In very few mobile robotic applications stereo vision based navigation and mapping is used because dealing with stereo images is very hard and very time consuming. Despite all the problems, stereo vision still becomes one of the most important resources of knowing the world for a mobile robot because imaging provides much more information than most other sensors. Real robotic applications are very complicated because besides the problems of finding how the robot should behave to complete the task at hand, the problems faced while controlling the robot’s internal parameters bring high computational load. Thus, finding the strategy to be followed in a simulated world and then applying this on real robot for real applications is preferable. In this study, we describe an algorithm for object recognition and cognitive map formation using stereo image data in a 3D virtual world where 3D objects and a robot with active stereo imaging system are simulated. Stereo imaging system is simulated so that the actual human visual system properties are parameterized. Only the stereo images obtained from this world are supplied to the virtual robot. By applying our disparity algorithm, depth map for the current stereo view is extracted. Using the depth information for the current view, a cognitive map of the environment is updated gradually while the virtual agent is exploring the environment. The agent explores its environment in an intelligent way using the current view and environmental map information obtained up to date. Also, during exploration if a new object is observed, the robot turns around it, obtains stereo images from different directions and extracts the model of the object in 3D. Using the available set of possible objects, it recognizes the object.

The series of ISCIS (International Symposium on Computer and Information Sciences) symposia have been held each year since 1986, mostly in Turkey and occasionally abroad. It is the main computer science and engineering meeting organized by Turkish academics and was founded by Erol Gelenbe. Each year ISCIS attracts a signi?cant number of international participants from all over the world. The 19th ISCIS was organized by Bilkent University, Department of Computer Engineering, and was held in Kemer-Antalya, Turkey during 27–29 October 2004. For ISCIS 2004, a total of 335 papers went through the review process and a large number of high-quality papers competed for acceptance. This volume of the Springer Lecture Notes in Computer Science (LNCS) series contains 100 of those papers that broadly fall into the following areas of interest: arti?cial int- ligence and machine learning, computer graphics and user interfaces, computer networksand security, computer vision and image processing,databasesystems, modeling and performance evaluation, natural languageprocessing, parallel and distributed computing, real-time control applications, software engineering and programming systems, and theory of computing.

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.

Computer vision researchers have been frustrated in their attempts to automatically derive depth information from conventional two-dimensional intensity images. Research on "shape from texture", "shape from shading", and "shape from focus" is still in a laboratory stage and had not seen much use in commercial machine vision systems. A range image or a depth map contains explicit information about the distance from the sensor to the object surfaces within the field of view in the scene. Information about "surface geometry" which is important for, say, three-dimensional object recognition is more easily extracted from "2 1/2 D" range images than from "2D" intensity images. As a result, both active sensors such as laser range finders and passive techniques such as multi-camera stereo vision are being increasingly utilized by vision researchers to solve a variety of problems. This book contains chapters written by distinguished computer vision researchers covering the following areas: Overview of 3D Vision Range Sensing Geometric Processing Object Recognition Navigation Inspection Multisensor Fusion A workshop report, written by the editors, also appears in the book. It summarizes the state of the art and proposes future research directions in range image sensing, processing, interpretation, and applications. The book also contains an extensive, up-to-date bibliography on the above topics. This book provides a unique perspective on the problem of three-dimensional sensing and processing; it is the only comprehensive collection of papers devoted to range images. Both academic researchers interested in research issues in 3D vision and industrial engineers in search of solutions to particular problems will find this a useful reference book.

Over the last decade, significant progress has been made in 3D imaging research. As a result, 3D imaging methods and techniques are being employed for various applications, including 3D television, intelligent robotics, medical imaging, and stereovision. Depth Map and 3D Imaging Applications: Algorithms and Technologies present various 3D algorithms developed in the recent years and to investigate the application of 3D methods in various domains. Containing five sections, this book offers perspectives on 3D imaging algorithms, 3D shape recovery, stereoscopic vision and autostereoscopic vision, 3D vision for robotic applications, and 3D imaging applications. This book is an important resource for professionals, scientists, researchers, academics, and software engineers in image/video processing and computer vision.

could be useful for object recognition tasks.