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| 1 | Automated melanoma recognition显示文摘 | Harald Ganster Axel Pinz | 2001 | Journal of IEEE Transactions on Medical Imaging2001，20，3： | 1 |
| 2 | A comparison of three uncertainty calculi for building sonar-based occupancy grids显示文摘 | Miguel Ribo Axel Pinz | 2001 | Robotics and Autonomous Systems2001，35，34： | 1 |
| 3 | Multi-spectral Classification of Landsat-images Using Neural Networks显示文摘 | Horst Bischof Werner Schneider Axel Pinz | 1992 | IEEE Transactions on Geoscience and Remote Sensing1992，30，3： | 1 |
| 4 | A comparison of three uncertainty calculi for building sonar-basedoccupancy grids显示文摘 | Miguel Ribo Axel Pinz | | 0，，35： | 1 |
| 5 | 实时运动结构重建在自主导航系统中的应用显示文摘实时运动结构重建是自主车辆、机器人导航、空间探测器自主降落、智能监控等领域中的重要研究课题。目前实时运动结构重建主要存在着特征匹配困难、鲁棒性差、系统无法自动获取初始参数和需要大量人工干预等诸多问题。利用高速CMOS摄像机与惯性传感数据融合提高了运动结构重建算法的精度及其鲁棒性。该算法在扩展卡尔曼滤波框架下是通过融合惯性与视觉传感器的数据来进行运动估计的。对场景中的每一个待估计结构的特征点建立对应的卡尔曼滤波器,以估计其空间三维结构信息。运动估计模块与结构估计模块交替运行,减小了系统运算的复杂度,提高了实时性能。通过对真实场景图像序列的实验验证结果表明,惯性传感器的额外信息能够有效地提高运动结构估计的精度,能够增强算法的鲁棒性。 | 陈靖 王涌天 Axel Pinz | 2006 | 光学技术2006，32，z1： | 1 |
| 6 | Real-Time Optical Edge and Comer Tracking at Subpixel Accuracy 显示文摘 | Stefan Brantner Thomas Auer Axel Pinz | 1999 | Real-Time Imaging(S1077-2014)1999，7，： | 1 |
| 7 | An automatic assessment scheme显示文摘 | Klaus Wiltschi Axel Pinz Tony Lindeberg | | 0，，03： | 1 |
| 8 | Information fusion in image understanding显示文摘 | Pinz Axel Renate Bartl | 1992 | Proc of IEEE1992，，： | 1 |
| 9 | Information fusion in image under-standing显示文摘 | Pinz Axel Renate Bartl | | 0，，： | 1 |
| 10 | Robust pose estimation from a planar target 显示文摘 | Gerald Schweighofer Axel Pinz | 2006 | IEEE Transaction on PAMI2006，28，12： | 1 |
| 11 | Automatic Restoration Algorithms for 35mm Film显示文摘 | Peter Schallauer Axel Pinz Werner Haasl | 1999 | Videre:Journal of Computer Vision Research the MIT Press1999，1，3： | 1 |
| 12 | Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection显示文摘 | Andreas Opelt Axel Pinz Andrew Zisserman | 2008 | International Journal of Computer Vision2008，，1： | 1 |
| 13 | Real-Time Structure and Motion by Fusion of Inertial and Vision Data for Mobile AR System显示文摘The performance of adding additional inertial data to improve the accuracy and robustness of visual tracking is investigated. For this real-time structure and motion algorithm, fusion is based on Kalman filter framework while using an extended Kalman filter to fuse the inertial and vision data, and a bank of Kalman filters to estimate the sparse 3D structure of the real scene. A simple, known target is used for the initial pose estimation. Motion and structure estimation filters can work alternately to recover the sensor motion, scene structure and other parameters. Real image sequences are utilized to test the capability of this algorithm. Experimental results show that the proper use of an additional inertial information can not only effectively improve the accuracy of the pose and structure estimation, but also handle occlusion problem. | 陈靖 王涌天 刘越 AXEL Pinz | 2006 | Journal of Beijing Institute of Technology2006，15，4： | 0 |
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