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The Computer Image is a unique book and CD-ROM package which provides a comprehensive overview of three converging areas of the computer image - computer graphics, image processing and computer vision.
How computer graphics transformed the computer from a calculating machine into an interactive medium, as seen through the histories of five technical objects. Most of us think of computer graphics as a relatively recent invention, enabling the spectacular visual effects and lifelike simulations we see in current films, television shows, and digital games. In fact, computer graphics have been around as long as the modern computer itself, and played a fundamental role in the development of our contemporary culture of computing. In Image Objects, Jacob Gaboury offers a prehistory of computer graphics through an examination of five technical objects--an algorithm, an interface, an object standard, a programming paradigm, and a hardware platform--arguing that computer graphics transformed the computer from a calculating machine into an interactive medium. Gaboury explores early efforts to produce an algorithmic solution for the calculation of object visibility; considers the history of the computer screen and the random-access memory that first made interactive images possible; examines the standardization of graphical objects through the Utah teapot, the most famous graphical model in the history of the field; reviews the graphical origins of the object-oriented programming paradigm; and, finally, considers the development of the graphics processing unit as the catalyst that enabled an explosion in graphical computing at the end of the twentieth century. The development of computer graphics, Gaboury argues, signals a change not only in the way we make images but also in the way we mediate our world through the computer--and how we have come to reimagine that world as computational.
Remotely-sensed images of the Earth provide information about the geographical distribution of natural and cultural features, as well as a record of changes in environmental conditions over time. This text offers technical guidance to those involved in processing and classifying such data.
Computer Image Processing and Recognition
The focus of this book is on providing a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics. Throughout, the authors concentrate on describing and analysing the underlying concepts rather than on presenting algorithms or pseudocode. As befits a modern introduction to this topic, a healthy balance is struck between discussing the underlying mathematics of the subject and the main topics covered: signal processing, data discretization, the theory of colour and different colour systems, operations in images, dithering and half-toning, warping and morphing, and image processing.
Computer Imaging: Digital Image Analysis and Processing brings together analysis and processing in a unified framework, providing a valuable foundation for understanding both computer vision and image processing applications. Taking an engineering approach, the text integrates theory with a conceptual and application-oriented style, allowing you to immediately understand how each topic fits into the overall structure of practical application development. Divided into five major parts, the book begins by introducing the concepts and definitions necessary to understand computer imaging. The second part describes image analysis and provides the tools, concepts, and models required to analyze digital images and develop computer vision applications. Part III discusses application areas for the processing of images, emphasizing human visual perception. Part IV delivers the information required to apply a CVIPtools environment to algorithm development. The text concludes with appendices that provide supplemental imaging information and assist with the programming exercises found in each chapter. The author presents topics as needed for understanding each practical imaging model being studied. This motivates the reader to master the topics and also makes the book useful as a reference. The CVIPtools software integrated throughout the book, now in a new Windows version, provides practical examples and encourages you to conduct additional exploration via tutorials and programming exercises provided with each chapter.
Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.
Complete Coverage of the Current Practice of Computer GraphicsComputer Graphics: From Pixels to Programmable Graphics Hardware explores all major areas of modern computer graphics, starting from basic mathematics and algorithms and concluding with OpenGL and real-time graphics. It gives students a firm foundation in today's high-performance graphic
Computer Vision and Image Processing contains review papers from the Computer Vision, Graphics, and Image Processing volume covering a large variety of vision-related topics. Organized into five parts encompassing 26 chapters, the book covers topics on image-level operations and architectures; image representation and recognition; and three-dimensional imaging. The introductory part of this book is concerned with the end-to-end performance of image gathering and processing for high-resolution edge detection. It proposes methods using mathematical morphology to provide a complete edge detection process that may be used with any slope approximating operator. This part also discusses the automatic control of low-level robot vision, presents an image partitioning method suited for parallel implementation, and describes invariant architectures for low-level vision. The subsequent two sections present significant topics on image representation and recognition. Topics covered include the use of the primitives chain code; the geometric properties of the generalized cone; efficient rendering and structural-statistical character recognition algorithms; multi-level thresholding for image segmentation; knowledge-based object recognition system; and shape decomposition method based on perceptual structure. The fourth part describes a rule-based expert system for recovering three-dimensional shape and orientation. A procedure of intensity-guided range sensing to gain insights on the concept of cooperative-and-iterative strategy is also presented in this part. The concluding part contains supplementary texts on texture segmentation using topographic labels and an improved algorithm for labeling connected components in a binary image. Additional algorithms for three-dimensional motion parameter determination and surface tracking in three-dimensional binary images are also provided.