Color-appearance modeling for cross-media image reproduction

Five color-appearance transforms were tested under a variety of conditions to determine which is best for producing CRT reproductions of original printed images. The transforms included: von Kries chromatic adaptation, CIELAB color space, RLAB color appearance model, Hunt\u27s color appearance model, and Nayatani\u27s color appearance model. It was found that RLAB produced the best matches for changes in white point, luminance level, and background changes, but did not accurately predict the effect of surround. The ability of CIELAB color space was equal to that of RLAB in many cases, and performed better for changes in surround. Expert observers generated CRT images in one viewing condition that they perceived to match an original image viewed in another condition. This technique produced images that were equal to or better than the best color appearance model tested and is a useful technique to generate color appearance data for developing new models and testing existing models

Programmable Image-Based Light Capture for Previsualization

Previsualization is a class of techniques for creating approximate previews of a movie sequence in order to visualize a scene prior to shooting it on the set. Often these techniques are used to convey the artistic direction of the story in terms of cinematic elements, such as camera movement, angle, lighting, dialogue, and character motion. Essentially, a movie director uses previsualization (previs) to convey movie visuals as he sees them in his minds-eye . Traditional methods for previs include hand-drawn sketches, Storyboards, scaled models, and photographs, which are created by artists to convey how a scene or character might look or move. A recent trend has been to use 3D graphics applications such as video game engines to perform previs, which is called 3D previs. This type of previs is generally used prior to shooting a scene in order to choreograph camera or character movements. To visualize a scene while being recorded on-set, directors and cinematographers use a technique called On-set previs, which provides a real-time view with little to no processing. Other types of previs, such as Technical previs, emphasize accurately capturing scene properties but lack any interactive manipulation and are usually employed by visual effects crews and not for cinematographers or directors. This dissertation\u27s focus is on creating a new method for interactive visualization that will automatically capture the on-set lighting and provide interactive manipulation of cinematic elements to facilitate the movie maker\u27s artistic expression, validate cinematic choices, and provide guidance to production crews. Our method will overcome the drawbacks of the all previous previs methods by combining photorealistic rendering with accurately captured scene details, which is interactively displayed on a mobile capture and rendering platform. This dissertation describes a new hardware and software previs framework that enables interactive visualization of on-set post-production elements. A three-tiered framework, which is the main contribution of this dissertation is; 1) a novel programmable camera architecture that provides programmability to low-level features and a visual programming interface, 2) new algorithms that analyzes and decomposes the scene photometrically, and 3) a previs interface that leverages the previous to perform interactive rendering and manipulation of the photometric and computer generated elements. For this dissertation we implemented a programmable camera with a novel visual programming interface. We developed the photometric theory and implementation of our novel relighting technique called Symmetric lighting, which can be used to relight a scene with multiple illuminants with respect to color, intensity and location on our programmable camera. We analyzed the performance of Symmetric lighting on synthetic and real scenes to evaluate the benefits and limitations with respect to the reflectance composition of the scene and the number and color of lights within the scene. We found that, since our method is based on a Lambertian reflectance assumption, our method works well under this assumption but that scenes with high amounts of specular reflections can have higher errors in terms of relighting accuracy and additional steps are required to mitigate this limitation. Also, scenes which contain lights whose colors are a too similar can lead to degenerate cases in terms of relighting. Despite these limitations, an important contribution of our work is that Symmetric lighting can also be leveraged as a solution for performing multi-illuminant white balancing and light color estimation within a scene with multiple illuminants without limits on the color range or number of lights. We compared our method to other white balance methods and show that our method is superior when at least one of the light colors is known a priori

An investigation into the practicality of using a digital camera\u27s raw data in print publishing applications

RAW file formats were introduced to the photography industry more than five years ago. However, not much information about their functionality, capabilities, or advantages in different situations has been made available. Some digital camera users are not aware of their existence and, if they were, they would not know what to do with them. RAW file formats functions are viewed as a concern of the professional photographer and not of the average user (Fraser, 2005). RAW file formats are unprocessed digital image data ? the type available from many current digital cameras. There is no standard RAW format. Each camera captures RAW data in a proprietary fashion. Thus, special camera-specific software is needed to access the RAW files. The widely used TIFF and JPEG file formats are processed within the camera right after shooting each image. TIFF files are uncompressed and therefore large. JPEG files are spatially compressed and smaller than TIFF files for images with the equivalent number of pixels. RAW file formats contain all the original data, uncompressed, with no adjustments to image sharpness, white balance, contrast, and saturation, but they are incomplete as images because they need to be processed using either proprietary software provided by the digital camera manufacturer or other software such as Adobe? Photoshop? CS. This study addresses the following research question: What is the real value, if any, of RAW file formats in magazine publishing? The author?s intention was to learn about RAW file formats and what is currently being claimed about their advantages and disadvantages. Photographing using RAW formats is like photographing with negative film, only in digital form. Using RAW formats is much like preserving the analog format workflow, where after all of the images are captured on film, the film is sent out for developing before we can see the image. Using RAW files is similar to this process, but it is done by the photographer using a computer and not a film-processing machine. To do this the photographer or processor needs software that can interpret the RAW format image. Research Method This research was exploratory in nature. Information was gathered from experts who have experimented with RAW file formats, who have had direct involvement with digital photography technology, and who have sought to discover its capabilities and its practicality in the real world. This thesis also discusses topics such as the various types of digital cameras suitable for publishing work. This study involved collecting data from interview sessions. Interviews were conducted with eight experts in the field of photography and publishing at Rochester Institute of Technology (RIT). (Interview questions are listed in Appendix I). Data analysis was based on information gathered during these interviews. From the collected information, a list was created of the potential advantages of Camera RAW workflows in magazine publishing applications. The conclusion addresses possible advantages, as well as the practicality of using Camera RAW data in magazine publishing applications. A set of guidelines for future Camera RAW workflow users is also provided. Conclusion Based on the findings from the interviews, it is concluded that RAW file format usage is currently impractical in the magazine publishing environment. The RAW workflow would not be practical for photojournalism, where speed is more important than the quality of the image. Time, cost, and demands from clients contribute to these changes. Because there is no standard RAW format and because the photographer must spend extra time to process the images, the RAW workflow does not address the needs of magazine publishing. It might be practical to use in the future, after the RAW format has been standardized, and the RAW workflow has been perfected. Endnotes for Abstract Fraser, B. (2005). Real World Camera Raw with Adobe? Photoshop? CS. California: Peachpit Press

Information Theoretic EEG Analysis of Children with Severe Disabilities in Response to Power Mobility Training: A Pilot Study

Studies have shown that self-initiated locomotion in infants and young children is paramount to developing motor skills, social skills, and visuospatial perception. Because children with multiple, severe disabilities are often not able to produce movement on their own due to limited motor control, access to a power mobility device may be vital to their overall development. This study extends data analysis from an exploratory study, and investigates the use of information theoretics in providing objective measures to characterize changes in electroencephalograms (EEG) as a result of power mobility training, and relates these changes to observed rehabilitation outcomes. Three subjects with multiple, severe disabilities participated in the 20-week study. A single subject, A-B-A-B study design was employed where each baseline (A) and intervention (B) phase lasted 5 weeks. Various conditions were included in both phases: No Interaction 1, Passive Mobility, and No Interaction 2. EEG data were collected under each condition for 5 minutes, sampled at 128 Hz. During B phases, the subject trained for 45-60 minutes with the power mobility device as well. All three subjects showed similar changes in activation in the frontoparietal recordings, likely due to the visuomotor, attention, and memory requirements of power mobility training. The emergence of a centroparietal network was also observed in one subject. Furthermore, a theta increase and alpha decrease in power for one subject may have been the result of increased alertness and anticipation, while a decrease in theta and increase in alpha power for the other subject may be associated with an increase in cognitive performance. Additionally, all subjects demonstrated rehabilitative growth that correlated well with trends seen in the EEG metrics. All three subjects also exhibited more significant change during the intervention phases compared to the baseline phases. This suggests that power mobility training may be responsible for consistent and objectively quantifiable changes in cortical activity that may be correlated with improvement in subjective measures of cognitive gains

Deep learning techniques for visual object tracking

Visual object tracking plays a crucial role in various vision systems, including biometric analysis, medical imaging, smart traffic systems, and video surveillance. Despite notable advancements in visual object tracking over the past few decades, many tracking algorithms still face challenges due to factors like illumination changes, deformation, and scale variations. This thesis is divided into three parts. The first part introduces the visual object tracking problem and discusses the traditional approaches that have been used to study it. We then propose a novel method called Tracking by Iterative Multi-Refinements, which addresses the issue of locating the target by redefining the search for the ideal bounding box. This method utilizes an iterative process to forecast a sequence of bounding box adjustments, enabling the tracking algorithm to handle multiple non-conflicting transformations simultaneously. As a result, it achieves faster tracking and can handle a higher number of composite transformations. In the second part of this thesis we explore the application of reinforcement learning (RL) to visual tracking. Presenting a general RL framework applicable to problems that require a sequence of decisions. We discuss various families of popular RL approaches, including value-based methods, policy gradient approaches, and Actor-Critic Methods. Furthermore, we delve into the application of RL to visual tracking, where an RL agent predicts the target's location, selects hyperparameters, correlation filters, or target appearance. A comprehensive comparison of these approaches is provided, along with a taxonomy of state-of-the-art methods. The third part presents a novel method that addresses the need for online tuning of offline-trained tracking models. Typically, offline-trained models, whether through supervised learning or reinforcement learning, require additional tuning during online tracking to achieve optimal performance. The duration of this tuning process depends on the number of layers that need training for the new target. However, our thesis proposes a pioneering approach that expedites the training of convolutional neural networks (CNNs) while preserving their high performance levels. In summary, this thesis extensively explores the area of visual object tracking and its related domains, covering traditional approaches, novel methodologies like Tracking by Iterative Multi-Refinements, the application of reinforcement learning, and a pioneering method for accelerating CNN training. By addressing the challenges faced by existing tracking algorithms, this research aims to advance the field of visual object tracking and contributes to the development of more robust and efficient tracking systems

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Visual literacy for libraries: A practical, standards-based guide

When we step back and think about how to situate visual literacy into a library context, the word critical keeps coming up: critical thinking, critical viewing, critical using, critical making, and the list goes on. To understand our approach, start with your own practice, add images, and see where it takes you. Do you encourage students to think critically as they research? How can you extend this experience to images? Do you embrace critical information literacy? Can you bring visual content to enrich that experience? Do you teach students to critically evaluate sources? How can you expand that practice to images? You’ll see a lot of questions in this book, because our approach is inquiry- driven. This is not to say that we don’t cover the basics of image content. Curious about color? Covered. Not sure where to find great images? We’ll show you. Wondering what makes a good presentation? We talk about that too. But what we really want you to get out of this book is a new understanding of how images fit into our critical (there it is again) practice as librarians and how we can advance student learning with our own visual literacy. This book grounds visual literacy in your everyday practice—connecting it to what you know and do as a librarian who engages in reflective practice. Heidi Jacobs put it well when she argued that, for information literacy pedagogy, “one of the best ways for us to encourage students to be engaged learners is for us to become engaged learners, delve deeply into our own problem posing, and embody the kind of engagement we want to see in our students” (Jacobs 2008). We extend this viewpoint to visual literacy pedagogy and provide many opportunities for you to embody the kind of visual literacy that you want to develop in your learners

Image Color Correction, Enhancement, and Editing

This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this thesis presents two different methods for ISP white balancing. Afterwards, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP. Then, we explore another factor that has a significant impact on the quality of camera-rendered colors, in which we outline two different methods to correct exposure errors in camera-rendered images. Lastly, we discuss post-capture auto color editing and manipulation. In particular, we propose auto image recoloring methods to generate different realistic versions of the same camera-rendered image with new colors. Through extensive evaluations, we demonstrate that our methods provide superior solutions compared to existing alternatives targeting color correction, color enhancement, and color editing

Cognitively-Engineered Multisensor Data Fusion Systems for Military Applications

The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory information have been and are currently being studied extensively. However, the cognitive aspects of multisensor data fusion have not received so much attention. Prior research in the field of cognitive engineering has shown that the cognitive aspects of any human-machine system should be taken into consideration in order to achieve systems that are both safe and useful. The goal of this research was to model how humans interpret multisensory data, and to evaluate the value of a cognitively-engineered multisensory data fusion system as an effective, time-saving means of presenting information in high- stress situations. Specifically, this research used principles from cognitive engineering to design, implement, and evaluate a multisensor data fusion system for pilots in high-stress situations. Two preliminary studies were performed, and concurrent protocol analysis was conducted to determine how humans interpret and mentally fuse information from multiple sensors in both low- and high-stress environments. This information was used to develop a model for human processing of information from multiple data sources. This model was then implemented in the development of algorithms for fusing imagery from several disparate sensors (visible and infrared). The model and the system as a whole were empirically evaluated in an experiment with fighter pilots in a simulated combat environment. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors, and that the algorithms show promise for assisting fighter pilots in quicker and more accurate target identification