In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cortex, and the high level PFC learning centers. The corresponding visual genome model (VGM), begun in 2014, is introduced herein as the basis for a visual genome project analogous to the Human Genome Project funded by the US government. The VGM introduces volume learning principles and Visual DNA (VDNA) taking a multivariate approach beyond deep neural networks. Volume learning is modeled as programmable learning and reasoning agents, providing rich methods for structured agent classification networks. Volume learning incorporates a massive volume of multivariate features in various data space projections, collected into strands of Visual DNA, analogous to human DNA genes. VGM lays a foundation for a visual genome project to sequence VDNA as visual genomes in a public database, using collaborative research to move synthetic vision science forward and enable new applications. Bibliographical references are provided to key neuroscience, computer vision, and deep learning research, which form the basis for the biologically plausible VGM model and the synthetic visual pathway. The book also includes graphical illustrations and C++ API reference materials to enable VGM application programming. Open source code licenses are available for engineers and scientists. Scott Krig founded Krig Research to provide some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Krig has worked for major corporations and startups in the areas of machine learning, computer vision, imaging, graphics, robotics and automation, computer security and cryptography. He has authored international patents in the areas of computer architecture, communications, computer security, digital imaging, and computer vision, and studied at Stanford. Scott Krig is the author of the English/Chinese Springer book Computer Vision Metrics, Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning, Textbook Edition, as well as other books, articles, and papers.

A concept of operations (CONOPS) for the Commercial and Business (CaB) aircraft synthetic vision systems (SVS) is described. The CaB SVS is expected to provide increased safety and operational benefits in normal and low visibility conditions. Providing operational benefits will promote SVS implementation in the fleet, improve aviation safety, and assist in meeting the national aviation safety goal. SVS will enhance safety and enable consistent gate-to-gate aircraft operations in normal and low visibility conditions. The goal for developing SVS is to support operational minima as low as Category IIIb in a variety of environments. For departure and ground operations, the SVS goal is to enable operations with a runway visual range of 300 feet. The system is an integrated display concept that provides a virtual visual environment. The SVS virtual visual environment is composed of three components: an enhanced intuitive view of the flight environment, hazard and obstacle detection and display.

The previous edition of the International Encyclopedia of Ergonomics and Human Factors made history as the first unified source of reliable information drawn from many realms of science and technology and created specifically with ergonomics professionals in mind. It was also a winner of the Best Reference Award 2002 from the Engineering Libraries

Avionics provide crews and passengers with an array of capabilities. Cockpit crews can operate with fewer pilots, greater efficiency, and immediate critical information. Passengers can enjoy the ultimate in inflight entertainment: live television and audio broadcasts and access to the Internet and e-mail. Since avionics are the among most ex

To give mobile robots real autonomy, and to permit them to act efficiently in a diverse, cluttered, and changing environment, they must be equipped with powerful tools for perception and reasoning. Artificial Vision for Mobile Robots presents new theoretical and practical tools useful for providing mobile robots with artificial vision in three dimensions, including passive binocular and trinocular stereo vision, local and global 3D map reconstructions, fusion of local 3D maps into a global 3D map, 3D navigation, control of uncertainty, and strategies of perception. Numerous examples from research carried out at INRIA with the Esprit Depth and Motion Analysis project are presented in a clear and concise manner. Nicolas Ayache is Research Director at INRIA, Le Chesnay, France. Contents. General Introduction. Stereo Vision. Introduction. Calibration. Image Representation. Binocular Stereo Vision Constraints. Binocular Stereo Vision Algorithms. Experiments in Binocular Stereo Vision. Trinocular Stereo Vision, Outlook. Multisensory Perception. Introduction. A Unified Formalism. Geometric Representation. Construction of Visual Maps. Combining Visual Maps. Results: Matching and Motion. Results: Matching and Fusion. Outlook.