilization of digital images by Scannews and NewsRescue. The study finally established several techniques that conform to journalism professional standards. The research reached a conclusion that, journalism is a profession and every profession has its norms for responsible practice which must be upheld at all times. The study thus, recommends among others that; journalism as a profession is anchored on five key principles of objectivity, accuracy, truthfulness, fairness and balance which must be upheld in all published news photograph; and that news photographers should employ journalism canons as yardstick for ethical decisions regarding the use of photographs

Digital archiving and reproduction of black and white photography

Capturing and reproducing black and white images are common problems for high quality print reproduction. This study compared the monotone reproduction quality of the Kodak Photo CD Master technology to the standard methods currently being employed using high resolution scanners such as the Agfa Horizon Scanner and the Optronics ColorGetter II. The Kodak Photo CD Master and the Optronics ColorGetter II were used to scan the original 35mm black and white film negatives. The negatives selected represent the various tonal ranges encountered by professional photographers. High key and low key images were included in the selection since these are the extreme density range of negatives. The same six monotone images, obtained from a professional photographer, were scanned using either the negative or the desired print. The flatbed scanners, the midrange Agfa Horizon and the low end Agfa StudioScan, captured the desired print as a digital file. The Optronics ColorGetter II, a drum scanner, and the Kodak Photo CD captured the monotone negative. This study determined whether the image captured by the Photo CD Master scanner could produce the image quality that is required by professional photographers. Currently, quality printing uses high end scanners to capture high resolutions and detail. Photo CD\u27s are being implemented for archival storage of dig ital images. Traditional methods of scanning were also investigated to determine whether it is possible to digitally reproduce a monotone desired print accurately to satisfy a professional photographer. Digital duplication of the desired print , with its darkroom manipulation, would be a significant achievement for the photographer. In using a digital format a photographer would be able to store and recall the information and exactly duplicate a print without spending additional time cus tom printing. Adobe Photoshop 2.5.1 was used to globally and locally control the negative to reproduce the photographer\u27s intent. A comparison was made between the desired print and the results obtained through the digital capture, manipulation, storage and printing of the image. Each digital image captured by the four scanners was printed on four different printers. The four printers used in this study are: The Canon Laser Copier 500 Color Electrophotographic Laser The Hewlett Packard LaserJet, monochrome electrophotographic laser The 3M Rainbow Dye Sublimation The Epson Stylus InkJet This thesis questions whether the digital darkroom can replace the professional photographer\u27s wet darkroom through the use of scanners, computers, software and desktop printers. It determines which method is best for capturing and reproducing the professional photographer\u27s images. An evaluation of the final digital prints is made by a professional photographer

Digital image forensics via meta-learning and few-shot learning

Digital images are a substantial portion of the information conveyed by social media, the Internet, and television in our daily life. In recent years, digital images have become not only one of the public information carriers, but also a crucial piece of evidence. The widespread availability of low-cost, user-friendly, and potent image editing software and mobile phone applications facilitates altering images without professional expertise. Consequently, safeguarding the originality and integrity of digital images has become a difficulty. Forgers commonly use digital image manipulation to transmit misleading information. Digital image forensics investigates the irregular patterns that might result from image alteration. It is crucial to information security. Over the past several years, machine learning techniques have been effectively used to identify image forgeries. Convolutional Neural Networks(CNN) are a frequent machine learning approach. A standard CNN model could distinguish between original and manipulated images. In this dissertation, two CNN models are introduced to recognize seam carving and Gaussian filtering. Training a conventional CNN model for a new similar image forgery detection task, one must start from scratch. Additionally, many types of tampered image data are challenging to acquire or simulate. Meta-learning is an alternative learning paradigm in which a machine learning model gets experience across numerous related tasks and uses this expertise to improve its future learning performance. Few-shot learning is a method for acquiring knowledge from few data. It can classify images with as few as one or two examples per class. Inspired by meta-learning and few-shot learning, this dissertation proposed a prototypical networks model capable of resolving a collection of related image forgery detection problems. Unlike traditional CNN models, the proposed prototypical networks model does not need to be trained from scratch for a new task. Additionally, it drastically decreases the quantity of training images

UC-423 Developing Support for DICOM medical Images

DICOM (Digital Imaging and Communications in Medicine) is the standard for storing and sharing medical image information. GIMP (GNU Image Manipulation Program) is the leading open-source program for processing professional and scientific images; however, it is currently unable to open many modern DICOM images. The project goal is to update GIMP\u27s DICOM import plugin with code to support all types of DICOM images. After creating a C++ wrapper to incorporate the GDCM (Grassroots DICOM) library into the existing software, GIMP could import images that previously caused errors. The updated plugin has been submitted as a merge request and is currently being reviewed by the developers for the next software release. The next step would be to expand GIMP\u27s DICOM metadata and display multi-frame images to continue to better support medical professionals and researchers

A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

An automated auroral detection system using deep learning: real-time operation in Tromsø, Norway

The activity of citizen scientists who capture images of aurora borealis using digital cameras has recently been contributing to research regarding space physics by professional scientists. Auroral images captured using digital cameras not only fascinate us, but may also provide information about the energy of precipitating auroral electrons from space; this ability makes the use of digital cameras more meaningful. To support the application of digital cameras, we have developed artificial intelligence that monitors the auroral appearance in Tromsø, Norway, instead of relying on the human eye, and implemented a web application, “Tromsø AI”, which notifies the scientists of the appearance of auroras in real-time. This “AI” has a double meaning: artificial intelligence and eyes (instead of human eyes). Utilizing the Tromsø AI, we also classified large-scale optical data to derive annual, monthly, and UT variations of the auroral occurrence rate for the first time. The derived occurrence characteristics are fairly consistent with the results obtained using the naked eye, and the evaluation using the validation data also showed a high F1 score of over 93%, indicating that the classifier has a performance comparable to that of the human eye classifying observed images

Aesthetic complexity: practice and perception in art & design

My research investigates the aesthetics of visual complexity in the practice and perception of visual art and design. The aim is to understand visual complexity in terms of the relationship between the objective properties of images and subjective properties of perception. I take a computational and empirical approach to this subject, incorporating methods from information theory, computer graphics, complexity theory and experimental psychology. For testing, I create cellular automata programs to generate stimulus images, and borrow other types of visual material from students and professional artists, designers and craftspeople. Visual complexity is measured in two ways: Firstly, an objective measure of complexity is based on the compression of digital image files, which provides an information-based scale of order to randomness. Secondly, psychophysical techniques are employed to measure the subjective complexity of the images and other aesthetic judgements. Research in complex systems theory and experimental aesthetics suggests that we can expect an inverted ‘U’ correlation between the two measures of complexity

Reconstructing the Past in 3D Using Historical Aerial Imgery

Historical aerial film images are a valuable record of the past, and are useful as a baseline for change detection and landcover analysis. To be useful in GIS analysis the images must be oriented to a spatial reference system. This is challenging as historical imagery is often missing flight and camera information. Traditional photogrammetric processing techniques exist to overcome these challenges, but they require specialized knowledge, time and expense to complete. Because of this, many collections of historical images are left unprocessed. This project produced a method to quickly standardize the photos, spatially orient them, correct them for distortion effects, and extract a digital surface model from the overlapping image series using Pix4D Professional. The horizontal accuracy met National Map Accuracy Standards when the Pix 4D process was combined with traditional georeferencing. The workflow was faster than traditional methods due to economies of scale in the new process