In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems...In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.展开更多
Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establis...Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.展开更多
基金supported by National Forestry Public Welfare Industry Scientific Research Special Subsidy Project(201304502)
文摘In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.
基金Project(61172047)supported by the National Natural Science Foundation of China
文摘Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.
