Download or read book Visual Motion Analysis for Autonomous Navigation written by Chandra Shekhar and published by . This book was released on 1992 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unknown (motion and structure) parameters in each case are then estimated recursively using an iterated extended Kalman filter (IEKF), after initialization by simple methods. This feature-based approach requires interest points to be detected and matched over the image sequence. A new method is presented for the extraction and matching of feature points using a Gabor wavelet representation. In this method, feature points are extracted using scale interactions, and matched using labelled graph matching. Feature correspondence is interleaved with recursive estimation. Experimental results on two real image sequences are presented."
Download or read book Visual Motion Analysis for 3D Robot Navigation in Dynamic Environments written by Chunrong Yuan and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This chapter concentrates on visual motion analysis for the safe navigation of mobile robots in dynamic environment. The aim is to build one of the important navigation abilities for robot systems: the detection of obstacles for collision avoidance during the 3D autonomous flight of UAVs. In dynamic environment, not only the robot itself but also some other objects are moving. With the proposed approach, we have shown a robot vision system capable of understanding the natural environment, analyzing the different motions and making appropriate decisions. Most motion estimation algorithms work well with perfect image flow measurement but are very sensitive to noise and outliers. To overcome this problem, we have designed a complete computational procedure for robust 3D motion/structure recovery. A well-known image flow algorithm has been extended and improved for the robust detection of image flow vectors. In order to estimate the camera motion, we proposed a novel approach for the separation of independent motion and removal of outliers. The motion parameters of the camera and the 3D position and orientation of scene points are then recovered using a linear estimation approach. With the output of our visual motion analysis, we are able to facilitate a flying platform with obstacle detection and avoidance ability. As a result, safe and autonomous navigation of UAV systems can be achieved.
Download or read book Motion Vision written by J. Kolodko and published by IET. This book was released on 2005 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book deals with motion estimation for autonomous systems from a biological, algorithmic and digital perspective. An algorithm, which is based on the optical flow constraint equation, is described in detail.
Download or read book Statistical and Geometrical Approaches to Visual Motion Analysis written by Daniel Cremers and published by Springer Science & Business Media. This book was released on 2009-07-25 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the International Dagstuhl-Seminar on Statistical and Geometrical Approaches to Visual Motion Analysis, held in Dagstuhl Castle, Germany, in July 2008. The workshop focused on critical aspects of motion analysis, including motion segmentation and the modeling of motion patterns. The aim was to gather researchers who are experts in the different motion tasks and in the different techniques used; also involved were experts in the study of human and primate vision. The 15 revised full papers presented were carefully reviewed and selected from or initiated by the lectures given at the workshop. The papers are organized in topical sections on optical flow and extensions, human motion modeling, biological and statistical approaches, alternative approaches to motion analysis.
Download or read book Motion Analysis and Image Sequence Processing written by M. Ibrahim Sezan and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: An image or video sequence is a series of two-dimensional (2-D) images sequen tially ordered in time. Image sequences can be acquired, for instance, by video, motion picture, X-ray, or acoustic cameras, or they can be synthetically gen erated by sequentially ordering 2-D still images as in computer graphics and animation. The use of image sequences in areas such as entertainment, visual communications, multimedia, education, medicine, surveillance, remote control, and scientific research is constantly growing as the use of television and video systems are becoming more and more common. The boosted interest in digital video for both consumer and professional products, along with the availability of fast processors and memory at reasonable costs, has been a major driving force behind this growth. Before we elaborate on the two major terms that appear in the title of this book, namely motion analysis and image sequence processing, we like to place them in their proper contexts within the range of possible operations that involve image sequences. In this book, we choose to classify these operations into three major categories, namely (i) image sequence processing, (ii) image sequence analysis, and (iii) visualization. The interrelationship among these three categories is pictorially described in Figure 1 below in the form of an "image sequence triangle".
Download or read book Computational Analysis of Visual Motion written by Amar Mitiche and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image motion processing is important to machine vision systems because it can lead to the recovery of 3D structure and motion. Author Amar Mitiche offers a comprehensive mathematical treatment of this key subject in visual systems research. Mitiche examines the interpretation of point correspondences as well as the interpretation of straight line correspondences and optical flow. In addition, the author considers interpretation by knowledge-based systems and presents the relevant mathematical basis for 3D interpretation.
Download or read book End of Year Technical Report Dynamic Image Interpretation for Autonomous Vehicle Navigation written by E. M. Riseman and published by . This book was released on 1987 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In pursuit of the goal of achieving dynamic image interpretation for autonomous vehicle navigation, we have made significant progress in the knowledge-based interpretation of road scenes, in visual motion analysis, and in mobile robot navigation. This work has been supported by development of necessary software, tools, installation of appropriate hardware, and concurrent investigations into applicable techniques for image analysis.
Download or read book Motion Analysis and Object Recognition for Autonomous Navigation written by and published by . This book was released on 1992 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research in computer vision described in this final report is directed towards the achievement of autonomous vehicle navigation using passive visual sensing. For a modeled environment, we have implemented a navigation system incorporating reactive planning, and based on the identification of known landmarks in the 3D scene. Robust algorithms have been demonstrated for the recovery of pose--the position and orientation of the camera--from model matching between the image and known environment. For an unknown environment, a navigation system has been demonstrated in which image based homing is used to move between neighboring target locations. For a completely unknown environment, multi frame structure from motion algorithms have been developed which use image sequences for the reconstruction of the camera motion and environmental structure. In a partially modeled environment, the combination of pose recovery with triangulation over image sequences yields a robust, accurate algorithm for incremental acquisition of a 3D scene model. Lastly, a new framework for obstacle detection from motion has been developed and demonstrated experimentally. In the area of static image interpretation and object recognition, research has been done on perceptual organization, invariant features, 3D reconstruction, and the automatic learning of strategies for object recognition. We have developed a new approach to distinguishing figure from ground, a prerequisite for obstacle detection, based on perceptual grouping techniques.
Download or read book Spatial Coherence for Visual Motion Analysis written by W. James MacLean and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Spatial Coherence for Visual Motion Analysis, 2004, held in May 2004. The eleven revised full research papers presented went through two rounds of reviewing and improvement. The papers in this volume cover a wide range in the field of motion analysis that is a central problem in computer vision. The workshop examined techniques for integrating spatial coherence constraints during motion analysis of image sequences.
Download or read book Visual Navigation written by Yiannis Aloimonos and published by Psychology Press. This book was released on 2013-05-13 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: All biological systems with vision move about their environments and successfully perform many tasks. The same capabilities are needed in the world of robots. To that end, recent results in empirical fields that study insects and primates, as well as in theoretical and applied disciplines that design robots, have uncovered a number of the principles of navigation. To offer a unifying approach to the situation, this book brings together ideas from zoology, psychology, neurobiology, mathematics, geometry, computer science, and engineering. It contains theoretical developments that will be essential in future research on the topic -- especially new representations of space with less complexity than Euclidean representations possess. These representations allow biological and artificial systems to compute from images in order to successfully deal with their environments. In this book, the barriers between different disciplines have been smoothed and the workings of vision systems of biological organisms are made clear in computational terms to computer scientists and engineers. At the same time, fundamental principles arising from computational considerations are made clear both to empirical scientists and engineers. Empiricists can generate a number of hypotheses that they could then study through various experiments. Engineers can gain insight for designing robotic systems that perceive aspects of their environment. For the first time, readers will find: * the insect vision system presented in a way that can be understood by computational scientists working in computer vision and engineering; * three complete, working robotic navigation systems presented with all the issues related to their design analyzed in detail; * the beginning of a computational theory of direct perception, as advocated by Gibson, presented in detail with applications for a variety of problems; and * the idea that vision systems could compute space representations different from perfect metric descriptions -- and be used in robotic tasks -- advanced for both artificial and biological systems.
Download or read book Real time Motion Estimation for Autonomous Navigation written by Julian Paul Kolodko and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This thesis addresses the design, development and implementation of a motion measuring sensor for use in the context of autonomous navigation. The sensor combines both visual and range information in a robust estimation framework. Range is used to allow calculation of translational ground plane velocity, to ensure real-time constraints are met and to provide a simple means of segmenting the environment into coherently moving regions. A prototype sensor has been implemented using Field Programmable Gate Array technology. This has allowed a 'system on a chip' solution with the only external devices being sensors (camera and range) and primary memoly. The sensor can process images of up to 512*32 pixels resolution in realtime. This thesis shows that, in the context of autonomous navigation the concept of real-time is linked to both object dynamics and sensor sampling considerations. Real time is shown to be 16Hz in the test environment used in this thesis. A combination of offline simulation results (using artificially generated data mimicking the real world thus allowing quantitative performance analysis) and real-time experimental results illustrates the performance of our sensor. This thesis makes the following contributions: 1. It presents the design and implementation of an integrated motion sensing solution that utilises both range and vision to robustly estimate rigid, translational ground plane motion for the purpose of autonomous navigation. 2. It develops the concept of dynamic scale space - a technique that utilises assumed environmental dynamics to focus motion estimation on the closest object so that the sensor meets real time requirements. 3. It develops a simple, iterative robust averaging estimator based on the concept of Least Trimmed Squares. This estimator (the Least Trimmed Squared Variant or LISV estimator) does not require reordering of data or stochastic sampling and does not have parameters that must be tuned to suit the data. At every iteration, the LTSV estimator requires a simple update of threshold parameters, a single division plus two addition operations for each data element. The performance of the LTSV estimator is compared against more traditional estimators (least squares, median, least trimmed squared and the Lorentzian M-Estimator) demonstrating its rapid convergence and consistently low bias. The simplicity and rapid convergence of the estimator are achieved at the expense of statistical efficiency. 4. It demonstrates the use of range information as a means of segmenting the environment into regions we call blobs, under the assumption that each blob moves coherently. In the domain of custom hardware implementations of motion estimation, we believe our solution is the first that; 1. uses both range and visual data, 2. estimates motion using a robust estimation frame work and, 3. embeds the motion estimation process in a (dynamic) scale space framework.
Download or read book Omnidirectional Optical Flow and Visual Motion Detection for Autonomous Robot Navigation written by Irem Stratmann and published by . This book was released on 2007 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Vision in Vehicle Technology written by Antonio M. López and published by John Wiley & Sons. This book was released on 2017-02-17 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified view of the use of computer vision technology for different types of vehicles Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment). The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed. Key features: Presents the latest advances in the field of computer vision and vehicle technologies in a highly informative and understandable way, including the basic mathematics for each problem. Provides a comprehensive summary of the state of the art computer vision techniques in vehicles from the navigation and the addressable applications points of view. Offers a detailed description of the open challenges and business opportunities for the immediate future in the field of vision based vehicle technologies. This is essential reading for computer vision researchers, as well as engineers working in vehicle technologies, and students of computer vision.
Download or read book High Orders Motion Analysis written by Yan Sun and published by Springer Nature. This book was released on with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Vision and Navigation written by Oleg Sergiyenko and published by Springer Nature. This book was released on 2019-09-30 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a variety of perspectives on vision-based applications. These contributions are focused on optoelectronic sensors, 3D & 2D machine vision technologies, robot navigation, control schemes, motion controllers, intelligent algorithms and vision systems. The authors focus on applications of unmanned aerial vehicles, autonomous and mobile robots, industrial inspection applications and structural health monitoring. Recent advanced research in measurement and others areas where 3D & 2D machine vision and machine control play an important role, as well as surveys and reviews about vision-based applications. These topics are of interest to readers from diverse areas, including electrical, electronics and computer engineering, technologists, students and non-specialist readers. • Presents current research in image and signal sensors, methods, and 3D & 2D technologies in vision-based theories and applications; • Discusses applications such as daily use devices including robotics, detection, tracking and stereoscopic vision systems, pose estimation, avoidance of objects, control and data exchange for navigation, and aerial imagery processing; • Includes research contributions in scientific, industrial, and civil applications.
Download or read book Biomimetic Visual Navigation Architectures for Autonomous Intelligent Systems written by Vivek Pant and published by . This book was released on 2007 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent systems with even the bare minimum of sophistication require extensive computational power and complex processing units. At the same time, small insects like flies are adept at visual navigation, target pursuit, motionless hovering flight, and obstacle avoidance. Thus, biology provides engineers with an unconventional approach to solve complicated engineering design problems. Computational models of the neuronal architecture of the insect brain can provide algorithms for the development of software and hardware to accomplish sophisticated visual navigation tasks. In this research, we investigate biologically-inspired collision avoidance models primarily based on visual motion. We first present a comparative analysis of two leading collision avoidance models hypothesized in the insect brain. The models are simulated and mathematically analyzed for collision and non-collision scenarios. Based on this analysis it is proposed that along with the motion information, an estimate of distance from the obstacle is also required to reliably avoid collisions. We present models with tracking capability as solutions to this problem and show that tracking indirectly computes a measure of distance. We present a camera-based implementation of the collision avoidance models with tracking. The camera-based system was tested for collision and non-collision scenarios to verify our simulation claims that tracking improves collision avoidance. Next, we present a direct approach to estimate the distance from an obstacle by utilizing non-directional speed. We describe two simplified non-directional speed estimation models: the non-directional multiplication (ND-M) sensor, and the non-directional summation (ND-S) sensor. We also analyze the mathematical basis of their speed sensitivity. An analog VLSI chip was designed and fabricated to implement these models in silicon. The chip was fabricated in a 0.18 um process and its characterization results are reported here. As future work, the tracking algorithm and the collision avoidance models may be implemented as a sensor chip and used for autonomous navigation by intelligent systems.
Download or read book Machine Learning for Vision Based Motion Analysis written by Liang Wang and published by Springer Science & Business Media. This book was released on 2010-11-18 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
