Refinement of the RLAB Color Space

The prediction of color appearance using the RLAB color space has been tested for a variety of viewing conditions and stimulus types. These tests have shown that RLAB performs well for complex stimuli and not-so-well for simple stimuli. This article reviews the various psychophysical results, interprets their differences, and describes evolutionary enhancements to the RLAB model that simplify it and improve its performance

Quantifying mixed adaptation in cross-media color reproduction

An investigation has been undertaken to address the goal set by the CEE Technical committee TC8-04: To investigate the state of adaptation of the visual system when comparing soft-copy images on self-luminous displays and hard copy images viewed under various ambient lighting conditions A set of psychophysical experiments have been conducted for the determination of corresponding colors between printed stimuli under CIE Illuminant D50 simulators and CRT displayed stimuli with a D93 white point. The experiments were completed with 15 observers and 6 different viewing conditions. Analysis was completed to quantify any systematic effects of viewing configuration and to identify the extent to which existing adaptation and appearance models can predict the results. After examining a number of adaptation transforms, preliminary results showed how a simple von Kries type adaptation transform provided the best predictions for all conditions while subsequent iterations of the von Kries transform using simple ratios between the adapting and ambient illuminants improved upon these results. The results also indicated how the CIECAM97s model, given certain conditions, could provide results equal to or better than the von Kries model

Evaluation and improvement of the workflow of digital imaging of fine art reproduction in museums

Fine arts refer to a broad spectrum of art formats, ie~painting, calligraphy, photography, architecture, and so forth. Fine art reproductions are to create surrogates of the original artwork that are able to faithfully deliver the aesthetics and feelings of the original. Traditionally, reproductions of fine art are made in the form of catalogs, postcards or books by museums, libraries, archives, and so on (hereafter called museums for simplicity). With the widespread adoption of digital archiving in museums, more and more artwork is reproduced to be viewed on a display. For example, artwork collections are made available through museum websites and Google Art Project for art lovers to view on their own displays. In the thesis, we study the fine art reproduction of paintings in the form of soft copy viewed on displays by answering four questions: (1) what is the impact of the viewing condition and original on image quality evaluation? (2) can image quality be improved by avoiding visual editing in current workflows of fine art reproduction? (3) can lightweight spectral imaging be used for fine art reproduction? and (4) what is the performance of spectral reproductions compared with reproductions by current workflows? We started with evaluating the perceived image quality of fine art reproduction created by representative museums in the United States under controlled and uncontrolled environments with and without the presence of the original artwork. The experimental results suggest that the image quality is highly correlated with the color accuracy of the reproduction only when the original is present and the reproduction is evaluated on a characterized display. We then examined the workflows to create these reproductions, and found that current workflows rely heavily on visual editing and retouching (global and local color adjustments on the digital reproduction) to improve the color accuracy of the reproduction. Visual editing and retouching can be both time-consuming and subjective in nature (depending on experts\u27 own experience and understanding of the artwork) lowering the efficiency of artwork digitization considerably. We therefore propose to improve the workflow of fine art reproduction by (1) automating the process of visual editing and retouching in current workflows based on RGB acquisition systems and by (2) recovering the spectral reflectance of the painting with off-the-shelf equipment under commonly available lighting conditions. Finally, we studied the perceived image quality of reproductions created by current three-channel (RGB) workflows with those by spectral imaging and those based on an exemplar-based method

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Semantic Localization and Mapping in Robot Vision

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks. This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence. The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features. Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system. These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems

The effect of image size on the color appearance of image reproductions

Original and reproduced art are usually viewed under quite different viewing conditions. One of the interesting differences in viewing condition is size difference. The main focus of this research was investigation of the effect of image size on color perception of rendered images. This research had several goals. The first goal was to develop an experimental paradigm for measuring the effect of image size on color appearance. The second goal was to identify the most affected image attributes for changes of image size. The final goal was to design and evaluate algorithms to compensate for the change of visual angle (size). To achieve the first goal, an exploratory experiment was performed using a colorimetrically characterized digital projector and LCD. The projector and LCD were light emitting devices and in this sense were similar soft-copy media. The physical sizes of the reproduced images on the LCD and projector screen could be very different. Additionally, one could benefit from flexibility of soft-copy reproduction devices such as real-time image rendering, which is essential for adjustment experiments. The capability of the experimental paradigm in revealing the change of appearance for a change of visual angle (size) was demonstrated by conducting a paired-comparison experiment. Through contrast matching experiments, achromatic and chromatic contrast and mean luminance of an image were identified as the most affected attributes for changes of image size. Measurement of the extent and trend of changes for each attribute were measured using matching experiments. Proper algorithms to compensate for the image size effect were design and evaluated. The correction algorithms were tested versus traditional colorimetric image rendering using a paired-comparison technique. The paired-comparison results confirmed superiority of the algorithms over the traditional colorimetric image rendering for the size effect compensation