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

In 1997, Azuma published a survey on augmented reality (AR). Our goal is to complement, rather than replace, the original survey by presenting representative examples of the new advances. We refer one to the original survey for descriptions of potential applications (such as medical visualization, maintenance and repair of complex equipment, annotation, and path planning); summaries of AR system characteristics (such as the advantages and disadvantages of optical and video approaches to blending virtual and real, problems in display focus and contrast, and system portability); and an introduction to the crucial problem of registration, including sources of registration error and error-reduction strategies.

https://www.cc.gatech.edu/~blair/papers/ARsurveyCGA.pdf

Recent advances in augmented reality

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Photo tourism: exploring photo collections in 3D

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.

/pdf/texture-synthesis-by-non-parametric-sampling-545ddgjo96.pdf

Texture synthesis by non-parametric sampling

We present a method of recovering high dynamic range radiance maps from photographs taken with conventional imaging equipment. In our method, multiple photographs of the scene are taken with different amounts of exposure. Our algorithm uses these differently exposed photographs to recover the response function of the imaging process, up to factor of scale, using the assumption of reciprocity. With the known response function, the algorithm can fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. We demonstrate our method on images acquired with both photochemical and digital imaging processes. We discuss how this work is applicable in many areas of computer graphics involving digitized photographs, including image-based modeling, image compositing, and image processing. Lastly, we demonstrate a few applications of having high dynamic range radiance maps, such as synthesizing realistic motion blur and simulating the response of the human visual system.

/pdf/recovering-high-dynamic-range-radiance-maps-from-photographs-zk2yr4fcgd.pdf

Recovering high dynamic range radiance maps from photographs

We present a novel method for surface reflectometry from a few observations of a scene under a single uniform spherical field of circularly polarized illumination. The method is based on a novel analysis of the Stokes reflectance field of circularly polarized spherical illumination and yields per-pixel estimates of diffuse albedo, specular albedo, index of refraction, and specular roughness of isotropic BRDFs. To infer these reflectance parameters, we measure the Stokes parameters of the reflected light at each pixel by taking four photographs of the scene, consisting of three photographs with differently oriented linear polarizers in front of the camera, and one additional photograph with a circular polarizer. The method only assumes knowledge of surface orientation, for which we make a few additional photometric measurements. We verify our method with three different lighting setups, ranging from specialized to off-the-shelf hardware, which project either discrete or continuous fields of spherical illumination. Our technique offers several benefits: it estimates a more detailed model of per-pixel surface reflectance parameters than previous work, it requires a relatively small number of measurements, it is applicable to a wide range of material types, and it is completely viewpoint independent.

Circularly polarized spherical illumination reflectometry

This paper presents a technique for representing and displaying high dynamic-range texture maps (HDRTMs) using current graphics hardware. Dynamic range in real-world environments often far exceeds the range representable in 8-bit per-channel texture maps. The increased realism afforded by a highdynamic range representation provides improved fidelity and expressiveness for interactive visualization of image-based models. Our technique allows for realtime rendering of scenes with arbitrary dynamic range, limited only by available texture memory.

In our technique, high-dynamic range textures are decomposed into sets of 8- bit textures. These 8-bit textures are dynamically reassembled by the graphics hardware's programmable multitexturing system or using multipass techniques and framebuffer image processing. These operations allow the exposure level of the texture to be adjusted continuously and arbitrarily at the time of rendering, correctly accounting for the gamma curve and dynamic range restrictions of the display device. Further, for any given exposure only two 8-bit textures must be resident in texture memory simultaneously.

We present implementation details of this technique on various 3D graphics hardware architectures. We demonstrate several applications, including high-dynamic range panoramic viewing with simulated auto-exposure, real-time radiance environment mapping, and simulated Fresnel reflection.

https://ict.usc.edu/pubs/Real-Time%20High-Dynamic%20Range%20Texture%20Mapping.pdf

Real-Time High Dynamic Range Texture Mapping

One of the main challenges in facial expression recognition is illumination invariance. Our long-term goal is to develop a system for automatic facial expression recognition that is robust to light variations. In this paper, we introduce a novel 3D Relightable Facial Expression (ICT-3DRFE) database that enables experimentation in the fields of both computer graphics and computer vision. The database contains 3D models for 23 subjects and 15 expressions, as well as photometric information that allow for photorealistic rendering. It is also facial action units annotated, using FACS standards. Using the ICT-3DRFE database we create an image set of different expressions/illuminations to study the effect of illumination on automatic expression recognition. We compared the output scores from automatic recognition with expert FACS annotations and found that they agree when the illumination is uniform. Our results show that the output distribution of the automatic recognition can change significantly with light variations and sometimes causes the discrimination of two different expressions to be diminished. We propose a ratio-based light transfer method, to factor out unwanted illuminations from given images and show that it reduces the effect of illumination on expression recognition.

Effect of illumination on automatic expression recognition: A novel 3D relightable facial database

We present a novel technique to relight images of human faces by learning a model of facial reflectance from a database of 4D reflectance field data of several subjects in a variety of expressions and viewpoints. Using our learned model, a face can be relit in arbitrary illumination environments using only two original images recorded under spherical color gradient illumination. The output of our deep network indicates that the color gradient images contain the information needed to estimate the full 4D reflectance field, including specular reflections and high frequency details. While capturing spherical color gradient illumination still requires a special lighting setup, reduction to just two illumination conditions allows the technique to be applied to dynamic facial performance capture. We show side-by-side comparisons which demonstrate that the proposed system outperforms the state-of-the-art techniques in both realism and speed.

Deep reflectance fields: high-quality facial reflectance field inference from color gradient illumination

An interactive, autostereoscopic system for displaying an object in 3D includes a mirror configured to spin around a vertical axis when actuated by a motor, a high speed video projector, and a processing system including a graphics card interfaced to the video projector. An anisotropic reflector is bonded onto an inclined surface of the mirror. The video projector projects video signals of the object from the projector onto the inclined surface of the mirror while the mirror is spinning, so that light rays representing the video signals are redirected toward a field of view of a 360 degree range. The processing system renders the redirected light rays so as to interactively generate a horizontal-parallax 3D display of the object. Vertical parallax can be included in the display by adjusting vertically the displayed views of the object, in response to tracking of viewer motion by a tracking system.

https://ict.usc.edu/pubs/Rendering%20for%20an%20Interactive%20360%20Light%20Field%20Display.pdf

Paul Debevec

Papers

Circularly polarized spherical illumination reflectometry

Real-Time High Dynamic Range Texture Mapping

Effect of illumination on automatic expression recognition: A novel 3D relightable facial database

Deep reflectance fields: high-quality facial reflectance field inference from color gradient illumination

Rendering for an interactive 360 degree light field display