摘要
针对图像识别中图像尺寸比例不一致、旋转角度不相同,以及识别率低、鲁棒性差的问题,提出一种图像的形态学识别算法。首先对图像进行中心化及归一化处理,同时将图形的轮廓图像转换为二值图像;其次采用大小变化的圆形进行图像过滤,获取图形不同尺寸的形态学特征,建立扇形区域特征向量;最后采用多尺度的分析方法进行图像识别以及图像角度分析。在角度无关性、比例无关性、轮廓干扰鲁棒性下与传统方法进行对比实验,实验结果表明该方法有较高的识别率,并可以分析出图像间的角度差值,在图像轮廓有噪声的情况下有较好的鲁棒性,同时大大降低了图像尺寸比例不同、旋转角度不同对图像识别的影响。
Focusing on the issues of size-varying and angle-varying of the images, and low recognition rate and poor robustness in image recognition, a morphological image recognition method was proposed. Firstly, image was centralized and normalized, and the silhouettes of image was converted into binary image. Secondly, varable circles were used to extract morphological features of image, and a fan-shaped area feature vector was established. Finally, multi-scale analysis method was applied to image recognition and image angle analy.,~is. Compared with traditional method in the conditions such as angle independence, proportion independence and profile robustness, the experimental results show that the proposed method has higher recognition rate, and can analyze the angle difference between the images. The method is robust to noise, and can significantly reduce the influence of different image scale and rotation angle on image recognition.
出处
《计算机应用》
CSCD
北大核心
2015年第2期502-505,549,共5页
journal of Computer Applications
基金
辽宁省科技厅资助项目(20131097)
辽宁省教育厅资助项目(L2014082)
关键词
图像识别
计算机视觉
多尺度
形态学分析
鲁棒性
image recognition
computer vision
multi-scale
morphological analysis
robustness
