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 Motion Vision written by J. Kolodko and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is divided into eight sections and four parts. Chapter 1 of the paper includes introduction of the book. Chapter 2, discusses motion estimation theory. (Chapter 3) elaborates motion estimation problem. Chapter 4 is devoted to consideration of the issue of real-time motion estimation in the context of autonomous navigation. In Chapter 5 motion estimation algorithm is considered in detail. In Chapter 6 the VHDL hardware description language is introduced, which is commonly used in FPGA design. Chapter 7 leaves the details of VHDL behind and considers design of sensor and the specific issues that arose in the development of our sensor

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 Robust Real Time Visual Odometry for Autonomous Ground Vehicles written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating the motion of an agent, such as a self-driving vehicle or mobile robot, is an essential requirement for many modern autonomy applications. Real-time and accurate position estimates are essential for navigation, perception and control, especially in previously unknown environments. Using cameras and Visual Odometry (VO) provides an effective way to achieve such motion estimation. Visual odometry is an active area of research in computer vision and mobile robotics communities, as the problem is still a challenging one. In this thesis, a robust real-time feature-based visual odometry algorithm will be presented. The algorithm utilizes a stereo camera which enables estimation in true scale and easy startup of the system. A distinguishing aspect of the developed algorithm is its utilization of a local map consisting of sparse 3D points for tracking and motion estimation. This results in the full history of each feature being utilized for motion estimation. Hence, drift in the ego-motion estimates are greatly reduced, enabling long-term operation over prolonged distances. Furthermore, the algorithm employs Progressive Sample Consensus (PROSAC) in order to increase robustness against outliers. Extensive evaluations on the challenging KITTI and New College datasets are presented. KITTI dataset was collected by a vehicle driving in the city of Karlsruhe in Germany, and represents one of the most commonly used datasets in evaluating self-driving algorithms. The New College dataset was collected by a mobile robot traversing within New College grounds in Oxford. Moreover, experiments on custom data are performed and results are presented.

Download or read book Autonomous Navigation in Dynamic Environments written by Christian Laugier and published by Springer. This book was released on 2007-10-14 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a foundation for a broad class of mobile robot mapping and navigation methodologies for indoor, outdoor, and exploratory missions. It addresses the challenging problem of autonomous navigation in dynamic environments, presenting new ideas and approaches in this emerging technical domain. Coverage discusses in detail various related challenging technical aspects and addresses upcoming technologies in this field.

Download or read book Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles written by Francesco Fanelli and published by Springer. This book was released on 2019-04-16 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on pose estimation algorithms for Autonomous Underwater Vehicles (AUVs). After introducing readers to the state of the art, it describes a joint endeavor involving attitude and position estimation, and details the development of a nonlinear attitude observer that employs inertial and magnetic field data and is suitable for underwater use. In turn, it shows how the estimated attitude constitutes an essential type of input for UKF-based position estimators that combine position, depth, and velocity measurements. The book discusses the possibility of including real-time estimates of sea currents in the developed estimators, and highlights simulations that combine real-world navigation data and experimental test campaigns to evaluate the performance of the resulting solutions. In addition to proposing novel algorithms for estimating the attitudes and positions of AUVs using low-cost sensors and taking into account magnetic disturbances and ocean currents, the book provides readers with extensive information and a source of inspiration for the further development and testing of navigation algorithms for AUVs.

Download or read book Motion Vision written by J. Kolodko and published by Institution of Engineering & Technology. This book was released on 2005 with total page 468 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 GRADIENT BASED BLOCK MATCHING MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2024-04-17 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first project, gui_motion_analysis_gbbm.py, is designed to streamline motion analysis in videos using the Gradient-Based Block Matching Algorithm (GBBM) alongside a user-friendly Graphical User Interface (GUI). It encompasses various objectives, including intuitive GUI design with Tkinter, enabling video playback control, performing optical flow analysis, and allowing parameter configuration for tailored motion analysis. The GUI also facilitates interactive zooming, frame-wise analysis, and offers visual feedback through motion vector overlays. Robust error handling and multi-instance support enhance stability and usability, while dynamic title updates provide context within the interface. Overall, the project empowers users with a versatile tool for comprehensive motion analysis in videos. By integrating the GBBM algorithm with an intuitive GUI, gui_motion_analysis_gbbm.py simplifies motion analysis in videos. Its objectives range from GUI design to parameter configuration, enabling users to control video playback, perform optical flow analysis, and visualize motion patterns effectively. With features like interactive zooming, frame-wise analysis, and visual feedback, users can delve into motion dynamics seamlessly. Robust error handling ensures stability, while multi-instance support allows for concurrent analysis. Dynamic title updates enhance user awareness, culminating in a versatile tool for in-depth motion analysis. The second project, gui_motion_analysis_gbbm_pyramid.py, is dedicated to offering an accessible interface for video motion analysis, employing the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach. Its objectives encompass several crucial aspects. Primarily, the project responds to the demand for motion analysis in video processing across diverse domains like computer vision and robotics. By integrating the GBBM algorithm into a GUI, it democratizes motion analysis, catering to users without specialized programming or computer vision skills. Leveraging the GBBM algorithm's effectiveness, particularly with the Pyramid Approach, enhances performance and robustness, enabling accurate motion estimation across various scales. The GUI offers extensive control options and visualization features, empowering users to customize analysis parameters and inspect motion dynamics comprehensively. Overall, this project endeavors to advance video processing and analysis by providing an intuitive interface backed by cutting-edge algorithms, fostering accessibility and efficiency in motion analysis tasks. The third project, gui_motion_analysis_gbbm_adaptive.py, introduces a GUI application for video motion estimation, employing the Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size. Users can interact with video files, control playback, navigate frames, and visualize optical flow between consecutive frames, facilitated by features like zooming and panning. Developed with Tkinter in Python, the GUI provides intuitive controls for adjusting motion estimation parameters and playback options upon launch. At its core, the application dynamically adjusts block sizes based on local gradient magnitude, enhancing motion estimation accuracy, especially in areas with varying complexity. Utilizing PIL and OpenCV libraries, it handles image processing tasks and video file operations, enabling users to interact with the video display canvas for enhanced analysis. Overall, gui_motion_analysis_gbbm_adaptive.py offers a versatile solution for motion analysis in videos, empowering users with visualization tools and parameter customization for diverse applications like video compression and object tracking. The fourth project, gui_motion_analysis_gbbm_lucas_kanade.py, introduces a GUI for motion estimation in videos, incorporating both the Gradient-Based Block Matching Algorithm (GBBM) and Lucas-Kanade Optical Flow. It begins by importing necessary libraries such as tkinter for GUI development, PIL for image processing, imageio for video file handling, cv2 for computer vision operations, and numpy for numerical computation. The VideoGBBM_LK_OpticalFlow class serves as the application container, initializing attributes and defining methods for video loading, playback control, parameter setting, frame display, and optical flow visualization. With features like zooming, panning, and event handling for user interactions, the script offers a comprehensive tool for visualizing and analyzing motion dynamics in videos using two distinct optical flow estimation techniques. The fifth project, gui_motion_analysis_gbbm_sift.py, introduces a GUI application for optical flow analysis in videos, employing both the Gradient-Based Block Matching Algorithm (GBBM) and Scale-Invariant Feature Transform (SIFT). It begins by importing essential libraries such as tkinter for GUI development, PIL for image processing, imageio for video handling, and OpenCV for computer vision tasks like optical flow computation. The VideoGBBM_SIFT_OpticalFlow class orchestrates the application, initializing GUI elements and defining methods for video loading, playback control, frame display, and optical flow computation using both GBBM and SIFT algorithms. With features for parameter adjustment, frame navigation, zooming, and event handling for user interactions, the script offers a user-friendly interface for in-depth optical flow analysis, enabling insights into motion patterns and dynamics within videos. The sixth project, gui_motion_analysis_gbbm_orb.py script, offers a user-friendly interface for motion estimation in videos, utilizing both the Gradient-Based Block Matching Algorithm (GBBM) and ORB (Oriented FAST and Rotated BRIEF) optical flow techniques. Its primary goal is to enable users to analyze and visualize motion dynamics within video files effortlessly. The GUI application provides functionalities for opening video files, navigating frames, adjusting parameters like zoom scale and step size, and controlling playback with buttons for play, pause, stop, next frame, and previous frame. Key to the application's functionality is its ability to compute and visualize optical flow using both GBBM and ORB algorithms. Optical flow, depicting object motion in videos, is represented with vectors overlaid on video frames, aiding users in understanding motion patterns and dynamics. Interactive features such as mouse wheel zooming and dragging enhance user exploration of video frames and optical flow visualizations, allowing dynamic adjustment of viewing perspective to focus on specific regions or analyze motion at different scales. Overall, this project provides a comprehensive tool for video motion analysis, merging user-friendly interface elements with advanced motion estimation techniques to empower users in tasks ranging from surveillance to computer vision research. The seventh project showcases object tracking using the Gradient-Based Block Matching Algorithm (GBBM), vital in various computer vision applications like surveillance and robotics. By continuously locating and tracking objects of interest in video streams, it highlights GBBM's practical application for real-time tracking. The GUI interface simplifies interaction with video files, allowing easy opening and visualization of frames. Users control playback, navigate frames, and adjust zoom scale, while the heart of the project lies in GBBM's implementation for tracking objects. GBBM estimates object motion by comparing pixel blocks between consecutive frames, generating motion vectors that describe the object's movement. Users can select regions of interest for tracking, adjust algorithm parameters, and receive visual feedback through dynamically adjusting bounding boxes around tracked objects, making it an educational tool for experimenting with object tracking techniques within an accessible interface. The eight project endeavors to create an application for object tracking using the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach, catering to various computer vision applications like surveillance and autonomous vehicles. Built with Tkinter in Python, the user-friendly interface presents controls for video display, object tracking, and parameter adjustment upon launch. Users can load video files, play, pause, navigate frames, and adjust zoom levels effortlessly. Central to the application is the GBBM algorithm with a pyramid approach for robust object tracking. By refining search spaces at multiple resolutions, it efficiently estimates motion vectors, accommodating scale variations and occlusions. The application visualizes tracked objects with bounding boxes on the video canvas and updates object coordinates dynamically, providing users with insights into object movement. Advanced features, including dynamic parameter adjustment, enhance the algorithm's adaptability, enabling users to fine-tune tracking based on video characteristics and requirements. Overall, this project offers a practical implementation of object tracking within an accessible interface, catering to users across expertise levels in computer vision. The ninth project, "Object Tracking with Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size", focuses on developing a graphical user interface (GUI) application for object tracking in video files using computer vision techniques. Leveraging the GBBM algorithm, a prominent method for motion estimation, the project aims to enable efficient object tracking across video frames, enhancing user interaction and real-time monitoring capabilities. The GUI interface facilitates seamless video file loading, playback control, frame navigation, and real-time object tracking, empowering users to interact with video frames, adjust zoom levels, and monitor tracked object coordinates throughout the video sequence. Central to the project's functionality is the adaptive block size variant of the GBBM algorithm, dynamically adjusting block sizes based on gradient magnitudes to improve tracking accuracy and robustness across various scenarios. By simplifying object tracking processes through intuitive GUI interactions, the project caters to users with limited programming expertise, fostering learning opportunities in computer vision and video processing. Additionally, the project serves as a platform for collaboration and experimentation, promoting knowledge sharing and innovation within the computer vision community while showcasing the practical applications of computer vision algorithms in surveillance, video analysis, and human-computer interaction domains. The tenth project, "Object Tracking with SIFT Algorithm", introduces a GUI application developed with Python's tkinter library for tracking objects in videos using the Scale-Invariant Feature Transform (SIFT) algorithm. Upon launching, users access a window featuring video display, center coordinates of tracked objects, and control buttons. Supported video formats include mp4, avi, mkv, and wmv, with the "Open Video" button enabling file selection for display within the canvas widget. Playback control buttons like "Play/Pause," "Stop," "Previous Frame," and "Next Frame" facilitate seamless navigation and video playback adjustments. A zoom combobox enhances user experience by allowing flexible zoom scaling. The SIFT algorithm facilitates object tracking by detecting and matching keypoints between frames, estimating motion vectors used to update the bounding box coordinates of the tracked object in real-time. Users can manually define object bounding boxes by clicking and dragging on the video canvas, offering both automated and manual tracking options for enhanced user control. The eleventh project, "Object Tracking with ORB (Oriented FAST and Rotated BRIEF)", aims to develop a user-friendly GUI application for object tracking in videos using the ORB algorithm. Utilizing Python's Tkinter library, the project provides an interface where users can open video files of various formats and interact with playback and tracking functionalities. Users can control video playback, adjust zoom levels for detailed examination, and utilize the ORB algorithm for object detection and tracking. The application integrates ORB for computing keypoints and descriptors across video frames, facilitating the estimation of motion vectors for object tracking. Real-time visualization of tracking progress through overlaid bounding boxes enhances user understanding, while interactive features like selecting regions of interest and monitoring bounding box coordinates provide further control and feedback. Overall, the "Object Tracking with ORB" project offers a comprehensive solution for video analysis tasks, combining intuitive controls, real-time visualization, and efficient tracking capabilities with the ORB algorithm.

Download or read book Prediction Based Guidance for Real time Navigation of Mobile Robots in Dynamic Cluttered Environments written by Faraz Ahmed Kunwar and published by . This book was released on 2008 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time motion-planning in autonomous vehicle navigation applications has typically referred to the on-line trajectory-planning problem to reach a designated location in minimal time. In this context, past research achievements have been subjected to three main limitations: (i) only the problem of interception (position matching) has been considered, whereas the problem of rendezvous (velocity matching) has not been rigorously investigated; (ii) obstacles have been commonly treated as moving with constant velocity as opposed to being highly maneuverable and following a priori unknown trajectories; and, (iii) mostly, structured indoor terrains have been considered. This Thesis addresses the abovementioned drawbacks by proposing the use of a novel advanced guidance-based rendezvous methodology in allowing an autonomous vehicle to accurately and safely maneuver in the presence of dynamic obstacles on realistic terrains. The objective is time-optimal rendezvous with static or dynamic targets. The proposed on-line motion-planning method minimizes rendezvous time with the target, as well as energy consumption, by directly considering the dynamics of the obstacles and the target, while accurately determining a feasible way to travel through an uneven terrain. This objective is achieved by determining rendezvous maneuvers using the Advanced Predictive Guidance (APG) law. Namely, the navigation method is designed to effectively cope with maneuvering targets/obstacles by predicting their future velocities and accelerations. The terrain navigation algorithm, also developed within the framework of this Thesis, computes a safe path through a realistic terrain that also minimizes the rendezvous time. All developed algorithms are seamlessly integrated into one overall vehicle guidance algorithm. Extensive simulation and experimental analyses, some of which are reported herein, have clearly demonstrated the time efficiency of the proposed rendezvous method on realistic terrains as well as the robustness of the proposed algorithm to measurement noise.

Download or read book Vision Sensor Design and Evaluation for Autonomous Navigation written by Fengchun Dong and published by . This book was released on 2012 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this thesis is to provide a robot navigation system based on visual sensors' measurements. To achieve this goal, we inquire about the design of an optimal visual sensor which allows to formulate a linear optimization problem of egomotion estimation. A multiple-camera system is built, mimicking the functioning of insects' compound eyes and it captures the visual information in a more complete form called the plenoptic function that encodes the spatial and temporal light radiance of the scene. Contributions of this thesis are presented on three axes. First, we present the mathematical formulation of the plenoptic function and the relationship between the motion estimation and the ray-based plenoptic model. A multi-scale approach is also introduced to increase the accuracy of the system and meanwhile to reduce the computational costs. The second axis is dedicated to optimize the plenoptic sensor for real-time indoor navigation. We show that a plenoptic sensor with low resolution can perform better than a state-of-the-art monocular camera with high resolution. We also give a complete design scheme by establishing the link between velocity, resolution, field-of-view and motion estimation accuracy. Finally, due to the sparsity of the plenoptic data, we use a random sampling scheme which measures only the useful part of the visual information. By processing directly the sparse measurements, the computational time is reduced with minimal loss of accuracy. Since the required amount of data is largely reduced at the acquisition stage, computation resources can be reallocated for other tasks. The performance of the current built plenoptic sensor is evaluated in a systematical way through synthetic and experimental data.

Download or read book Real time Image and Video Processing written by Nasser Kehtarnavaz and published by Morgan & Claypool Publishers. This book was released on 2006 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-Time Image and Video Processing presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods.Throughout the book, carefully selected, representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, readers will have a strong understanding of the wide variety of techniques and tools involved in designing a real-time image or video processing system.

Download or read book IEEE Intelligent Vehicles Symposium written by and published by . This book was released on 2004 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Guidance Based Methods for Real Time Navigation of Mobile Robots written by Faraz Kunwar and published by LAP Lambert Academic Publishing. This book was released on 2011-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time motion-planning in autonomous vehicle navigation applications has typically referred to the on-line trajectory-planning problem to reach a designated location in minimal time. In this context, past research achievements have been subjected to three main limitations: (i) only the problem of interception (position matching) has been considered, whereas the problem of rendezvous (velocity matching) has not been rigorously investigated; (ii) obstacles have been commonly treated as moving with constant velocity as opposed to being highly maneuverable and following a priori unknown trajectories; and, (iii) mostly, structured indoor terrains have been considered. This book addresses the above drawbacks by proposing the use of guidance-based methods that can be used by an autonomous vehicle to accurately and safely maneuver in the presence of dynamic obstacles on realistic terrains. The objective is time-optimal rendezvous with static or dynamic targets. The proposed methods minimizes rendezvous time and energy consumption, by directly considering the dynamics of the obstacles and the target, while accurately determining a feasible way to travel through an uneven terrain.

Download or read book Handbook of Position Location written by Reza Zekavat and published by John Wiley & Sons. This book was released on 2019-01-28 with total page 1376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of position location technology — from fundamental theory to advanced practical applications Positioning systems and location technologies have become significant components of modern life, used in a multitude of areas such as law enforcement and security, road safety and navigation, personnel and object tracking, and many more. Position location systems have greatly reduced societal vulnerabilities and enhanced the quality of life for billions of people around the globe — yet limited resources are available to researchers and students in this important field. The Handbook of Position Location: Theory, Practice, and Advances fills this gap, providing a comprehensive overview of both fundamental and cutting-edge techniques and introducing practical methods of advanced localization and positioning. Now in its second edition, this handbook offers broad and in-depth coverage of essential topics including Time of Arrival (TOA) and Direction of Arrival (DOA) based positioning, Received Signal Strength (RSS) based positioning, network localization, and others. Topics such as GPS, autonomous vehicle applications, and visible light localization are examined, while major revisions to chapters such as body area network positioning and digital signal processing for GNSS receivers reflect current and emerging advances in the field. This new edition: Presents new and revised chapters on topics including localization error evaluation, Kalman filtering, positioning in inhomogeneous media, and Global Positioning (GPS) in harsh environments Offers MATLAB examples to demonstrate fundamental algorithms for positioning and provides online access to all MATLAB code Allows practicing engineers and graduate students to keep pace with contemporary research and new technologies Contains numerous application-based examples including the application of localization to drone navigation, capsule endoscopy localization, and satellite navigation and localization Reviews unique applications of position location systems, including GNSS and RFID-based localization systems The Handbook of Position Location: Theory, Practice, and Advances is valuable resource for practicing engineers and researchers seeking to keep pace with current developments in the field, graduate students in need of clear and accurate course material, and university instructors teaching the fundamentals of wireless localization.

Download or read book A Few Steps Towards 3D Active Vision written by Thierry Vieville and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: T. Viéville: A Few Steps Towards 3D Active Vision appears as Vol. 33 in the Springer Series in Information Sciences. A specific problem in the field of active vision is analyzed, namely how suitable is it to explicitly use 3D visual cues in a reactive visual task? The author has collected a set of studies on this subject and has used these experimental and theoretical developments to propose a synthetic view on the problem, completed by some specific experiments. With this book scientists and graduate students will have a complete set of methods, algorithms, and experiments to introduce 3D visual cues in active visual perception mechanisms, e.g. autocalibration of visual sensors on robotic heads and mobile robots. Analogies with biological visual systems provide an easy introduction to this subject.

Download or read book Computer Vision ECCV 2016 Workshops written by Gang Hua and published by Springer. This book was released on 2016-11-03 with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.

Download or read book Proceedings of 2020 Chinese Intelligent Systems Conference written by Yingmin Jia and published by Springer Nature. This book was released on 2020-09-29 with total page 841 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on new theoretical results and techniques in the field of intelligent systems and control. It provides in-depth studies on a number of major topics such as Multi-Agent Systems, Complex Networks, Intelligent Robots, Complex System Theory and Swarm Behavior, Event-Triggered Control and Data-Driven Control, Robust and Adaptive Control, Big Data and Brain Science, Process Control, Intelligent Sensor and Detection Technology, Deep learning and Learning Control Guidance, Navigation and Control of Flight Vehicles and so on. Given its scope, the book will benefit all researchers, engineers, and graduate students who want to learn about cutting-edge advances in intelligent systems, intelligent control, and artificial intelligence.