Deformable Surface 3D Reconstruction from Monocular Images ab 63.99 € als Taschenbuch: . Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
Monocular Model-Based 3D Tracking of Rigid Objects ab 40.49 € als Taschenbuch: A Survey. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
Variation Based Dense 3D Reconstruction ab 58.99 € als pdf eBook: Application on Monocular Mini-Laparoscopic Sequences. Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet,
Simultaneous Localization and Mapping, comprising estimation of robot ego-motion and building a map of the surrounding environment, is one of the most fundamental tasks of mobile robotics. Many SLAM systems proposed in the past make use of the Global Positioning System (GPS), which renders them both expensive and overly dependent on the presence of the GPS signal. We propose an alternative, low-cost approach for portable SLAM which is based on monocular vision, a promising technique due to its flexibility, ease of use, and ease of calibration. In order to perform this task we use an Extended Kalman Filter, one of the most efficient and robust methods used in SLAM systems. We show how it is possible to improve the estimated position and reduce its uncertainty by fusing data from different sensors, in particular using a simple 3-axis accelerometer. We prove, through careful and intelligent selection and tuning of image analysis algorithms, that real-time, low-cost SLAM is feasible. This work is useful to professionals developing SLAM systems and to people in the larger field of computer vision, especially those interested in feature detection and tracking.
Many applications of video technology require multi-target tracking, for example for indoor or outdoor surveillance, intelligent vehicles, robotics, or experiments in biology. This book shows how specifica of targets influence the design of tracking methods. Four totally different types of targets are tracked, namely fruit flies (drosophila melanogaster), vehicles, pedestrians, and lane marks. The book presents methods for tracking of such targets under various recording settings (i.e. static, or moving cameras, monocular, binocular, or trinocular). In order to further improve the tracking performance, more specific methods are proposed and discussed for the different targets. This book starts with a comprehensive review of state-of-the-art multi-traget tracking methods. It is suitable for students or researchers who would like to have advise about tracking methods possibly applicable to their application, or who are contributing in the computer vision field to the design of tracking methods.
Networked 3D virtual environments allow multiple users to interact with each other over the Internet. Users can share some sense of telepresence by remotely animating an avatar that represents them. However, avatar control may be tedious and still render user gestures poorly. This work aims at animating a user s avatar from real time 3D motion capture by monocular computer vision, thus allowing virtual telepresence to anyone using a personal computer with a webcam. The approach followed consists of registering a 3D articulated upper-body model to a video sequence. The first contribution of this work is a method of allocating computing iterations under real-time constrain that achieves optimal robustness and accuracy. The major issue for robust 3D tracking from monocular images is the 3D/2D ambiguities that result from the lack of depth information. As a second contribution, this work enhances particle filtering for 3D/2D registration under limited computation constrains with a number of heuristics, the contribution of which is demonstrated experimentally. A parameterization of the arm pose based on their end-effector is proposed to better model uncertainty in the depth direction.
Visual scene understanding is one of the ultimate goals in computer vision and has been in the field's focus since its early beginning. Despite continuous effort over several years, applications such as autonomous driving and robotics are still subject to active research. In recent years, improved probabilistic methods became a popular tool for state-of-the-art computer vision algorithms. Additionally, high resolution digital imaging devices and increased computational power became available. By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection, tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments. This book improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety. It develops and improves approaches for object class detection and semantic scene labeling and integrates those into models for global scene reasoning which exploit context at different levels.
Vision based inference for mobile robots in order to avoid obstacles, is one of the most attracted areas for both domains of Computer Vision and Robotics. Computer Vision, more specifically the vision for intelligent machines enables mobile robots to perceive the external world with 'wisdom'. Therefore Vision based obstacle avoidance has become one of the major research areas of Robotics. Estimating the motion path and predicting the motion behavior of a dynamic object with only single camera (monocular vision) is a real challenge. We realized this can be done by analyzing a sequence of image frames extracted from a live video stream. But, these analytic techniques must be extremely fast in real time processing, since the decision drawn within reasonably short response time is the only 'god' to safeguard the robot & ensure the safety of others in environment! Therefore, we postulate a fuzzy-mathematical model: an Artificial Intelligence approach to achieve the ultimate objective, which has a significant impact in terms of simplicity (reduced complexity) together with efficiency (minimized computational overhead: resource consumption), rather than conventional mathematical modeling.
Scene interpretation is a fundamental task in both computer vision and robotic systems. We deal with two important aspects of scene interpretation, they are scene reconstruction and scene recognition. Scene reconstruction is determining 3D positions of world points and retrieving camera poses from images. It has several applications such as virtual building editing, video augmentation, and planning and navigation in robotics. Among several approaches to modeling the scene, we deal with piecewise planar modeling due to several advantages. We propose a convex optimization based, approach for piecewise planar reconstruction. Scene recognition in robotics, specifically terrain scene recognition is one of the fundamental tasks of autonomous navigation. Navigable terrains are examples of planar scenes. It has applications in various domains such as advanced driver assistance systems, remote sensing, etc. Various sensing modalities such as ladars, lasers, accelerometers, stereo cameras, or combination of them are used in literature. We propose an algorithm which is purely based on a single monocular camera.