基于Camshift算法的神经丝蛋白自动跟踪

神经丝蛋白体型细长、形态多变,跟踪难度大。为了能够准确稳定的获取神经丝蛋白质的运动状态,利用了改进的Camshift算法实现了对神经丝蛋白运动的自动跟踪。利用HSV颜色空间的颜色直方图建立目标模型,结合预测点对目标特征点加权。通过在目标颜色概率模型中引入核函数,利用核密度梯度来进行目标搜索,最终在每帧图像中获取目标的具体位置。鉴于神经丝蛋白的特殊性,还对比分析了其他两种概率预测类算法的跟踪效果。实验结果表明,此方法能够快速稳定跟踪神经丝蛋白,为神经丝蛋白质的医学研究提供了新的途径。

Neurofilament Protein Automaticaly Tracking Using Camshift Algorithm

Neurofilament proteins are small in size,changeable in morphology,and difficult to track.In order to accurately and stably obtain the movement state of neurofilament proteins,using the improved Camshift algorithm to achieve the automatic tracking for neurofilament proteins.Using the color histogram in HSV color space to establish a target model and weighting target feature points according to prediction point.introducing a kernel function into the target color probability model so that uses the kernel density gradient to Search object,finally obtains the specific location of the object in each frame of image.thanks to the particularity of the neurofilament protein,this paper also compares and analyzes the tracking effects of two other probabilistic prediction algorithms.The experimental results show that this method can quickly and stably track neurofilament proteins and provide a new approach for neurofilament protein research.