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| 1 | 基于人脸侧影线角点检测的鼻尖点定位方法显示文摘为实现人脸表情及姿态变化下,鼻尖点的快速准确定位,提出一种基于人脸侧影线角点检测的鼻尖点定位方法。首先利用柱状人头模型,进行人脸姿态粗矫正;然后通过旋转投影法提取人脸的侧影轮廓线,并基于B样条尺度空间检测侧影线角点,根据角点位置定位鼻尖点候选区域;最后根据鼻尖点的形状特征及凸出特性准确定位鼻尖点位置。在CASIA 3D和BOSPHORUS三维人脸数据库的实验结果表明,该方法对表情和姿态鲁棒性较好,且定位精度优于基于先验信息和基于统计模板的方法。 | 潘腊青 徐海黎 韦勇 沈标 | 2018 | 计算机工程与应用2018，54，13： | 4 |
| 2 | Tracking with nonlinear measurement model by coordinate rotation transformation显示文摘A new filtering method is proposed to accurately estimate target state via decreasing the nonlinearity between radar polar measurements(or spherical measurements in three-dimensional(3D) radar) and target position in Cartesian coordinate. The degree of linearity is quantified here by utilizing correlation coefficient and Taylor series expansion. With the proposed method, the original measurements are converted from polar or spherical coordinate to a carefully chosen Cartesian coordinate system that is obtained by coordinate rotation transformation to maximize the linearity degree of the conversion function from polar/spherical to Cartesian coordinate. Then the target state is filtered along each axis of the chosen Cartesian coordinate. This method is compared with extended Kalman filter(EKF), Converted Measurement Kalman filter(CMKF), unscented Kalman filter(UKF) as well as Decoupled Converted Measurement Kalman filter(DECMKF). This new method provides highly accurate position and velocity with consistent estimation. | ZENG Tao LI Chun Xia LIU Quan Hua CHEN Xin Liang | 2014 | Science China(Technological Sciences)2014，57，12： | 4 |
| 3 | A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system显示文摘Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches(86% reducing for a 0.5 min visual tracking). Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme(edge detection), and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration. | Zhongwei TAN Chuanchuan YANG Yuliang LI Yan YAN Changhong HE Xinyue WANG Ziyu WANG | 2016 | Science China(Information Sciences)2016，59，4： | 0 |
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