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

Numerical Methods for Scientists and Engineers

Image blur is caused by a number of factors such as motion, defocus, capturing light over the non-zero area of the aperture and pixel, the presence of anti-aliasing filters on a camera sensor, and limited sensor resolution We present an algorithm that estimates non-parametric, spatially-varying blur functions (ie, point-spread functions or PSFs) at subpixel resolution from a single image Our method handles blur due to defocus, slight camera motion, and inherent aspects of the imaging system Our algorithm can be used to measure blur due to limited sensor resolution by estimating a sub-pixel, super-resolved PSF even for in-focus images It operates by predicting a ldquosharprdquo version of a blurry input image and uses the two images to solve for a PSF We handle the cases where the scene content is unknown and also where a known printed calibration target is placed in the scene Our method is completely automatic, fast, and produces accurate results

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PSF estimation using sharp edge prediction

The development and adoption of standards for the evaluation of digital camera resolution has helped foster the widespread use of slanted-edge-based analysis. In addition, the form of these evaluation methods suggests their use in imaging system analysis and design. The standards-specific methods and algorithms, however, are not intended for direct MTF evaluation, but if care is taken to avoid bias and minimize random error, the methods can successfully be used for this purpose. In this paper the influence of several variables are discussed. Specifically, the effect of color misregistration, edge location estimation, data-record length and image noise on the measured MTF are addressed.

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Slanted-Edge MTF for Digital Camera and Scanner Analysis

When the size of a CMOS imaging sensor array is fixed, the only way to increase sampling density and spatial resolution is to reduce pixel size. But reducing pixel size reduces the light sensitivity. Hence, under these constraints, there is a tradeoff between spatial resolution and light sensitivity. Because this tradeoff involves the interaction of many different system components, we used a full system simulation to characterize performance. This paper describes system simulations that predict the output of imaging sensors with the same dye size but different pixel sizes and presents metrics that quantify the spatial resolution and light sensitivity for these different imaging sensors.

Resolution and light sensitivity tradeoff with pixel size

It has been almost five years since the ISO adopted a standard for measurement of image resolution of digital still cameras using slanted-edge gradient analysis. The method has also been applied to the spatial frequency response and MTF of film and print scanners, and CRT displays. Each of these applications presents challenges to the use of the method. Previously, we have described causes of both bias and variation error in terms of the various signal processing steps involved. This analysis, when combined with observations from practical systems testing, has suggested improvements and interpretation of results. Specifically, refinements in data screening for signal encoding problems, edge feature location and slope estimation, and noise resilience will be addressed.

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Refined Slanted-Edge Measurement from Practical Camera and Scanner Testing.

The modulation transfer function (MTF) has long been used as a diagnostic tool for analog image capture, by tracking frequency response caused by aperture, field position, or defocus optical phenomena. For digital capture, these factors still exist, in addition to a host of others introduced by detector motion, sampling, and image processing. Many of these problems can be identified via their MTF signatures and often can be quantified with complementary image analysis tools. Such techniques are useful for monitoring specification compliance and evaluating true imaging performance for digital capture. Using the slanted-edge MTF technique described in ISO 12233, a variety of MTF examples associated with characteristics from the above list are shown for several actual digital capture devices.

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Diagnostics for Digital Capture using MTF

Modulation transfer function (MTF) metrology and interpretation for digital image capture devices has usually concentrated on mid- to high-frequency information, relative to the half-sampling frequency. These regions typically quantify characteristics and operations such as sharpening, limiting resolution, and aliasing. However, a potential wealth of low-frequency, visually significant information is often masked in existing measurement results because of spatial data truncation. For print or document scanners, this influences measurements in the spatial frequency range of 0 to 2.0 cycles/mm, where the effects of veiling flare, micro flare, and integrating cavity effect (ICE) often manifest themselves. Using a form of edge-gradient analysis based on slanted edges, we present a method for measurement of these characteristics. By carefully adapting this well-established technique, these phenomena can be quantified. We also show how, in many cases, these effects can be treated as other spread-function or device-MTF components. The theory and field metrology of several devices using the adapted technique are also presented.

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Low-frequency MTF estimation for digital imaging devices using slanted edge analysis

identify a region of interest (ROI)fit a linear equation to the centroid locationusing linear fit to line, project the image data along the edge direction to top or bottom edge of ROIapply window and compute edgederivativeyof this arraycompute the centroid of each line (LSF) A compute discrete Fourier transform (DFT) of this array‘bin’ data, sampled at 1/4 of original image samplingnormalize modulus as SFRcompute derivative in the x (pixel) direction using FIR filterreport resultstransform image data using the OECFOECFderive luminance record if data is R, G, B .store equationfor eachcolor recordcompute edge derivative of thisarray, and appl window to resultcompute discrete Fourier We describe a digital image processing method to measure translation error between color records, that can be computed from the acquired image. Several steps in the ISO 12233 standard for measurement of resolution for digital cameras are used to locate an edge for each color plane. The method has proven reliable and can be applied to images acquired from both digital cameras and scanners.

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Using Slanted Edge Analysis for Color Registration Measurement

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

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Don Williams

Papers

Refined Slanted-Edge Measurement from Practical Camera and Scanner Testing.

Diagnostics for Digital Capture using MTF

Low-frequency MTF estimation for digital imaging devices using slanted edge analysis

Using Slanted Edge Analysis for Color Registration Measurement

Evolution of slanted edge gradient SFR measurement