Balance control of a biped robot using camera image of reference object

This paper presents a new balance control scheme for a biped robot. Instead of using dynamic sensors to measure the pose of a biped robot, this paper uses only the visual information of a specific reference object in the workspace. The zero moment point (ZMP) of the biped robot can be calculated from the robot’s pose, which is measured from the reference object image acquired by a CCD camera on the robot’s head. For balance control of the biped robot a servo controller uses an error between the reference ZMP and the current ZMP, estimated by Kalman filter. The efficiency of the proposed algorithm has been proven by the experiments performed on both flat and uneven floors with unknown thin obstacles. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA(Institute for Information Technology Advancement) (IITA-2008-C1090-0803-0006). Sangbum Park received the B.S. and M.S. degrees from Electronic Engineering of Soongsil University, Seoul, Korea, in 2004 and 2006 respectively. He has been with School of Electronic Engineering, Soongsil University since 2006, where he is currently pursuing a Ph.D. His current research interests include biped walking robot, robotics vision. Youngjoon Han received the B.S., M.S. and Ph.D. degrees in Electronic Engineering from Soongsil University, Seoul, Korea, in 1996, 1998, and 2003, respectively. He is currently an Assistant Professor in the School of Electornic Engineering at Soongsil University. His research interests include robot vision system, and visual servo control. Hernsoo Hahn received the B.S. and M.S. degrees in Electronic Engineering at Soongsil University and Younsei University, Korea in 1982 and 1983 respectively. He received the Ph.D. degree in Computer Engineering from University of Southern California in 1991, and became an Assistant Professor at the School Electroncis Engneering in Soongsil University in 1992. Currently, he is a Professor. His research interests include application of vision sensors to mobile robots and measurement systems.     

Learning images using compositional pattern-producing neural networks for source camera identification and digital demographic diagnosis

In this work, we propose a neural network based framework to explore the statistical correlation intrinsically embedded due to interpolations in a relatively small neighborhood, in which the interpolation process is cognized from the interpolation results and the spatially invariant stylized computational rules in interpolation algorithms are simulated and learned by adjusting weights and bias values of neural networks. Experiments show that, our approach is competitive among the state of the art of source camera identification methods. It is also effective for digital forgery detection and other interesting experiments such as the digital demographic diagnosis and prediction. The framework can also be applied to other types of image interpolations such as super-resolution.     

Estimating the velocity and direction of moving targets using a single optical VHR satellite sensor image

The present contribution demonstrates the feasibility and explores the limits of a new method for estimating the velocity and direction of moving targets using a single, optical, very‐high‐resolution (VHR) satellite sensor dataset. The method is based on the fact that there is a time lag between the data collection of the panchromatic (P) and multi‐spectral (MS) sensors in the same VHR platform. Consequently, it is developed around three main steps: (1) accurate image‐to‐image registration between MS and P images with a sub‐pixel displacement error, (2) precise location of barycentre of targets by mathematical morphology‐based image transforms, and (3) estimation of the ground velocity and direction of the target using the MS‐P spatial displacement, the known time lag, and an image‐to‐ground transformation taking into account the interior and exterior orientation of the sensors and a terrain height reference. An evaluation of the reliability and limits of the proposed method based on the observation of the results regarding manually selected moving and non‐moving targets is included.     

Robot calibration using a 3D vision-based measurement system with a single camera

One of the problems that slows the development of off-line programming is the low static and dynamic positioning accuracy of robots. Robot calibration improves the positioning accuracy and can also be used as a diagnostic tool in robot production and maintenance. This work presents techniques for modeling and performing robot calibration processes with off-line programming using a 3D vision-based measurement system. The measurement system is portable, accurate and low cost, consisting of a single CCD camera mounted on the robot tool flange to measure the robot end-effector pose relative to a world coordinate system. Radial lens distortion is included in the photogrammetric model. Scale factors and image centers are obtained with innovative techniques, making use of a multiview approach. Results show that the achieved average accuracy using a common off-the-shelf CCD camera varies from 0.2 to 0.4 mm, at distances from 600 to 1000 mm from the target, respectively, with different camera orientations. Experimentation is performed on two industrial robots to test their position accuracy improvement using the calibration system proposed: an ABB IRB-2400 and a PUMA-500. The robots were calibrated at different regions and volumes within their workspace achieving accuracy from three to six times better when comparing errors before and after calibration, if measured locally. The proposed off-line robot calibration system is fast, accurate and easy to set up.     

An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques

This paper presents an empirical study investigating the effects of training dataquantity, measurement error, pixel coordinate noise, and the choice of camera model, oncamera calibration accuracy, based on three publicly available techniques, developed byTsai, Heikkilä and Zhang. Results are first provided for asimulated camera system and then verified through carefully controlled experiments using real-world measurements. Our aims are to provide suggestions to researchers who require an immediate solution for camera calibration and a warning of the practical difficulties one may encounter in a real environment. In addition, we offer some insight into the factors to keep in mind when selecting a calibration technique. Finally, we hope that this paper can serve as an introduction to the field for those newly embarking upon calibration-related research. Wei Sun received the B.Eng. degree in Electronic Engineering from Shanghai Jiao Tong University in 1995 and the M.Sc. degree in Computer Science from Fudan University in 1998. At present, she is a Ph.D. candidate working at Centre for Intelligent Machines, Department of Eletrical and Computer Engineering of McGill University. Her research interests include image and video processing, computervision, machine learning and computer graphics. Jeremy R. Cooperstock (Ph.D. Toronto, 1996) is a professor of Electrical and Computer Engineering, a member of the Centre for Intelligent Machines, and a founding member of the Centre for Interdisciplinary Research in Music, Media and Technology at McGill University. Cooperstock is a member of the ACM and chairs the AES Technical Committee on Network Audio Systems. Cooperstock's research interests focus on computer mediation to facilitate high-fidelity human communication and the underlying technologies that support this goal.     

基于指纹纹线形态特征的方向图修正算法研究

指纹图像存在着各种各样的噪声,直接取得的指纹方向图并不准确,需要修正后才能使用.传统修正方法如加权平均、直方图等块方向图算法具有较强的局部限制性,修正能力较弱.大量研究结果表明,在一个较大窗口中,指纹图像上绝大多数点的方向与该点垂向上各点的方向近似平行,因此提出了一种新的方向图修正算法.此算法的处理窗口是目前常用块方向算法的3至5倍,一定程度上摆脱了局部限制性,大大提高了修正能力,并在大量的实验中显示出优越性.     

图像时域方向与频域方向的关系研究

方向滤波器组能够对图像进行不同子带的划分,各个子带图像代表不同的方向,在时域上面表现出不同的方向,但是方向滤波器组的方向划分主要是基于图像频域的划分。在利用傅里叶变换求幅度谱以及相应的功率谱的基础上,分析了图像边缘纹理时域方向与频域方向之间的关系,通过一序列公式进行推导并且用实例展示了图像边缘纹理时域方向与图像边缘频域方向有相互正交的关系。     

The normalised image of the absolute conic and its application for zooming camera calibration

We present a novel technique for calibrating a zooming camera based on the invariance properties of the normalised image of the absolute conic (NIAC). We show that the camera parameters independent of position, orientation and zooming are determined uniquely by the NIAC, and we exploit these invariance properties to develop a stratified calibration method that decouples the calibration parameters. The method is organised in three steps: (i) computation of the NIAC, (ii) computation of the focal length for each image and (iii) computation of the orientation and the position of the camera. The method requires a minimum of three views of a single planar grid. Experiments with synthetic and real data suggest that the method is competitive with other state-of-the-art plane-based zooming calibration methods in the scenarios considered.     

The effect of viewing angle on wrist posture estimation from photographic images using novice raters

Observational assessment of wrist posture using photographic methods is theoretically affected by camera view angle. A study was conducted to investigate whether wrist flexion/extension and radial/ulnar deviation postures were estimated differently by raters depending on the viewing angle and compared to predictions using a quantitative 2D model of parallax. Novice raters (n = 26) estimated joint angles from images of wrist postures photographed from ten different viewing angles. Results indicated that ideal views, orthogonal to the plane of motion, produced more accurate estimates of posture compared to non-ideal views. The neutral (0°) posture was estimated the most accurately even at different viewing angles. Raters were more accurate than model predictions. Findings demonstrate a need for more systematic methods for collecting and analyzing photographic data for observational studies of posture. Renewed caution in interpreting existing studies of wrist posture where viewing angle was not controlled is advised.     

图像运动模糊方向的检测方法

为了降低运动模糊图像恢复问题的复杂度,提高计算效率,将图像恢复的二维问题转化为一维问题,可以将图像的模糊方向旋转到水平方向,这就需要识别图像的运动模糊方向.计算模糊图像任意角度的方向微分,根据微分图像灰度值的绝对值的分布特征,可以看到模糊方向和微分图像的绝对灰度值存在一定的关系,由此可以鉴别出模糊方向.在鉴别过程中,给出了精度更高的三次样条插值计算微分图像的详细方法.仿真实验表明,该方法稳定性好,具有较高的精度.     

Solving camera parameters from the perspective projection of a parameterized curve

Given the two-dimensional perspective projection of a known conic or polygon of unknown size and position in three-dimensional space, we show how to determine the camera look angles relative to the plane where the curve lies. Separate cases are discussed for conic and polygons. We then show that this technique can be used to solve for any planar or nonplanar curves, as long as they can be represented in parametric forms.     

Computing camera parameters using vanishing-line information from a rectangular parallelepiped

A new approach to camera calibration using vanishing line information for three-dimensional computer vision is proposed. Calibrated parameters include the orientation, the position, the focal length, and the image plane center of a camera. A rectangular parallelepiped is employed as the calibration target to generate three principal vanishing points and then three vanishing lines from the projected image of the parallelepiped. Only a monocular image is required for solving these camera parameters. It is shown that the image plane center is the orthocenter of a triangle formed by the three vanishing lines. From the slopes of the vanishing lines the camera orientation parameters can be determined. The focal length can be computed by the area of the triangle. The camera position parameters can then be calibrated by using related geometric projective relationships. The derived results show the geometric meanings of these camera parameters. The calibration formulas are analytic and simple to compute. Experimental results show the feasibility of the proposed approach for a practical application—autonomous land vehicle guidance.This work was supported by National Science Council, Republic of China under Grant NSC-77-0404-E-009-31.     

A solution to illumination direction estimation of a shaded image: Genetic algorithm

In oblique shape from shading (SfS), the illumination direction is essential for recovering the 3D surface of a shaded image. On the other hand, fast marching methods (FMM) are SfS algorithms that use the mechanism of wave propagation to reconstruct the surface. In this paper, the estimation of illumination direction is addressed and we model it as an optimization problem. The idea is to minimize the inconsistency of wave propagation of FMM during the reconstruction. As the consistency of wave propagation is a multi-modal function of illumination direction, genetic algorithm (GA) is utilized. The proposed algorithm is examined on four synthetic models and a real world object. The experimental results show that the proposed algorithm is superior to benchmark methods.     

基于FPGA的方向滤波指纹图像增强算法实现

设计了一种基于FPGA纯硬件方式实现方向滤波的指纹图像增强算法。采用寄存器传输级(RTL)硬件描述语言(Verilog HDL),利用时分复用和流水线处理等技术完成了方向滤波指纹图像增强算法在FPGA上的实现。系统通过了Modelsim的仿真验证,并在Terasic公司的DE2平台上完成了硬件测试。     

基于同态补偿翻拍图像的方向预测方法

为抵抗翻拍图像对人脸识别等认证系统的攻击,提出一种人脸图像梯度方向预测算法。通过自适应高斯同态滤波进行光照补偿增强真实活体图像与翻拍图像的对比度,用八方向Sobel算子与像元卷积方向预测,并使用支持向量机(SVM)分类器设计图像分类器判别两类图像。抽取国内外数据库(南京航空航天大学与耶鲁大学人脸库)活体人脸与翻拍人脸共522张进行实验,检测率达到99.51%;另用三星Galaxy Nexus手机拍摄261张真实人脸,同时进行翻拍,得到样本库522张人脸,实验检测率达到98.08%,特征提取用时167.04s。结果表明能有效地检测分类出真实人脸照片与翻拍假冒照片,并具有较高的特征提取效率。     

Detecting the wheel pattern of a vehicle using stereo images

This paper presents a method for detecting the wheels of a vehicle in stereo image pairs. The method consists of two steps: (i) geometrical transformation; and (ii) circle extraction. The geometrical transformation uses the disparity values obtained from a stereo image pair to calculate the parameters of the plane containing wheels of the vehicle. By using these parameters, we transform any elliptical wheels contained in the plane to circular ones which can be extracted by the circle extraction algorithm. The circle extraction algorithm consists of (1) template matching and (2) Hough transform. In order to save computation and improve the results in the Hough transform, we employ two constraints (a) the neighbor-region edge connectivity and (b) the gradient direction of each edge point, to eliminate non-circular edge points. Experimental results show that these two constraints do eliminate non-circular edge points and preserve any circle embedded in edges. From the final results, we can observe that our method can detect and locate the wheels of a vehicle successfully.     

Log-Gabor梯度方向下的角点检测

目的 角点是图像的基本特征,在图像处理与计算机视觉系统中,经常作为复杂计算的第1步,例如,目标识别、目标跟踪等。因此,角点检测器的检测性能显得尤为重要。基于此,提出了一个既利用到图像边缘轮廓信息又利用到图像灰度信息的基于Log-Gabor梯度方向一致性的角点检测算法,以提高角点检测器的检测性能。方法 根据角点的定义可知,角点在各个方向的灰度变化都很大,并且每个角点的梯度方向与相邻像素的梯度方向都具有很大差别。然而,相邻边缘像素点的梯度方向是一致的,都是垂直于边缘脊的方向。因此,本文利用角点与边缘像素的这一特性,构建了一个新的角点测度。该算法首先利用边缘检测器检测并提取图像的边缘映射;然后利用Log-Gabor虚部滤波器提取边缘像素周围的灰度变化信息,找到边缘像素点的梯度方向,利用梯度方向计算新的角点测度;最后对角点测度进行阈值化处理,得到最终的角点检测结果。结果 提出的算法分别与CPDA（chord-to-point distance accumulation）算法,He & Yung算法,以及Harris算法在标准轮廓图像和仿射变换下进行性能比较。平均重复率与定位误差分别作为评价角点检测器检测稳定性以及定位性能的指标。从平面曲线上的仿真实验结果可以看到,本文提出的角点检测算法能够较好地检测到真实角点,避免对角点的漏检与误检。旋转变换、非统一尺度变换以及高斯噪声下的平均重复率和定位误差结果的平均排名CPDA为2.00, Harris为3.33,He & Yung为2.83,本文算法为1.67。实验结果表明,本文算法的综合性能最优。本文算法优于其他3种角点检测算法,包括检测稳定性能和定位性能。结论 基于边缘的角点检测算法大多只依赖于图像的边缘轮廓信息,没有考虑到图像的灰度变化,而基于灰度的角点检测算法大多只考虑到图像的灰度信息。本文算法既考虑到图像的边缘形状也考虑到图像的灰度变化,并且利用log-Gabor虚部滤波器充分的提取图像的局部信息。在此基础上,利用图像边缘像素的梯度方向一致性构建了新的角点测度,以提高角点检测器的检测性能。实验结果表明,本文算法拥有良好的角点检测稳定性与定位性能。     

Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera

The application of adequate nitrogen (N) fertilizers to grass seed crops is important to achieve high seed yield. Application of N will inevitably result in over-fertilization on some fields and, concomitantly, an increased risk of adverse environmental impacts, such as ground- and/or surface-water contamination. This study was designed to estimate the N status of two grass seed crops: red fescue (Festuca rubra L.) and perennial ryegrass (Lolium perenne L.) using images captured with an unmanned aerial vehicle (UAV) mounted multispectral camera. Two types of UAV, a fixed-wing UAV and a multi-rotor UAV, operating at two different heights and mounted with the same multispectral camera, were used in different field experiments at the same location in Denmark in the period from 432 to 861 growing degree-days. Seven vegetation indices, calculated from multispectral images with four bands: red, green, red edge and near infrared (NIR), were evaluated for their relationship to dry matter (DM), N concentration, N uptake and N nutrition index (NNI). The results showed a better prediction of N concentration, N uptake and NNI, than DM using vegetation indices. Furthermore, among all vegetation indices, two red-edge-based indices, normalized difference red edge (NDRE) and red edge chlorophyll index (CI_RE), performed best in estimating N concentration (R² = 0.69–0.88), N uptake (R² = 0.41–0.84) and NNI (R² = 0.47–0.86). In addition, there was no effect from the choice of UAV, and thereby flight height, on the estimation of NNI. The choice of UAV type therefore seems not to influence the possibility of diagnosing N status in grass seed crops. We conclude that it is possible to estimate NNI based on multispectral images from drone-mounted cameras, and the method could guide farmers as to whether they should apply additional N to the field. We also conclude that further research should focus on estimating the quantity of N to apply and on further developing the method to include more grass species.     

利用图像边缘构造水印同步信息的新水印算法

提出一种利用图像边缘和Directionlet变换构造水印同步信息的新水印算法。将图像一条显著边缘的方向作为Directionlet的变换方向,与之成特定角度的方向作为队列方向,据此生成整数栅格的采样矩阵;水印在每个陪集的中低频子带内自适应嵌入;以图像显著边缘为一坐标轴建立坐标系,在该坐标系内检测水印,可以排除几何攻击对水印位置的影响。实验结果表明,较之基于Wavelet和Contourlet的水印算法,本文方法对抗几何攻击的优势非常突出,尤其对旋转攻击,效果更明显。     

频谱预处理模糊运动方向鉴别的改进算法

为了提高匀速直线运动模糊图像的运动方向检测精度,提出一种基于频谱预处理的模糊运动方向鉴别改进算法.首先,通过对模糊图像的频谱图执行两次傅里叶对数变换,实现频谱图的亮条细化处理;然后,以亮条细化后的频谱图为研究对象,采用滑动邻域操作实施频谱图的二值化处理,剔除图像中的噪声和十字亮线,得到频谱图的预处理图像;最后,对预处理图像执行Radon变换,检测图像中的直线成分,并将检测结果映射为模糊图像的运动方向.实验数据显示,在不同模糊尺度和运动方向环境下,本文算法始终能够将运动方向的检测误差控制在0.5°的有效范围之内;实验结果表明,本文算法的运动方向检测精度优于其他同类的算法.