Real-time hand tracking based on YOLOv4 model and Kalman filter

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%     

低比度钢板微小缺陷的图像增强和分割

针对低对比度、微小的钢板表面缺陷，提出了基于图像增强的图像分割算法来有效分割和识别缺陷目标。采用小波变换与同态滤波结合算法对图像进行增强处理，不仅消除照度不均的影响，还突出了缺陷细节的信息，达到图像增强的效果。最后，利用粒子群算法优化最大类间方差法参数（PSO-Otsu）确定增强后图像的最佳阈值，并结合Canny算子进行缺陷检测。对比其他算法，该算法在检测低对比度的微小缺陷上取得了良好的效果。     

基于Canny算子的改进图像边缘检测算法

图像的边缘检测是图像处理领域内最关键的技术之一.针对工件分拣中需要机器视觉精确的检测出其边缘信息,并且从噪声和其他无关信息中筛选出来,提出了一种改进的Canny算法对工件进行边缘检测.该算法利用双边滤波来替代高斯滤波进行图像预处理,从而不仅可以保留更多的图像边缘细节也可以有效的去除噪声.而后运用最大类间方差法(Otsu...