Inertial measurement unit-camera calibration based on incomplete inertial sensor information

This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit(IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMUcamera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First,the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt(LM)algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.

Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics

微笑作为一种典型的生物多样性特征信号,在社会交往中有较大影响力,它揭示了人的情感感受和内心状态。自发性的微笑与假笑由不同大脑系统发出,在形态学和动力学上均存在差异。区分这两种类型的微笑仍具有挑战性,因为其中细微差别很难被肉眼观察到,仍有待被识别捕捉。已有相关研究大多是提取自发性微笑的几何特征,而外观特征并没有被充分利用,导致纹理信息的丢失。本文提出一种基于特定区域纹理描述来表示不同面部区域的局部模式变化,从而弥补几何特征研究的局限性。每个面部区域的时间相位是通过计算相应的面部区域强度来划分,而非仅考虑嘴巴区域强度。同时利用支持向量机的中层融合策略,将两种特征类型结合起来。实验结果表明,本文提出的外观表示法及其与基于几何形状的人脸动力学的结合技术,在BBC、SPOS、MMI和UvA‐NEMO四个基准数据库中得到很好的应用。