正交边缘投影快速算法在胶片弊病识别中的应用

为实现对胶片弊病的实时识别,提出了一种正交边缘投影快速检测算法.基于对红外胶片图像特点的分析,通过综合运用邻域平均、边缘检测和投影法等图像处理技术,实现了对胶片弊病的快速、准确识别,并据此算法编制了相应的VC++程序.该算法保证实时的关键是将二维图像投影为两个一维信号进行处理,该算法还可根据实际需要实现功能扩展,用于确定弊病的大小及严重程度.通过在乐凯胶片集团公司的在线实验,证明其能够准确检测出各种典型的胶片弊病,处理一帧大小为659×494像素的图像只需20ms.     

改进型图像中的直线快速检测

为提高数字图像中的直线的检测速度,从时频域变换和空间域变换两个角度出发,提出了一种改进型数字图像中直线快速检测方法。首先,采用小波提升提取图像中的低频轮廓信息,抑制高频噪声。然后,对像素降低后的图像进行二值化处理;基于"两点确定一条直线"以及Hough变换过程中"图像空间中一条直线上的多个点对应参数空间中一个点"的原理,按照从局部到整体的检测顺序,将二值化后图像空间中的非零点映射到参数空间中具有较大存在概率的累加单元,而不是所有可能的累加单元。最后,对累加单元进行统计,以确定图像中直线的参数。利用该方法对一幅像素为128×128的数字图像进行直线检测,耗时为213ms。该方法有效地提高了数字图像中直线检测的实时性。     

复杂海空背景下弱小目标的快速自动检测

针对海面运动载体的可见光序列图像,结合复杂海空背景图像的特点,提出了一种不以检测海天线为前提的弱小目标检测方法.首先,修复图像中被高亮度噪声损毁的部分,如曝光区域或反光区域;接着,量化子图像的区域复杂度以及单元区域上下邻域的灰度差异,据此来判断海天线区域是否存在,若存在则预测海天线区域的位置,否则放弃后续处理;然后,采取Mean-shift分割算法中先滤波后聚类的策略,使用周围纹理抑制滤波来平滑海天线区域,并以像素点和点集为单位对平滑图像进行聚类;最后,将最大面积区域与其他区域分离来二值化图像,完成目标提取.试验证明,该方法能够很好地定位目标信息,单帧处理平均耗时为35 ms,具有准确性和实时性.     

应用颜色聚类图像块的多舰船显著性检测

由于多舰船目标显著性检测过程容易将边界像素作为背景处理,本文提出了应用颜色聚类图像块的多舰船显著性检测方法。该方法首先检测邻域像素是否具有颜色相似性,并将临近的具有相似颜色的像素聚集在一起作为一个图像块。接着,对获得的图像块进行扩展,使图像块包含很多其他图像块的像素以提高图像块内像素间的对比强度;对边缘像素进行背景索引标记,计算图像块中像素的显著性强度,采用阈值分割方法获得目标显著性区域。最后,基于颜色聚类的图像块存在部分重叠的特点,利用权值对存在叠加的显著性图像进行融合,从而获得多舰船目标整幅图像的显著性检测结果。对获得的多舰船目标图像进行了实验测试,并对本文算法结果和当前比较先进的其它显著性检测算法进行了效果对比。结果显示:提出的利用颜色聚类图像块的舰船显著性检测方法的查全率达到78%以上,准确率达到92%以上,综合评价指标Fβ≥0.7;无论考虑单个指标还是整体指标,本文算法均优于其他对比算法。     

基于梯度聚类的有序点云边缘优化提取方法

应用传统的2D边缘检测器检测低分辨率深度图中物体边缘时,边缘检测精度较差,召回率低;而当前基于3D点云的边缘提取方法也存在实时性差、抗干扰能力弱等缺点。为此,提出一种基于梯度聚类的边缘优化提取方法,实现从有序点云中快速、稳定地检测物体的边缘。首先,通过邻域点距离分析滤除飞行像素噪声,消除边缘误检;其次,提出一种基于梯度聚类的边缘点/非边缘点分离方法,快速获取物体的粗边缘;最后,结合快速平行细化算法与掩膜滤波,优化粗边缘,获得物体精确边缘。在公共数据集和TOF相机实测数据上进行实验验证。结果表明,提出方法的实时性与检测精度均优于现有方法,在实测数据中的边缘检测精度达89%,FPS达28 fps。     

高速机械系统运动特性的实时视觉检测技术研究

介绍一种利用高速视觉传感器,对机械系统高速运动进行实时运动特性提取的技术,并搭建高速视觉检测系统,可以对复杂的高速机械设备进行长时间、无接触的运动频率检测。本技术将高速相机视为多传感器系统,对预存储的多周期高帧率图像进行离线处理,设计周期运动特性关键像素点提取算法,以这些像素点为固定的多个子传感器,将图像简化为一维向量,并建立周期运动特性样本序列数据库,实现基于视觉平台的高速机械运动相位和频率检测技术。为验证算法的有效性和系统的稳定性,利用步进电动机带动的旋转装置和缝纫机的机械运动部分进行试验,结果证明了本算法和系统可以正确的提取周期运动特性关键像素点,建立样本数据库,完成高速实时视觉检测。     

基于数字图像处理的印刷电路板缺陷检测

针对传统PCB检测方法高成本、低效率问题,提出一种基于数字图像处理的PCB板自动光学检测方法,建立了将标准图像和待测图像进行对比的检测系统。针对PCB板生产过程中遇到的短路、断路、空洞、凸起、凹陷等缺陷,采用了求连通区域数、计算欧拉数、求缺陷区域面积等方法,来检测上述缺陷问题,并给出算法的全过程。经实验结果分析,该方法能准确地检测出待检测电路板上存在的缺陷,达到了自动实时检测的目的。     

基于Hough变换的水下管道检测方法

由于海底管道背景复杂,简单的检测算法不能给出理想的检测结果;复杂的检测算法又不能满足实时作业要求。为了克服上述问题,实时而又准确地检测水下管道,通过分析水下图像的特点以及传统Hough变换对于管道直线信息提取的局限性,有针对性地提出了对Hough变换方法的2步改进,包括前期处理改善像素点的分布以减少参与Hough变换的无效像素点的数目,和Hough变换过程中的峰值点逆向清零以保证管道边界检测的唯一性。在仿真环境和水池环境下分别对改进的方法和未改进的方法进行了对比实验,结果表明前者可以有效地提高管道检测的实时性和精确性。     

Welding deviation detection algorithm based on extremum of molten pool image contour

The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding(GMAW) molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels(0.16 mm), and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.     

药品生产线瓶盖缝隙的视觉检测技术研究

结合药品生产线瓶盖缝隙检测的具体需求,提出一种视觉成像采集系统的筛选与设计思路以及在此基础上的视觉图像处理方法。分析了视觉图像信息采集系统与具体生产线需求的关系,确定了图像采集系统的参数,设计了透射式照明与成像采集系统架构。在获取视觉图像后,提出使用区域标记算法、canny边缘检测算法、黑白二值化算法等,自动寻找瓶盖区域并确定瓶盖缝隙的像素宽度,最终给出实际物理宽度。测试实验表明,该系统测量的准确率为100%,最大误差值为0.016 mm,最大误差百分比为2.73%,平均误差百分比为1.15/%。     

基于红外图像识别的电气设备温升检测

为了更好地利用红外热成像技术对电气设备故障进行识别和诊断的问题,提出了一种基于红外图像特征和种子区域生长法的设备温升检测方法。采用邻域平均法减少了红外图像的噪声干扰,提取出了红外图像RGB空间中的红色分量图及绿色分量图,应用种子区域生长法分别对两分量图进行了分割,先通过寻找局部最高温点划分区域,再计算各区域内形态学梯度筛选出有故障的高温点,并将该点所在区域作为种子区域从而实现种子区域的自动选取,将像素点4个方向上的最大梯度及像素点与种子点的灰度差作为种子生长的判定条件,用交集的方法将分割后的红色分量图和绿色分量图融合,提取出了设备温升过高区域。实验及研究结果表明,该方法能确定高温升区域,且轮廓清晰,为电气设备温升故障诊断提供依据。     

Ship Detection in Optical Satellite Image Based on RX Method and PCAnet

In this paper, we present a novel method for ship detection in optical satellite image based on the ReedXiaoli (RX) method and the principal component analysis network (PCAnet). The proposed method consists of the following three steps. First, the spatially adjacent pixels in optical image are arranged into a vector, transforming the optical image into a 3D cube image. By taking this process, the contextual information of the spatially adjacent pixels can be integrated to magnify the discrimination between ship and background. Second, the RX anomaly detection method is adopted to preliminarily extract ship candidates from the produced 3D cube image. Finally, real ships are further confirmed among ship candidates by applying the PCAnet and the support vector machine (SVM). Specifically, the PCAnet is a simple deep learning network which is exploited to perform feature extraction, and the SVM is applied to achieve feature pooling and decision making. Experimental results demonstrate that our approach is effective in discriminating between ships and false alarms, and has a good ship detection performance.     

陶瓷墙地砖生产线实时监控和生产管理系统的实现

结合当前陶瓷墙地砖生产企业的现状和实际需求,设计和实施了一套陶瓷墙地砖生产线实时监控和生产管理系统。重点分析了该系统的总体架构、实时监控系统网络以及功能模块,并对系统实现中相关的关键技术进行了论述。     

Accurate shape from focus based on focus adjustment in optical microscopy

Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus–sharpness–is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming. Microsc. Res. Tech., 2009. © 2008 Wiley-Liss, Inc.     

纹理粗糙度在红外图像显著性检测中的应用

提出了基于纹理粗糙度的红外图像显著性检测算法,以解决红外图像对比度低,目标显著性检测难的问题。首先,研究了Tamura的粗糙度原理,对粗糙度进行分析和评价,提出了新的粗糙度计算方法。然后,将图像分解为超级像素集合,并计算超级像素的最大平均强度差;利用最大平均强度差定义超级像素的最佳尺度,作为纹理粗糙度的度量。最后,将超级像素区域均匀外延,利用粗糙度的局部对比度和灰度信息度量红外图像的显著性。通过实验验证了本文算法的有效性,结果表明:在10%的噪声水平下,本文粗糙度保持不变,粗糙度特征图一致性较好,而Tamura的粗糙度特征图中杂点明显增多。与其它显著性检测算法对比,本文算法击中率最高,为0.752。该算法挖掘了红外图像的纹理粗糙度特征,为红外图像显著性检测提供了新的特征选择。     

CCD传感器在人体运动图像检测系统中的应用

介绍了一种基于CCD传感器的人体运动图像检测新方法,它采用图像动态采集及标志点自动识别跟踪技术,实现了运动图像的在线采集与分析;利用该方法构建了二维和三维人体运动图像检测分析系统,并将其用于人体步态和人手运动分析。     

A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces

Optical instruments are widely used for precision surface measurement. However, the dynamic range of optical instruments, in terms of measurement area and resolution, is limited by the characteristics of the imaging and the detection systems. If a large area with a high resolution is required, multiple measurements need to be conducted and the resulting datasets needs to be stitched together. Traditional stitching methods use six degrees of freedom for the registration of the overlapped regions, which can result in high computational complexity. Moreover, measurement error increases with increasing measurement data. In this paper, a stitching method, based on a Gaussian process, image registration and edge intensity data fusion, is presented. Firstly, the stitched datasets are modelled by using a Gaussian process so as to determine the mean of each stitched tile. Secondly, the datasets are projected to a base plane. In this way, the three-dimensional datasets are transformed to two-dimensional (2D) images. The images are registered by using an (x, y) translation to simplify the complexity. By using a high precision linear stage that is integral to the measurement instrument, the rotational error becomes insignificant and the cumulative rotational error can be eliminated. The translational error can be compensated by the image registration process. The z direction registration is performed by a least-squares error algorithm and the (x, y, z) translational information is determined. Finally, the overlapped regions of the measurement datasets are fused together by the edge intensity data fusion method. As a result, a large measurement area with a high resolution is obtained. A simulated and an actual measurement with a coherence scanning interferometer have been conducted to verify the proposed method. The stitching result shows that the proposed method is technically feasible for large area surface measurement.     

套准偏差自动检测方法

目前,国内多色印刷机多采用手动方式调整套准精度,自动化程度较低,为解决该问题,提出一种套准偏差自动检测方法。基于视觉成像原理拍摄套准标记图像,采用图像分析理论建立套准偏差计算方法。通过彩色图像矢量信号滤波法预处理标记图像并提取图像目标,给出青(C)、品红(M)、黄(Y)、黑(B)四种原色及其复合色像素的分色归类模型,在此基础上检测四种原色套准标记的边缘像素,进一步结合最小二乘和Hough变换法拟合套准标记的多条边缘直线,最后在图像中计算四种原色特征直线之间的偏移距离,通过视觉标定给出物空间的套准偏差。试验结果表明该方法自动检测套准偏差的精度高于20 μm,符合国家印刷行业标准规定的套准精度要求,适用于以C、M、Y、K合成原理为基础的双色、四色胶印系统。     

遥感相机焦面CCD机械拼接中重叠像元数的确定

针对推扫式遥感相机前后摆成像中焦面拼接的CCD其像片间出现视场漏缝的问题,提出了一种焦面CCD的非均匀重叠像元数机械拼接方法.首先,构造了相机前后摆的成像原理模型;接着,分析了CCD间产生地面视场漏缝的原因,并依据模型推导出相机前摆和后摆时各CCD视场间漏缝大小的数学公式,从而得到各片间容许的最少重叠像元数量公式.在此基础上,进一步给出了后续电子学图像拼接与配准中片间重复像元数的函数形式,为实现图像无缝拼接提供了软件实现依据.分析表明,该方法的理论误差ε小于一个像元尺寸,即|ε/D|＜1.最后,给出了工程应用实例.     

Weld deviation detection based on wide dynamic range vision sensor in MAG welding process

Weld deviation detection is a precondition for welding automation. Capturing high-quality welding images and extracting deviation information using image processing methods are two steps for weld deviation detection. In this paper, based on the analysis of the imaging characteristics during metal active gas (MAG) welding process, real-time welding images are acquired clearly and steadily using a wide dynamic range vision sensor. According to the connection relationship between the top of the arc and the end of the wire during MAG welding process, a method for determining the wire centreline is proposed. After extracting the precise arc region, the region of interest (ROI) is segmented along the welding direction. To detect the two edges of the V-groove, an improved Canny algorithm is developed. Furthermore, both the Hough Transform and a screening method based on prior knowledge are used to connect V-groove edges. The V-groove centreline is then determined. As a result, the weld deviation between the wire centreline and the V-groove centreline is calculated. Further experiments showed that the precision range of deviation detection can be controlled within ±0.25 mm, which can meet the requirement of real-time welding. This deviation can be used as the input variable for a welding robot, thus laying the foundation for MAG welding automation.