A Fuzzy-Mathematical Model to Motion Detection with Monocular Vision ab 44.9 € als Taschenbuch: Vision Based Mobile Robots. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
Who killed Nanna Birk Larsen? Through the dark wood where the dead trees give no shelter Nanna Birk Larsen runs…. There is a bright monocular eye that follows, like a hunter after a wounded deer. It moves in a slow approaching zigzag, marching through the Pineseskoven wasteland, through the Pentecost Forest. The chill water, the fear, his presence not so far away... There is one torchlight on her now, the single blazing eye. And it is here.... Sarah Lund is looking forward to her last day as a detective with the Copenhagen Police department before moving to Sweden. But everything changes when nineteen-year-old student, Nanna Birk Larsen, is found raped and brutally murdered in the woods outside the city. Lund’s plans to relocate are put on hold as she leads the investigation along with fellow detective Jan Meyer. While Nanna’s family struggles to cope with their loss, local politician Troels Hartmann is in the middle of an election campaign to become the new mayor of Copenhagen. When links between City Hall and the murder suddenly come to light, the case takes an entirely different turn. Over the course of twenty days, suspect upon suspect emerges as violence and political intrigue cast their shadows over the hunt for the killer. 1. Language: English. Narrator: Christian Rodska. Audio sample: http://samples.audible.de/bk/macm/000614/bk_macm_000614_sample.mp3. Digital audiobook in aax.
A Fuzzy-Mathematical Model to Motion Detection with Monocular Vision ab 44.9 EURO Vision Based Mobile Robots
Depth estimation from a single monocular image is a difficult problem.The task is even more challenging as depth cues such as motion, stereo correspondences are not present in single image. Hence machine learning based approach for extracting depth information from single image is proposed. Firstly depth is generated by manifold learning in which LLE algorithm is used, it is a non linear method of dimensionality reduction in which neighbors of input set in higher dimensional space are preserved while being transformed into lower dimensional space. The depth maps obtained are further refined by fixed point algorithm, it is supervised learning in which those features are extracted from image which have strong correspondences with labels.
We present a visual servoing system for an amphibious legged robot. This is a monocular-vision based servoing mechanism that enables the robot to track and follow a target both underwater and on the ground. We use three different tracking algorithms to track and localize the target in the image, with color being the tracked feature. Tracking is performed based on the color of the target object, target color distribution and color distribution with a probabilistic kernel. Output from the tracker is channeled to a proportional-integral-derivative controller, which generates steering commands for the robot controller. The robot controller in turn takes the steering commands and generates motor commands for the six legs of the robot. A large class of significant applications can be addressed by allowing such a robot to follow a diver or some other moving target. The system has been evaluated in open-water environments and under natural lighting conditions, and has successfully performed tracking and following of a wide variety of target objects.
This book presents a hardware architecture for the Simultaneous Localization And Mapping (SLAM) problem applied to embedded robots. The architecture is composed by highly specialized modules for robot localization and feature-based map building from images obtained directly from CMOS cameras in real time. The system is completely embedded on a Field-Programmable Gate Array (FPGA) device, where several hardware-orientated optimizations are exploited. The main modules of the architecture are the Extended Kalman Filter (EKF) and the feature detection system based on the SIFT (Scale Invariant Feature Transform) algorithm. Additionally, this book also presents basic concepts about mapping and state-of-the-art algorithms for SLAM with monocular and stereo vision.
Recovery of dense geometry and camera motion from a set of monocular images is a well-known problem that can be solved quite reliably in well-conditioned environments. Typical algorithms dealing with this problem assume static lighting and presence of sufficient scene texture. There are, however, many situations where these prerequisites are not met, and common algorithms fail. One example is medical video-endoscopy, where surfaces do not exhibit much texture, and lighting conditions change due to the moving light source that is mounted on the camera. We suggest to address the problem by applying a purely intensity-based approach that also takes into account changes in lighting conditions. In this thesis, we investigate the applicability of sliding window intensity-based bundle-adjustment methods to this problem.
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.