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References 1. Chutorian, M. E., M. Trivedi. Head Pose Estimation in Computer Vision: A Survey. – IEEE Trans Pattern Analysis and Machine Intelligence, Vol. 31 , 2008, No 4, pp. 607-326. 2. Huang, J., X. Shao, H. Wechsler. Face Pose Discrimination Using Support Vector Machines (SVM). – In: International. Conf. Pattern Recognition, Vol. 1 , 1998, No 4, pp. 154-156. 3. Li, Y., S. Gong, J. Sherrah, H. Liddell. Support Vector Machine Based Multi-View Face Detection and Recognition. – Image and Vision Computing, Vol. 22 , 2004, No 5, pp. 413-427. 4. Srinivasan, S


In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.

REMOTE SATELLITE POSITION & POSE ESTIMATION USING MONOCULAR VISION Daniel Francois Malan1^ Prof. W.H. Steynf Prof. B.M. Herbst* Key Words: 3 D Tracking, Compute r Vision, Kaiman Filter, Structure f rom Motion, Satellite Format ionkeeping Abstract* This article investigates methods for estimating relative 3D position and pose from monocular image sequences. The intended future application is of one satellite observing another, when flying in close formation. The ideas explored in this article build on methods developed in camera calibration and Kaiman


This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions.

MARKER REMOVAL FOR C-ARM POSE ESTIMATION BASED BRONCHOSCOPE NAVIGATION USING IMAGE INPAINTING Teena Steger1, Sebastian Steger1 and Stefan Wesarg1 1Medical Computing, Fraunhofer IGD, Darmstadt, Germany Abstract: Using marker-based C-arm pose estimation dur- ing bronchoscopy in combination with a preoperative CT segmentation of the bronchial tree, the 3D spatial position of the bronchoscope tip inside the airways can be deter- mined. Naturally, the markers used for pose estimation ap- pear on the fluoroscopy images, which can be

-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep

References 1. OpenCV:, (Accessed: Apr. 24, 2013). 2. Point Cloud Library (PCL),, (Accessed: Apr. 24, 2013). 3. O. SKOTHEIM, J. T. THIELEMANN, A. BERGE, AND A. SOMMERFELT. 2010. Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm. Instrumentation. 4. R. BLOSS. 2006. Smart robot that picks parts from bins. Assembly Automation, Vol. 26, No. 4, pp. 279-282. 5. K. BOEHNKE. 2007. Object localization in range data for robotic bin picking. In: Automation Science and Engineering

integrated, the line of sight problem is minimized and the QR code markers are intuitively in the field of view of the camera. A special feature is that QR codes of different sizes can be flexibly used for various instruments since the QR code size, that is required to determine the position, and transformation from marker to tip can be stored in the QR code. A further benefit of using a QR code as a marker for pose estimation, is that the QR code structure allows to detect many feature points that can be used for precise detection. Figure 1 shows a system setup with a

automotive applications. Schlagwörter Prototyp-Passung, Kreisbogensplines, Objekterkennung, Lageerkennung Keywords Prototype fitting, arc splines, object recognition, pose estimation 1 Einleitung Der Iterative Closest Point (ICP) Algorithmus hat sich zum Quasi-Standard für die Lage-Passung geometri- scher Modelle entwickelt. Bei dieser Fragestellung, welche auch als Prototyp-Passung bezeichnet wird, werden üblicherweise eine Vielzahl von Abständen zwischen extrahierten Konturpunkten und einem Musterobjekt be- rechnet (vgl. [1; 2]). Bei den meisten ICP-Erweiterungen und

DE GRUYTER OLDENBOURG tm – Technisches Messen 2016; 83(9): 521–530 Beiträge Sebastian Vater*, Johannes Pallauf, Marian Hoffmann, Thorsten Stein und Fernando Puente León Erzeugung präziser Referenzdaten für die 3D-Kopfposenschätzung Creating precise reference data for 3D head pose estimation DOI 10.1515/teme-2015-0104 Eingang 12. November 2015; überarbeitet 6. Juli 2016; angenommen 10. Juli 2016 Zusammenfassung: In dieser Arbeit wird eine Metho- de zur Erzeugung von Referenzdaten für die 3D-Kopfpo- senschätzung mit Hilfe eines Vicon MX Motion-Capture- Systems