Image registration is an important research topic in the field of computer vision,in which the registration and mosaic of side-scan sonar images is the keypoints of underwater navigation.However,the image registration...Image registration is an important research topic in the field of computer vision,in which the registration and mosaic of side-scan sonar images is the keypoints of underwater navigation.However,the image registration method of keypoints is not suitable for sonar images which do not have obvious feature points.Therefore,a method of sonar-image registration and mosaic based on line segment extraction and triangle matching is proposed in this paper.Firstly,in order to extract features from sonar image,the LSD method is introduced to detect line feature from images,and line segments are filtered by the principle of attention;after that,triangles are formed from line segments,an image transformation matrix can be calculated through the heuristic greedy algorithm from these triangles;finally,images are merged based on the transformation information.On the basis of practical tests,it is found that,the feature extraction method used in this paper can better describe the outline of underwater terrain,and there is no obvious stitching gap between the result of sonar images stitched.Experimental results show that the proposed method is effective than the keypoints method of the registration and mosaic of sonar images.展开更多
Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such ...Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hypertension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm, in this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE （digital retinal images for vessel extraction） retinal database, which is much better than other methods.展开更多
文摘Image registration is an important research topic in the field of computer vision,in which the registration and mosaic of side-scan sonar images is the keypoints of underwater navigation.However,the image registration method of keypoints is not suitable for sonar images which do not have obvious feature points.Therefore,a method of sonar-image registration and mosaic based on line segment extraction and triangle matching is proposed in this paper.Firstly,in order to extract features from sonar image,the LSD method is introduced to detect line feature from images,and line segments are filtered by the principle of attention;after that,triangles are formed from line segments,an image transformation matrix can be calculated through the heuristic greedy algorithm from these triangles;finally,images are merged based on the transformation information.On the basis of practical tests,it is found that,the feature extraction method used in this paper can better describe the outline of underwater terrain,and there is no obvious stitching gap between the result of sonar images stitched.Experimental results show that the proposed method is effective than the keypoints method of the registration and mosaic of sonar images.
基金supported by the NSC under Grant NSC 102-2221-E-005-082
文摘Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hypertension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm, in this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE （digital retinal images for vessel extraction） retinal database, which is much better than other methods.
