Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more. Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.

An examination of how artists have combined performance and moving image for decades, anticipating our changing relation to images in the internet era. In Performing Image, Isobel Harbison examines how artists have combined performance and moving image in their work since the 1960s, and how this work anticipates our changing relations to images since the advent of smart phones and the spread of online prosumerism. Over this period, artists have used a variety of DIY modes of self-imaging and circulation—from home video to social media—suggesting how and why Western subjects might seek alternative platforms for self-expression and self-representation. In the course of her argument, Harbison offers close analyses of works by such artists as Robert Rauschenberg, Yvonne Rainer, Mark Leckey, Wu Tsang, and Martine Syms. Harbison argues that while we produce images, images also produce us—those that we take and share, those that we see and assimilate through mass media and social media, those that we encounter in museums and galleries. Although all the artists she examines express their relation to images uniquely, they also offer a vantage point on today's productive-consumptive image circuits in which billions of us are caught. This unregulated, all-encompassing image performativity, Harbison writes, puts us to work, for free, in the service of global corporate expansion. Harbison offers a three-part interpretive framework for understanding this new proximity to images as it is negotiated by these artworks, a detailed outline of a set of connected practices—and a declaration of the value of art in an economy of attention and a crisis of representation.

This book constitutes the refereed proceedings of the 15th International Conference on Image Analysis and Processing, ICIAP 2009, held in Vietri sul Mare, Italy, in September 2009. The 107 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 168 submissions. The papers are organized in topical sections on computer graphics and image processing, low and middle level processing, 2D and 3D segmentation, feature extraction and image analysis, object detection and recognition, video analysis and processing, pattern analysis and classification, learning, graphs and trees, applications, shape analysis, face analysis, medical imaging, and image analysis and pattern recognition.

Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection.

This three-book set constitutes the refereed proceedings of the Second International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2018, held in Solapur, India, in December 2018. The 173 revised full papers presented were carefully reviewed and selected from 374 submissions. The papers are organized in topical sections in the tree volumes. Part I: computer vision and pattern recognition; machine learning and applications; and image processing. Part II: healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications.

The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

This book constitutes the refereed proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'99, held in Cambridge, UK, in September 1999. The 133 revised full papers presented were carefully reviewed and selected from a total of 213 full-length papers submitted. The book is divided into topical sections on data-driven segmentation, segmentation using structural models, image processing and feature detection, surfaces and shape, measurement and interpretation, spatiotemporal and diffusion tensor analysis, registration and fusion, visualization, image-guided intervention, robotic systems, and biomechanics and simulation.

After a slow and somewhat tentative beginning, machine vision systems are now finding widespread use in industry. So far, there have been four clearly discernible phases in their development, based upon the types of images processed and how that processing is performed: (1) Binary (two level) images, processing in software (2) Grey-scale images, processing in software (3) Binary or grey-scale images processed in fast, special-purpose hardware (4) Coloured/multi-spectral images Third-generation vision systems are now commonplace, although a large number of binary and software-based grey-scale processing systems are still being sold. At the moment, colour image processing is commercially much less significant than the other three and this situation may well remain for some time, since many industrial artifacts are nearly monochrome and the use of colour increases the cost of the equipment significantly. A great deal of colour image processing is a straightforward extension of standard grey-scale methods. Industrial applications of machine vision systems can also be sub divided, this time into two main areas, which have largely retained distinct identities: (i) Automated Visual Inspection (A VI) (ii) Robot Vision (RV) This book is about a fifth generation of industrial vision systems, in which this distinction, based on applications, is blurred and the processing is marked by being much smarter (i. e. more "intelligent") than in the other four generations.

Magnetic resonance imaging has already become a most valuable imaging modality in the diagnostic work-up of musculoskeletal neoplasms. While high accuracy of MRI for staging purposes has been proven, we will focus in this monograph on the characterization of primary bone and soft tissue tumors by MRI. The major purpose of this monograph is to provide an atlas of magnetic resonance features of primary bone and soft tissue tumors for radiologists, orthopedic surgeons and physiotherapists. The results presented are based on investigations of 94 primary bone and soft tissue tumors and mimicking conditions by magnetic resonance imaging. Although the scale of the material allows for statistical handling, the number of patients per subgroup is too small to come to definite conclusions. We will therefore limit ourselves to the description of and comments on a great number of cases to illustrate the diagnostic potential of this new imaging modality. We would like to thank the anonymous cooperators: referring clinicians, pathologists, nurses, technicians and secretaries whose help enabled us to present this monograph. We would also like to express our gratitude to the firms Siemens AG and Schering AG for technical support.

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.