Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.     

基于数学形态学的气象杂波抑制方法

本文针对现有方法无法有效抑制气象杂波的问题,提出了一种基于数学形态学的气象杂波抑制方法.该方法将经过预处理之后的雷达距离-多普勒平面与形态学的图像处理方法相结合,如膨胀或腐蚀算法、两者的组合开运算或闭运算等,能够较准确地获得气象杂波所在区域范围,再结合雷达恒虚警检测后的平面,可剔除由于气象杂波造成的虚警点,实现对气象杂...     

一种基于多任务学习的微多普勒目标识别方法

近年来为了更好地适应不同场景下的智能化探测,人们对雷达目标识别任务的需求越来越高,因此本文提出了一种基于多任务学习方法的微多普勒目标识别方法.该方法基于BLSTM框架,针对两个不同的微多普勒目标识别任务使用特征提取器共享的方法训练模型.本方法既减小了识别系统整体的计算量,同时由于共享了不同任务之间的特征表示,又提高了模...