机械式冲床停上死点位置的控制策略设计与实现

车牌定位是车牌自动识别的第一步,而如何考虑当照影响是车牌定位是否成功的关键。本文提出了一种考虑不同光照条件的车牌定位方法,首先,针对不同光照条件下采集到的车牌图像明暗度的不同,利用多阈值处理方法得到车牌信息不丢失的、最佳的二值图像,然后,在其二值图像中首先利用灰度跳变定位车牌的上下边界,接着对字符垂直投影后的宽度进行统一的调整并以固定的字符间隔特征定位车牌的左右边界,从而完成车牌定位。最后,通过实验验证了该方法的有效性。     

一种复杂车辆图像中的多车牌定位方法

针对复杂背景中多个车牌的定位问题,提出一种新的定位方法.该方法综合利用边缘检测、连通域分析、倾斜矫正等多种方法,解决了复杂背景中定位难的问题.能够准确定位杂乱背景中的车牌,对天气、光照变化、车牌在图像中的移动和旋转等,具有良好的适应能力.该方法为后续的字符分割和字符识别提供旋转角度、字符区域定位信息.     

基于BP神经网络的车牌字符识别算法研究

车牌字符识别是车牌识别系统中的关键环节。采用图像处理和神经网络相结合的方法设计新的车牌字符识别算法,先对分割出的车牌字符进行归一化处理,然后进行SOBEI．边缘检测和角点特征提取,最后输入BP神经网络进行训练、识别,其中BP神经网络模型属于改进型神经网络。通过一系列神经网络训练和仿真实验,车牌识别速度和正确率得到了明显的提高。     

License plate location based on trichromatic imaging and color-discrete characteristic

License plate location (LPL) is the key part of automatic license plate recognition (LPR) system that plays an important role in many applications and a number of LPL methods based on color information have been proposed. However, some problems, such as country specific, similar color interference, lighting variation sensitive, time consuming, processing only one kind of LP with same color combination a time and omitting the case of more than one combinations in a LP, should be solved further. In this study, a color-based LPL method that consists of three modules: color edge extraction, denoising and searching, is proposed. Color edge extraction, the kernel of the proposed, is designed by color-discrete characteristics of license plates in the trichromatic wavebands. In the experiment, 1384 images taken from natural scenes in China and other 104 countries or regions are employed. Of which, 74 have been failed to locate the license plates. The success rate and average execution time are 94.7% and 57 ms, respectively.     

Detection and tracking strategy for license plate detection in video

In this paper, we present a novel method to detect license plates in video sequences automatically. The framework mainly integrates the cascade detectors method and the Tracking Learning Detection (TLD) algorithm. The cascade detectors are used to detect license plates, and the TLD algorithm is adopted to track the license plate regions. The license plates in the first frame image are detected by the cascade detectors to build the original tracking list, the tracking results and the detection results in following frames will be compared, and the newly appearing license plate information will be added to the tracking list. Meanwhile, the tracking results existing in the current tracking list would be replaced by the corresponding detection results with higher degree of confidence. We demonstrate the effectiveness of our algorithm for license plate detection task on a road intersection dataset, and the experimental evaluation shows the detection performance has been greatly improved by synthetically using the detection and tracking strategy.     

驾驶人眼睛区域的鲁棒性定位算法研究

疲劳驾驶是造成交通事故的重要原因. 通过机器视觉技术对眼睛动作和视线转移特征的分析可实现驾驶人疲劳状态的有效估计. 然而, 实际行车环境中光照条件的随机、快速变化以及驾驶人面部姿态的不确定性使得眼睛区域的鲁棒性定位变得异常困难. 为此, 本文引入基于点分布模型的主动形状模型(ASM)算法并针对其在实际行车环境中存在的问题提出了三点改进. 首先, 建立了基于自商图的局部纹理模型以克服光照变化的影响; 其次, 充分利用面部局部区域良好的聚类性, 建立了层叠式全局形状模型, 以适应驾驶人姿态的大角度偏转; 再次, 在行车过程中, 通过对驾驶人面部形状的在线学习实现模型参数分布特征的获取, 为ASM算法的配准提供了更加紧致的约束. 实验结果显示, 本文算法对光照和姿态变化具有较强的鲁棒性, 在驾驶人面部器官不发生自遮挡的情况下可实现眼睛区域的高精度配准.     

未系安全带智能识别中安全带定位关键方法研究

安全带定位是实现机动车未系安全带智能识别的关键。针对道路监控图像特点,提出一种基于梯度变换的安全带定位方法。该方法在对卡口图像进行预处理的基础上,采用自适应阈值边缘检测算法以及积分投影方式定位车辆位置,设立车牌检测区域,以减少运算量,降低干扰,同时利用训练得到的Haar分类器识别车牌位置,通过逼近方式切取车辆右侧图像。最后采用梯度变换算法求得车窗各边缘坐标,实现车窗精准定位,并计算得到安全带位置。试验表明,该方法可实现安全带的准确定位,具有较好的实用性,为后续安全带识别奠定基础。     

Graying for images with color-discrete characteristic

It is realized that there is a category of images with color-discrete characteristic, widely investigated in the fields of machine vision, image processing and pattern recognition. Despite the authority of weighted-average method on graying, it is not robust enough in the preceding case. In this paper, a graying method based on trichromatic imaging and color-discrete characteristic is proposed. Detailed experimental evidence has demonstrated that this method can maximize contrast of target region furthest while graying; also this feature can improve performance of thresholding and image enhancement, which has significant effect on image processing tasks in natural scenes. In addition, the proposed may help to optimize design of imaging devices.     

一种改进的小波隐性马尔可夫树模型纹理分割及在车牌定位中的应用

小波隐性马尔可夫树模型(WHMTM)能对纹理特征进行多尺度的精确描述。在WHMTM纹理分割的基础上提出了一种改进的方法,并已将它应用到车牌定位中。在WHMTM中,通过引入用于描述尺度间邻域相关性的指数加权系数和相同尺度内邻域纹理分类的标识变量,使多尺度粗分割结果的融合更加精确。实验结果表明,该方法对多尺度的纹理分割及车牌定位有更好的效果。     

基于多核的车牌识别的架构实现

多核技术是现在提高芯片性能的主要方法。区别于传统以PC和DSP为核心的车牌识别系统,以FPGA为核心,利用SOPC技术构建了车牌识别多核处理器。给出了一种基于多核的车牌识别架构,在该多核处理器中,以3个Nios II 软核为主要处理器核处理车牌定位、字符特征识别提取及识别等处理,同时构建硬件加速器作为协处理器处理图像增强、边缘检测和膨胀、腐蚀等数学形态学处理。在CQ片上路由器基础上,构建了NOC用以实现片上多核通信。另外,为了保证路由器与多处理器核之间的快速、并行通信,加入了数据驱动模块。整个系统在Altera Cyclone IV FPGA上实现了车牌的识别。这种片上系统设计方法具有硬件设计灵活,可扩展性强等优点,能有效地降低系统软硬件设计的难度,缩短开发周期,并提高设计的可靠性。     

基于特征自适应选择的金字塔均值漂移跟踪方法

针对均值漂移跟踪算法框架不足以对目标帧间运动过大及快速尺度变化进行有效地处理,且单个图像特征对环境适应性较差.提出了一种特征自适应选择方法,通过分析目标与背景的特征区分度来选择出最有效的特征.将金字塔自适应分解和均值漂移跟踪结合,提出了金字塔均值漂移跟踪方法.采用背景加权直方图描述目标模板模型,核函数加权直方图描述候选...     

Indirect target detection method in FLIR image sequences

Due to the complexity of the scene, target detection in forward-looking infrared (FLIR) imagery is a challenging problem, especially for occluded target. The main contribution of this paper is to propose an indirect detection method for improving the recognition probability and effectiveness of target detection method in FLIR image sequences under complex conditions. The proposed method mainly includes four steps: preparation of forward-looking reference image of landmark, extraction of the real-time scene image, template matching and target location, in which some key technologies are proposed, such as perspective transformation used to solve projective problems, position prediction for improving real-time performance, and target location used for identifying the target’s position. Experimental results are shown to demonstrate the robustness and efficiency of proposed method in FLIR image sequences.     

基于混合自适应小波基的织物疵点检测算法

为了改进具有复杂纹理织物的疵点检测效果,提出了一种基于混合自适应小波基的织物疵点检测算法,采用各自优化的自适应小波基实现对不同层织物图像的分解变换。首先对正常图像和其经一层小波分解后的低频子图像优化得到混合自适应小波基,然后用该小波基将织物疵点图像进行二层小波分解,最后采用阈值法对径向子图像进行分割得到检测结果。实验结果证明,本文提出的算法能有效实现疵点检测,具有较好的疵点分割和定位结果。     

Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness.     

License plate detection using gradient information and cascade detectors

In this paper, we propose a license plate detection method based on the gradient information and a cascade detection framework. The proposed method consists of three main modules: image preprocessing, license plate detection and license plate confirmation. Considering the diversities of Chinese license plate, we first get the gradient images to unify plate forms through image preprocessing. The license plate will be detected roughly by using the cascade AdaBoost classifier in the next step. Finally, several heuristic judgment strategies and a voting-based method are used to verify the candidate license plates. The effectiveness of our method is validated with an experimental study in license plate detection.     

一种新的红外成像末制导目标跟踪方法

为了稳定跟踪导弹末制导阶段的红外目标,提出了一种基于尺度不变特征变换的红外目标跟踪算法.尺度不变性特征变换所提取的图像纹理特征具有尺度和旋转不变性,跟踪算法分别提取目标模板和待跟踪图像的尺度不变特征变换特征.根据最小欧氏距离准则提取目标模板与待跟踪图像间相匹配的尺度不变特征变换特征点对,利用该特征点对拟合反映两图像间映射关系的仿射模型,并据此估计目标中心位置及调整目标模板尺寸.仿真结果表明,跟踪算法能够较好地实现在导弹末制导阶段对红外地面杂波背景下目标的稳定跟踪,其跟踪准确度和稳定度优于传统方法.
关键词:末制导跟踪|尺度不变性特征变换|特征匹配|仿射模型     

Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging

The purpose of this study was to evaluate the potential utility of texture analysis of parametric apparent diffusion coefficient (ADC) maps in quantifying hepatic fibrosis. To this end, using ex vivo murine liver tissues from a dietary model of hepatic fibrosis, an array of texture analysis techniques, including histogram-based, gray-level co-occurrence matrix-based, and gray-level run-length-based features, was used to evaluate correlations with liver fibrosis. Moderate to very strong correlation between several of the texture-based features and both subjective as well as digital image analysis-based assessments of hepatic fibrosis was demonstrated. This rigorous study of texture analysis applied to parametric ADC maps in a liver fibrosis model study demonstrates and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis.     

红外鱼眼系统下的多目标威胁评估研究

针对红外鱼眼告警系统在对几公里以外的目标成像时表现为点目标,无距离、几何形状和纹理信息可以利用,很难对来袭目标的威胁程度做出较准确的评估问题,研究了红外鱼眼系统下的多目标威胁评估排序方法。提出了一种红外鱼眼系统下的多目标威胁评估模型。该模型利用激光测距机所获取的各目标的初始距离信息推导出了目标在各时刻的距离和径向速度,从而建立起了以目标的距离、径向速度、航向角和高低角为威胁指标的多目标威胁评估模型。然后根据多目标威胁评估的非线性特点,以及神经网络在解决非线性复杂问题所具有的良好的自适应能力和自学习能力,利用径向基函数(RBF)神经网络对多目标威胁程度进行评估并讨论了训练样本的生成方法。实验结果证明了该方法的可行性和有效性。     

圆目标定位连续阈值质心算法研究

以DSP平台圆目标定位为研究对象,提出了连续阈值质心算法,利用算术平均原理对普通质心法进行了改进。结果表明,该算法减小了随机误差和系统误差。存在噪声的情况下,其定位精度能达到0.02～0.05pixels,同等情况下与普通质心法相比较,精度提高了4～10倍,抗噪声能力更强,而计算量却没有增加,能够用于DSP平台的圆目标快速精确定位。     

红外单站被动定位技术速度更新算法研究

针对红外单站被动定位技术中空中机动目标的运动速度不断变化这一实际情况,提出了一种基于红外相机运动特性的对目标的运动速度进行实时更新的校正算法.该算法克服了大多基于匀速直线运动这一空间模型的红外单站被动定位方案目标速度不变的缺点,客观地反映了空中机动目标的实际运动状况,不仅能对目标的运动速度进行实时准确的更新校正,而且可推演出一种新的红外单站被动定位算法,对推动红外单站被动定位技术的工程实用化进程具有重要的意义.