The book comprehensively covers almost all aspects of stereo vision. In addition reader can find topics from defining knowledge gaps to the state of the art algorithms as well as current application trends of stereo vision to the development of intelligent hardware modules and smart cameras. It would not be an exaggeration if this book is considered to be one of the most comprehensive books published in reference to the current research in the field of stereo vision. Research topics covered in this book makes it equally essential and important for students and early career researchers as well as senior academics linked with computer vision.
Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints.
A revelatory account of the brain's capacity for change When neuroscientist Susan Barry was fifty years old, she experienced the sense of immersion in a three dimensional world for the first time. Skyscrapers on street corners appeared to loom out toward her like the bows of giant ships. Tree branches projected upward and outward, enclosing and commanding palpable volumes of space. Leaves created intricate mosaics in 3D. Barry had been cross-eyed and stereoblind since early infancy. After half a century of perceiving her surroundings as flat and compressed, on that day she saw the city of Manhattan in stereo depth for first time in her life. As a neuroscientist, she understood just how extraordinary this transformation was, not only for herself but for the scientific understanding of the human brain. Scientists have long believed that the brain is malleable only during a "critical period" in early childhood. According to this theory, Barry's brain had organized itself when she was a baby to avoid double vision - and there was no way to rewire it as an adult. But Barry found an optometrist who prescribed a little-known program of vision therapy; after intensive training, Barry was ultimately able to accomplish what other scientists and even she herself had once considered impossible. Dubbed "Stereo Sue" by renowned neurologist Oliver Sacks, Susan Barry tells her own remarkable journey and celebrates the joyous pleasure of our senses.
An Analog VLSI System for Stereoscopic Vision investigates the interaction of the physical medium and the computation in both biological and analog VLSI systems by synthesizing a functional neuromorphic system in silicon. In both the synthesis and analysis of the system, a point of view from within the system is adopted rather than that of an omniscient designer drawing a blueprint. This perspective projects the design and the designer into a living landscape. The motivation for a machine-centered perspective is explained in the first chapter. The second chapter describes the evolution of the silicon retina. The retina accurately encodes visual information over orders of magnitude of ambient illumination, using mismatched components that are calibrated as part of the encoding process. The visual abstraction created by the retina is suitable for transmission through a limited bandwidth channel. The third chapter introduces a general method for interchip communication, the address-event representation, which is used for transmission of retinal data. The address-event representation takes advantage of the speed of CMOS relative to biological neurons to preserve the information of biological action potentials using digital circuitry in place of axons. The fourth chapter describes a collective circuit that computes stereodisparity. In this circuit, the processing that corrects for imperfections in the hardware compensates for inherent ambiguity in the environment. The fifth chapter demonstrates a primitive working stereovision system. An Analog VLSI System for Stereoscopic Vision contributes to both computer engineering and neuroscience at a concrete level. Through the construction of a working analog of biological vision subsystems, new circuits for building brain-style analog computers have been developed. Specific neuropysiological and psychophysical results in terms of underlying electronic mechanisms are explained. These examples demonstrate the utility of using biological principles for building brain-style computers and the significance of building brain-style computers for understanding the nervous system.
The book presents a wide range of innovative research ideas and current trends in stereo vision. The topics covered in this book encapsulate research trends from fundamental theoretical aspects of robust stereo correspondence estimation to the establishment of novel and robust algorithms as well as applications in a wide range of disciplines. Particularly interesting theoretical trends presented in this book involve the exploitation of the evolutionary approach, wavelets and multiwavelet theories, Markov random fields and fuzzy sets in addressing the correspondence estimation problem. Novel algorithms utilizing inspiration from biological systems (such as the silicon retina imager and fish eye) and nature (through the exploitation of the refractive index of liquids) make this book an interesting compilation of current research ideas.
What is Computer Stereo Vision Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. This is similar to the biological process of stereopsis. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Computer stereo vision Chapter 2: 3D reconstruction Chapter 3: Active contour model Chapter 4: Harris affine region detector Chapter 5: Foreground detection Chapter 6: Matrix Chernoff bound Chapter 7: Similarity Chapter 8: Structural similarity Chapter 9: Variance function Chapter 10: Fréchet inception distance (II) Answering the public top questions about computer stereo vision. (III) Real world examples for the usage of computer stereo vision in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Computer Stereo Vision.
This book addresses an area of perception engineering which deals with constructive processes. A model of the environment is analyzed using the information acquired from mUltiple viewpoints of multiple disparate sensors at multiple time instants. Although the role of successive model building and active exploration of the environment, as is discussed in this book, is of great importance, only a few researchers of machine perception have thus far addressed the problem in these directions. Krotkov's book, which is a modification and continuation of his highly successful dissertation, focuses on active exploratory sensing in the context of spatial layout perception. He uses stereo and focus to obtain distance By information, and to eventually develop cooperative combining techniques. means of a stereo system with verging cameras, it is demonstrated that the distance measurements can be significantly improved by combining two sources. In addition, the problem of merging information from the multiple views is discussed in detail. As the field of perception engineering seems to be of growing scientific and applied importance, both practitioners and researchers in machine perception will find this book a valuable addition to their libraries. RameshJain Series Editor Acknowledgements I would like to thank Professor Ruzena Bajcsy for her constant encouragement and guidance during the five years of research leading to the dissertation upon which this book is based. Without her help in all matters, this work would never have been possible.
When planning road construction measures, it is essential to have up-to-date information on road conditions. If this information is not to be obtained manually, it is currently obtained using laser scanners mounted on mobile mapping vehicles, which can measure the 3D road profile. However, a large number of mobile mapping vehicles would be necessary to record an entire road network on a regular basis. Since 2D road damages can be found automatically on monocular camera images, the idea was born to use a stereo camera system to capture the 3D profile of roads. With stereo camera systems, it would be possible to equip a large number of vehicles and regularly collect data from large road networks. In this thesis, the potential applications of a stereo camera system for measuring road profiles, which is mounted behind the windshield of a vehicle, are investigated. Since this requires a calibration of the stereo camera system, but the effort for the user should be kept low, the camera self-calibration for this application is also examined. 3D reconstruction from stereoscopic images is a well-studied topic, but its application on road surfaces with little and repetitive textures requires special algorithms. For this reason, a new stereo method was developed. It is based on the plane-sweep approach in combination with semi-global matching. It was tested with different measures for pixel comparison. Furthermore, the plane-sweep approach was implemented in a neural network that solves the stereo correspondence problem in a single step. It uses the stereoscopic images as input and provides an elevation image as output. A completely new approach was developed for the self-calibration of mono cameras and stereo camera systems. Previous methods search for feature points in several images of the same scene. The points are matched between the images and used for the calibration. In contrast to these methods, the proposed method uses feature maps instead of feature points to compare multiple views of one and the same plane. To estimate the unknown parameters, the backpropagation algorithm is used together with the gradient descent method. The measurements obtained by stereoscopic image processing were compared with those obtained by industrial laser scanners. They show that both measurements are very close to each other and that a stereoscopic camera system is in principle suitable for capturing the surface profile of a road. Experiments show that the proposed self-calibration method is capable of estimating all parameters of a complex camera model, including lens distortion, with high precision. Bei der Planung von Straßenbaumaßnahmen ist es unabdingbar, über aktuelle Informationen über den Straßenzustand zu verfügen. Sollen diese Informationen nicht manuell gewonnen werden, werden derzeit Messfahrzeug mit Laserscannern verwendet, welche das 3D-Straßenprofil vermessen können. Für die regelmäßige Erfassung eines gesamten Straßennetzes wäre jedoch eine große Anzahl von Messfahrzeugen erforderlich. Da 2D-Straßenschäden automatisch auf monokularen Kamerabildern gefunden werden können, entstand die Idee, ein Stereokamerasystem zur Erfassung des 3D-Profils zu verwenden. Eine große Anzahl von Fahrzeugen könnte damit ausgerüstet werden und es könnten regelmäßig Daten von großen Straßennetzen erfasst werden. In dieser Arbeit werden die Einsatzmöglichkeiten eines Stereokamerasystems zur Messung von Straßenprofilen untersucht, dass sich hinter der Windschutzscheibe eines Fahrzeugs befindet. Da hierzu das Stereokamerasystems kalibriert sein muss, der Aufwand für den Anwender aber geringgehalten werden soll, wird außerdem die Selbstkalibrierung für diesen Einsatzzweck untersucht. Die 3D-Rekonstruktion aus stereoskopischen Bildern ist ein viel untersuchtes Thema, aber ihre Anwendung auf Straßenoberflächen mit wenig und sich wiederholenden Texturen erfordert spezielle Algorithmen. Aus diesem Grund wurde ein neues Stereoverfahren entwickelt. Es basiert auf dem Plane-sweep-Ansatz in Kombination mit Semi-global Matching. Es wurde mit verschiedene Maßen für den Vergleich von Pixeln getestet. Darüber hinaus wurde der Plane-sweep-Ansatz in einem neuronalen Netzwerk implementiert, das das Stereo-Korrespondenzproblem in einem einzigen Schritt löst. Es verwendet die stereoskopischen Bilder als Eingabe und liefert als Ausgabe ein Höhenbild. Für die Selbstkalibrierung von Monokameras und Stereokamerasystemen wurde ein völlig neuer Ansatz entwickelt. Bisherige Methoden suchen nach Merkmalspunkten in mehreren Bildern der gleichen Szene. Die Punkte werden zwischen den Bildern zugeordnet und für die Kalibrierung verwendet. Die vorgeschlagene Methode verwendet anstelle von Merkmalspunkten Feature-Maps um mehrere Ansichten derselben Ebene zu vergleichen. Zur Schätzung der unbekannten Parameter wird der Backpropagation-Algorithmus zusammen mit dem Gradientenabstiegsverfahren verwendet. Die durch stereoskopische Bildverarbeitung erhaltenen Messungen wurden mit Messungen von industriellen Laserscannern verglichen. Sie zeigen, dass beide sehr nahe beieinander liegen und dass ein Stereokamerasystem für die Erfassung des Oberflächenprofils einer Straße grundsätzlich geeignet ist. Experimente zeigen, dass die neue Selbstkalibrierungsmethode in der Lage ist, alle Parameter eines komplexen Kameramodells, einschließlich der Linsenverzerrung, mit hoher Präzision abzuschätzen.
A neurobiologist reexamines the personal nature of perception in this groundbreaking guide to a new model for our senses. We think of perception as a passive, mechanical process, as if our eyes are cameras and our ears microphones. But as neurobiologist Susan R. Barry argues, perception is a deeply personal act. Our environments, our relationships, and our actions shape and reshape our senses throughout our lives. This idea is no more apparent than in the cases of people who gain senses as adults. Barry tells the stories of Liam McCoy, practically blind from birth, and Zohra Damji, born deaf, in the decade following surgeries that restored their senses. As Liam and Zohra learned entirely new ways of being, Barry discovered an entirely new model of the nature of perception. Coming to Our Senses is a celebration of human resilience and a powerful reminder that, before you can really understand other people, you must first recognize that their worlds are fundamentally different from your own.
NATIONAL BESTSELLER • From “the poet laureate of medicine" (The New York Times) and the author of the classic The Man Who Mistook his Wife for a Hat comes a fascinating exploration of the remarkable, unpredictable ways that our brains cope with the loss of sight by finding rich new forms of perception. “Elaborate and gorgeously detailed.... Again and again, Sacks invites readers to imagine their way into minds unlike their own, encouraging a radical form of empathy.” —Los Angeles Times With compassion and insight, Dr. Oliver Sacks again illuminates the mysteries of the brain by introducing us to some remarkable characters, including Pat, who remains a vivacious communicator despite the stroke that deprives her of speech, and Howard, a novelist who loses the ability to read. Sacks investigates those who can see perfectly well but are unable to recognize faces, even those of their own children. He describes totally blind people who navigate by touch and smell; and others who, ironically, become hyper-visual. Finally, he recounts his own battle with an eye tumor and the strange visual symptoms it caused. As he has done in classics like The Man Who Mistook his Wife for a Hat and Awakenings, Dr. Sacks shows us that medicine is both an art and a science, and that our ability to imagine what it is to see with another person's mind is what makes us truly human.
