Estimating body shape of dressed humans

The paper presents a method to estimate the detailed 3D body shape of a person even if heavy or loose clothing is worn. The approach is based on a space of human shapes, learned from a large database of registered body scans. Together with this database we use as input a 3D scan or model of the person wearing clothes and apply a fitting method, based on ICP (iterated closest point) registration and Laplacian mesh deformation. The statistical model of human body shapes enforces that the model stays within the space of human shapes. The method therefore allows us to compute the most likely shape and pose of the subject, even if it is heavily occluded or body parts are not visible. Several experiments demonstrate the applicability and accuracy of our approach to recover occluded or missing body parts from 3D laser scans.     

基于小波多分辨率分析的指横纹定位新算法

针对指横纹感兴趣区域(ROI)难以准确定位的问题,在明确提出指横纹ROI定义的前提下,提出利用小波多分辨率分析进行指横纹ROI自动检测定位的新算法.该方法利用纹理相似性原理,在高频子图采用基于特征向量和区域生长法产生指横纹的候选子区域集合,然后利用低频子图Radon投影得到的指横纹的区域特征对候选子区域进行验证,最后结合直线拟合手指轮廓得到指横纹在原图的有效位置,最终实现指横纹ROI的精准定位.实验证明该算法不仅能有效克服噪声以及手形姿态变化的影响,并且对多种情况有很强的鲁棒性.     

一种风力发电机组机舱风速修正方法

风力发电机组叶轮后风速因受到叶轮遮挡,以及湍流、尾流等因素的影响,机舱风速仪测得的风速不足以准确反映风力发电机组的功率特性.为此,提出了一种风力发电机组机舱风速修正方法.该方法首先基于风力发电机组的控制策略对机舱风速进行分段,然后通过机舱风速与来流风速的相关性分析,确定拟合函数的类型,最后利用最小二乘法分段拟合得到试验机型机舱风速与来流风速的修正函数.通过实例验证表明:修正之后的风速误差更小,具有一定的实用性.     

基于STM32的手持式测氧仪设计

针对国内传统手持式测氧仪精度低、功耗高的问题,提出了采用嵌入式微控制器STM32F103RBT6作为控制芯片的手持式测氧仪.该仪器通过控制元器件的开断来降低待机功耗,根据所测浓度人工切换系统放大倍数,通过键盘设置时间和报警值等相关参数.算法上采用中位值平均滤波算法进行软件滤波,结合传统两点标定法和多点标定法的优缺点,采用多点标定法进行氧浓度标定,采用分段斜率拟合算法计算氧浓度值.经实验测试证明设计的手持测氧仪具有高精度和高稳定性.     

基于巡逻小车的人脸遮挡异常事件实时检测

近年来,随着城市化进程的加快,银行、政府、学校等场所的安全越来越成为人们关心的问题,智能监控已成为目前的一个研究热点问题. 本文主要研究室内巡逻小车监控下,面部异常遮挡问题. 本文首先对监控视频进行前景提取;接着,基于提取的前景,进行肩部定位、头部区域椭圆拟合;然后通过肤色检测判断人脸区域;最后,通过Haar检测器检测人脸区域的眼睛和嘴巴,以此来判断是否存在异常遮挡行为. 实验结果表明,本文提出的算法能够实时、有效的检测出面部异常遮挡问题.     

基于Hough变换与最小二乘拟合的香烟小包拉线错牙检测方法

针对小盒香烟包装生产过程中经常出现外包装透明薄膜上没有拉线或拉线错牙等现象,提出一种基于图像处理技术的检测方法。首先,对烟包拉线图像进行边缘检测,获得拉线的轮廓;其次,利用Hough变换检测小盒拉线的上下边缘;当Hough变换无法完全找到拉线的上下边界时,采用最小二乘算法对左右两侧拉线弧进行圆拟合,最后根据计算出的拉线宽度或两圆心的垂直距离判断拉线是否错牙并计算出拉线错牙偏移量。实验结果表明所提方法能有效地检测出烟包拉线的错牙程度,且具有较好的鲁棒性和有效性。     

基于提升小波和自适应多项式拟合的传感器网络多模数据压缩算法

为提高无线传感器网络的感知精度,提出了一种基于提升小波变换和自适应多项式拟合的多模数据压缩算法（adaptive multiple-modalities data compression algorithm based on lifting wavelet and adaptive polynomial fitting,简称AMLP）。在给定相关度阈值的前提下,AMLP算法先对数据进行灰色关联聚类,再对类中的相关数据进行自适应的多项式拟合,然后把未拟合的特征数据抽象成一个矩阵,利用提升小波变换去除数据的时间和空间相关性。最后,通过游程编码对数据作进一步压缩。仿真结果表明,AMLP算法能够有效去除不同数据间的冗余信息以及同种数据间的时间和空间冗余信息,提高压缩比,降低网络能耗。与基于小波的自适应多模数据压缩算法（adaptive multiple-modalities data compression algorithm based on wavelet,简称AMMC）相比,AMLP算法的数据恢复精度大大优于AMMC算法,压缩比和能耗相近。因此,AMLP算法更适用于要求高精度数据的传感器网络应用,如地质灾害监测、医疗和军事领域。     

An extended iterative format for the progressive-iteration approximation

Progressive-iteration approximation (PIA) is a new data fitting technique developed recently for blending curves and surfaces. Taking the given data points as the initial control points, PIA constructs a series of fitting curves (surfaces) by adjusting the control points iteratively, while the limit curve (surface) interpolates the data points. More importantly, progressive-iteration approximation has the local property, that is, the limit curve (surface) can interpolate a subset of data points by just adjusting a part of corresponding control points, and remaining others unchanged. However, the current PIA format requires that the number of the control points equals that of the data points, thus making the PIA technique inappropriate to fitting large scale data points. To overcome this drawback, in this paper, we develop an extended PIA (EPIA) format, which allows that the number of the control points is less than that of the given data points. Moreover, since the main computations of EPIA are independent, they can be performed in parallel efficiently, with storage requirement O(n), where n is the number of the control points. Therefore, due to its local property and parallel computing capability, the EPIA technique has great potential in large scale data fitting. Specifically, by the EPIA format, we develop an incremental data fitting algorithm in this paper. In addition, some examples are demonstrated in this paper, all implemented by the parallel computing toolbox of Matlab, and run on a PC with a four-core CPU.     

Hyper least squares fitting of circles and ellipses

This work extends the circle fitting method of Rangarajan and Kanatani (2009) to accommodate ellipse fitting. Our method, which we call HyperLS, relies on algebraic distance minimization with a carefully chosen scale normalization. The normalization is derived using a rigorous error analysis of least squares (LS) estimators so that statistical bias is eliminated up to second order noise terms. Numerical evidence suggests that the proposed HyperLS estimator is far superior to the standard LS and is slightly better than the Taubin estimator. Although suboptimal in comparison to maximum likelihood (ML), our HyperLS does not require iterations. Hence, it does not suffer from convergence issues due to poor initialization, which is inherent in ML estimators. In this sense, the proposed HyperLS is a perfect candidate for initializing the ML iterations.