To segment high-resolution remote sensing images(RSIs)accurately on an object level and meet the precise boundary dividing requirement,an improved superpixel segmentation and region merging algorithm is proposed.Simpl...To segment high-resolution remote sensing images(RSIs)accurately on an object level and meet the precise boundary dividing requirement,an improved superpixel segmentation and region merging algorithm is proposed.Simple linear iterative clustering(SLIC)is widely used because of its advantages in performance and effect;however,it causes over-segmentation,which is very disadvantageous to information extraction.In this proposed method,SLIC is firstly adopted for initial superpixel partition.The second stage follows the iterative merging procedure,which uses a hierarchical clustering algorithm and introduces a local binary pattern(LBP)texture feature operator during the process of merging.The experimental results indicate that the proposed method achieved a good segmentation and region merging performance,and worked effectively on cloud detection preprocessing in high-resolution RSIs with cloud and snow overlap situations.展开更多
Disparity estimation is an ill-posed problem in computer vision. It is explored comprehensively due to its usefulness in many areas like 3D scene reconstruction, robot navigation, parts inspection, virtual reality and...Disparity estimation is an ill-posed problem in computer vision. It is explored comprehensively due to its usefulness in many areas like 3D scene reconstruction, robot navigation, parts inspection, virtual reality and image-based rendering. In this paper, we propose a hybrid disparity generation algorithm which uses census based and segmentation based approaches. Census transform does not give good results in textureless areas, but is suitable for highly textured regions. While segment based stereo matching techniques gives good result in textureless regions. Coarse disparities obtained from census transform are combined with the region information extracted by mean shift segmentation method, so that a region matching can be applied by using affine transformation. Affine transformation is used to remove noise from each segment. Mean shift segmentation technique creates more than one segment of same object resulting into non-smoothness disparity. Region merging is applied to obtain refined smooth disparity map. Finally, multilateral filtering is applied on the disparity map estimated to preserve the information and to smooth the disparity map. The proposed algorithm generates good results compared to the classic census transform. Our proposed algorithm solves standard problems like occlusions, repetitive patterns, textureless regions, perspective distortion, specular reflection and noise. Experiments are performed on middlebury stereo test bed and the results demonstrate that the proposed algorithm achieves high accuracy, efficiency and robustness.展开更多
Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low compu...Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50ms to process a 481x321 image on a single Intel i3 CPU at 2.5 GHz.展开更多
文摘To segment high-resolution remote sensing images(RSIs)accurately on an object level and meet the precise boundary dividing requirement,an improved superpixel segmentation and region merging algorithm is proposed.Simple linear iterative clustering(SLIC)is widely used because of its advantages in performance and effect;however,it causes over-segmentation,which is very disadvantageous to information extraction.In this proposed method,SLIC is firstly adopted for initial superpixel partition.The second stage follows the iterative merging procedure,which uses a hierarchical clustering algorithm and introduces a local binary pattern(LBP)texture feature operator during the process of merging.The experimental results indicate that the proposed method achieved a good segmentation and region merging performance,and worked effectively on cloud detection preprocessing in high-resolution RSIs with cloud and snow overlap situations.
文摘Disparity estimation is an ill-posed problem in computer vision. It is explored comprehensively due to its usefulness in many areas like 3D scene reconstruction, robot navigation, parts inspection, virtual reality and image-based rendering. In this paper, we propose a hybrid disparity generation algorithm which uses census based and segmentation based approaches. Census transform does not give good results in textureless areas, but is suitable for highly textured regions. While segment based stereo matching techniques gives good result in textureless regions. Coarse disparities obtained from census transform are combined with the region information extracted by mean shift segmentation method, so that a region matching can be applied by using affine transformation. Affine transformation is used to remove noise from each segment. Mean shift segmentation technique creates more than one segment of same object resulting into non-smoothness disparity. Region merging is applied to obtain refined smooth disparity map. Finally, multilateral filtering is applied on the disparity map estimated to preserve the information and to smooth the disparity map. The proposed algorithm generates good results compared to the classic census transform. Our proposed algorithm solves standard problems like occlusions, repetitive patterns, textureless regions, perspective distortion, specular reflection and noise. Experiments are performed on middlebury stereo test bed and the results demonstrate that the proposed algorithm achieves high accuracy, efficiency and robustness.
文摘Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50ms to process a 481x321 image on a single Intel i3 CPU at 2.5 GHz.
