用图像模板测量技术检测零件不规则薄圆孔的研究

针对零件的不规则薄孔,常规的方法无法对其精确测量,提出了一种基于数字图像处理的模板匹配检测方法,运用数字图像处理技术对采集的圆孔网像进行截取、降噪、分割、边缘检测等处理,再把提取出的圆与标定模饭上的标准阋进行比较,实现对不规则网孔尺寸的检测,并通过实验证明了陔方法的可行性和实用性.     

一种基于模板匹配的目标识别方法

在基于模板的目标识别方法中,模板的精度是一个关键问题。由于合成孔径雷达成像机理的原因,在SAR图像中会出现迎坡缩短、顶底倒置和遮挡等现象。因此,SAR图像中的目标形状与一般光学图像中的不同。在进行模板匹配的过程中,其模板也会有相应的变化。针对该问题提出一种目标识别方法,使用新的模板生成方式,即利用OpenGL技术并根据特定目标模型生成符合SAR图像特征的目标模板,这是一种基于模型的识别方法。使用MSTAR提供的数据对该方法进行验证,试验结果证明了方法的有效性。     

Target localization based on cross-view matching between UAV and satellite

Matching remote sensing images taken by an unmanned aerial vehicle (UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset (University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.