Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

We use a simple statistical analysis to impose one image's color characteristics on another. We can achieve color correction by choosing an appropriate source image and apply its characteristic to another image.

https://www.cs.tau.ac.il/~turkel/imagepapers/ColorTransfer.pdf

Color transfer between images

Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

A morphable model for the synthesis of 3D faces

Three-dimensional (3D) displays have become important for many applications including vision research, operation of remote devices, medical imaging, surgical training, scientific visualization, virtual prototyping, and more. In many of these applications, it is important for the graphic image to create a faithful impression of the 3D structure of the portrayed object or scene. Unfortunately, 3D displays often yield distortions in perceived 3D structure compared with the percepts of the real scenes the displays depict. A likely cause of such distortions is the fact that computer displays present images on one surface. Thus, focus cues-accommodation and blur in the retinal image-specify the depth of the display rather than the depths in the depicted scene. Additionally, the uncoupling of vergence and accommodation required by 3D displays frequently reduces one's ability to fuse the binocular stimulus and causes discomfort and fatigue for the viewer. We have developed a novel 3D display that presents focus cues that are correct or nearly correct for the depicted scene. We used this display to evaluate the influence of focus cues on perceptual distortions, fusion failures, and fatigue. We show that when focus cues are correct or nearly correct, (1) the time required to identify a stereoscopic stimulus is reduced, (2) stereoacuity in a time-limited task is increased, (3) distortions in perceived depth are reduced, and (4) viewer fatigue and discomfort are reduced. We discuss the implications of this work for vision research and the design and use of displays.

Vergence-accommodation conflicts hinder visual performance and cause visual fatigue.

Phong-shaded 3D imagery does not provide geometric information of the same richness as human-drawn technical illustrations. A non-photorealistic lighting model is presented that attempts to narrow this gap. The model is based on practice in traditional technical illustration, where the lighting model uses both luminance and changes in hue to indicate surface orientation, reserving extreme lights and darks for edge lines and highlights. The lighting model allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. In addition, we show how this lighting model is modified when portraying models of metal objects. These illustration methods give a clearer picture of shape, structure, and material composition than traditional computer graphics methods. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.

/pdf/a-non-photorealistic-lighting-model-for-automatic-technical-3fgs8ioxky.pdf

A non-photorealistic lighting model for automatic technical illustration

The ubiquity of computer-generated imagery around us, in movies, advertising or on the Internet is already being taken for granted and what impresses most people is the photorealistic quality of the images. Pictures, as we have often been told, are worth a thousand words and the information transported by an image can take many different forms. Many computer graphics researchers are exploring non-photorealistic rendering techniques as an alternative to realistic rendering. Defined by what it is not, non-photorealistic rendering brings art and science together, concentrating less on the process and more on the communication content of an image. Techniques that have long been used by artists can be applied to computer graphics to emphasize subtle attributes and to omit extraneous information. This book provides an overview of the published research on non-photorealistic rendering in order to categorize and distill the current research into a body of usable techniques. A summary of some of the algorithms as well as pseudo-code for producing some of the images is included.

Non-Photorealistic Rendering

In the real world, people are quite accurate in judging distances to locations in the environment, at least for targets resting on the ground plane and distances out to about 20m. Distance judgments in visually immersive environments are much less accurate. Several studies have now shown that in visually immersive environments, the world appears significantly smaller than intended, This study investigates whether or not the compression in apparent distances is the result of the low-quality computer graphics utilized in previous investigations, Visually directed triangulated walking was used to assess distance judgments in the real world and in three virtual environments with graphical renderings of varying quality.

/pdf/does-the-quality-of-the-computer-graphics-matter-when-2j6ti3pevn.pdf

Does the quality of the computer graphics matter when judging distances in visually immersive environments

Visually important image features often disappear when color images are converted to grayscale. The algorithm introduced here reduces such losses by attempting to preserve the salient features of the color image. The Color2Gray algorithm is a 3-step process: 1) convert RGB inputs to a perceptually uniform CIE L*a*b* color space, 2) use chrominance and luminance differences to create grayscale target differences between nearby image pixels, and 3) solve an optimization problem designed to selectively modulate the grayscale representation as a function of the chroma variation of the source image. The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.

/pdf/color2gray-salience-preserving-color-removal-dzgipedmbp.pdf

Color2Gray: salience-preserving color removal

We carried out three experiments to examine the influence of field of view and binocular viewing restrictions on absolute distance perception in real-world indoor environments. Few of the classical visual cues provide direct information for accurate absolute distance judgments to points in the environment beyond about 2 m from the viewer. Nevertheless, in previous work it has been found that visually directed walking tasks reveal accurate distance estimations in full-cue real-world environments to distances up to 20 m. In contrast, the same tasks in virtual environments produced with head-mounted displays (HMDs) show large compression of distance. Field of view and binocular viewing are common limitations in research with HMDs, and have been rarely studied under full pictorial-cue conditions in the context of distance perception in the real-world. Experiment 1 showed that the view of one's body and feet on the floor was not necessary for accurate distance perception. In experiment 2 we manipulated the horizontal and the vertical field of view along with head rotation and found that a restricted field of view did not affect the accuracy of distance estimations when head movement was allowed. Experiment 3 showed that performance with monocular viewing was equal to that with binocular viewing. These results have implications for the information needed to scale egocentric distance in the real-world and reduce the support for the hypothesis that a limited field of view or imperfections in binocular image presentation are the cause of the underestimation seen with HMDs.

/pdf/the-influence-of-restricted-viewing-conditions-on-egocentric-ejsyhlgcpc.pdf

Amy A. Gooch

Papers

A non-photorealistic lighting model for automatic technical illustration

Non-Photorealistic Rendering

Does the quality of the computer graphics matter when judging distances in visually immersive environments

Color2Gray: salience-preserving color removal

The influence of restricted viewing conditions on egocentric distance perception: implications for real and virtual indoor environments.