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

This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.

/pdf/automatic-panoramic-image-stitching-using-invariant-features-qz6yy9ngai.pdf

Automatic Panoramic Image Stitching using Invariant Features

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

We report an imaging method, termed Fourier ptychographic microscopy (FPM), which iteratively stitches together a
number of variably illuminated, low-resolution intensity images in Fourier space to produce a wide-field, high-resolution
complex sample image. By adopting a wavefront correction strategy, the FPM method can also correct for aberrations and
digitally extend a microscope’s depth of focus beyond the physical limitations of its optics. As a demonstration, we built a
microscope prototype with a resolution of 0.78 µm, a field of view of ∼120 mm^2 and a resolution-invariant depth of focus
of 0.3 mm (characterized at 632 nm). Gigapixel colour images of histology slides verify successful FPM operation. The
reported imaging procedure transforms the general challenge of high-throughput, high-resolution microscopy from one
that is coupled to the physical limitations of the system’s optics to one that is solvable through computation.

Wide-field, high-resolution Fourier ptychographic microscopy

This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. This tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It ends with a discussion of open research problems in the area.

/pdf/image-alignment-and-stitching-a-tutorial-57yf6bt9xq.pdf

Image Alignment and Stitching: A Tutorial

Foreword Preface 1 Introduction 2 Light And Color 3 HDR Image Encodings 4 HDR Image Capture 5 Display Devices 6 The Human Visual System and HDR Tone Mapping 7 Spatial Tone Reproduction 8 Frequency Domain and Gradient Domain Tone Reproduction 9 Image-Based Lighting List of Symbols References Index

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting

The dynamic range of many real-world environments exceeds the capabilities of current display technology by several orders of magnitude. In this paper we discuss the design of two different display systems that are capable of displaying images with a dynamic range much more similar to that encountered in the real world. The first display system is based on a combination of an LCD panel and a DLP projector, and can be built from off-the-shelf components. While this design is feasible in a lab setting, the second display system, which relies on a custom-built LED panel instead of the projector, is more suitable for usual office workspaces and commercial applications. We describe the design of both systems as well as the software issues that arise. We also discuss the advantages and disadvantages of the two designs and potential applications for both systems.

https://ws.iat.sfu.ca/papers/hdr_demonstration.pdf

High dynamic range display systems

This landmark book is the first to describe HDRI technology in its entirety and covers a wide-range of topics, from capture devices to tone reproduction and image-based lighting. The techniques described enable you to produce images that have a dynamic range much closer to that found in the real world, leading to an unparalleled visual experience. As both an introduction to the field and an authoritative technical reference, it is essential to anyone working with images, whether in computer graphics, film, video, photography, or lighting design.



New material includes chapters on High Dynamic Range Video Encoding, High Dynamic Range Image Encoding, and High Dynammic Range Display Devices
Written by the inventors and initial implementors of High Dynamic Range Imaging
Covers the basic concepts (including just enough about human vision to explain why HDR images are necessary), image capture, image encoding, file formats, display techniques, tone mapping for lower dynamic range display, and the use of HDR images and calculations in 3D rendering
Range and depth of coverage is good for the knowledgeable researcher as well as those who are just starting to learn about High Dynamic Range imaging


Table of Contents

Introduction; Light and Color; HDR Image Encodings; HDR Video Encodings; HDR Image and Video Capture; Display Devices; The Human Visual System and HDR Tone Mapping; Spatial Tone Reproduction; Frequency Domain and Gradient Domain Tone Reproduction; Inverse Tone Reproduction; Visible Difference Predictors; Image-Based Lighting.

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)

New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

We have developed an emissive high dynamic range (HDR) display that is capable of displaying a luminance range of 10,000cd/m2 to 0.1cd/m2 while maintaining all features found in conventional LCD displays such as resolution, refresh rate and image quality. We achieve that dynamic range by combining two display systems — a high resolution transmissive LCD and a low resolution, monochrome display composed of high brightness light emitting diodes (LED). This paper provides a description of the technology as well as findings from a supporting psychological study that establishes that correction for the low resolution display through compensation in the high resolution display yields an image which does not differ perceptibly from that of a purely high resolution HDR display.

Greg Ward

Papers

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting

High dynamic range display systems

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

54.2: A High Dynamic Range Display Using Low and High Resolution Modulators