Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speed...Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.展开更多
The scale-invariant feature transform (SIFT) is often applied to extract tie-points for airborne SAR images. When a pair of airborne SAR images differs with look angles obviously, shadow sizes and shapes of same objec...The scale-invariant feature transform (SIFT) is often applied to extract tie-points for airborne SAR images. When a pair of airborne SAR images differs with look angles obviously, shadow sizes and shapes of same objects will differ obviously. In main and slave SAR images, key-points around shadows often match as tie-points, although they are not homologous points. The phenomenon worsens the performance of SIFT on SAR images. On the basis of SIFT, a modified matching method is proposed to decrease the number of incorrect tie-points. High-resolution airborne SAR images are used in Experiments. Experiment results show that the proposed method is very effective to extract correct tie-points for SAR images.展开更多
IR （Image Registration） is one of the important operation of image processing system which is the process of aligning two or more images into one coordinate system that are taken at different times, from different s...IR （Image Registration） is one of the important operation of image processing system which is the process of aligning two or more images into one coordinate system that are taken at different times, from different sensors, or from different viewpoints. It has a lot of applications especially medical imaging and remote sensing. The main purpose of this paper is to provide a comprehensive review of existing literatures available on image registration system and proposed a new feature-based IR technique using edge of images. We used edges as a feature of images for registration. It will be a useful document for researchers who will work on feature-based image registration regardless for specific applications.展开更多
To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, ...To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, 3 rotation-invariant concentric-ring grids around the key-point location are used instead of 16 square grids used in the original SIFT. Then, 10 orientations are accumulated for each grid, which results in a 30-dimension descriptor. In descriptor matching, rough rejection mismatches is proposed based on the difference of grey information between matching points. The per- formance of the proposed method is tested for image mosaic on simulated and real-worid images. Experimental results show that the M-SIFT descriptor inherits the SIFT＇ s ability of being invariant to image scale and rotation, illumination change and affine distortion. Besides the time cost of feature extraction is reduced by 50% compared with the original SIFT. And the rough rejection mismatches can reject at least 70% of mismatches. The results also demonstrate that the performance of the pro- posed M-SIFT method is superior to other improved SIFT methods in speed and robustness.展开更多
In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information imag...In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.展开更多
An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utiliz...An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.展开更多
Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessme...Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessment has been crack detection, rather than direct analysis of image features such as aggregate loss, changes in surface texture or deterioration of road markings. Any attempt to assess pavement condition change from features in a sequence of such images captured months or years apart requires image registration. A method for registering road pavement images is presented that makes use of an affine transformation based on pseudo-features within images. An affine trans- formation is considered suitable for registering road pavement images because of the linear way in which pavements are surveyed. Pseudo feature points are found using a modified corner detector, and then matching points between reference and template im- ages established via a correlation analysis of pavement image texture. With 4 such points it is possible to establish an affine transformation between the images. The method is tested on pavement images captured on three UK sites between winter 2014/15 and 2015/16. The method successfully registered 98% of images captured on sites typical of the UK＇s strategic road network, and 65% of images captured on a site typical of the UK＇s minor road network.展开更多
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.
基金Supported by the National Key Research and Development Program of China(No.2016YFB0502502)the Special Research and Trial Production Project of Sanya(No.sy17xs0113)
文摘The scale-invariant feature transform (SIFT) is often applied to extract tie-points for airborne SAR images. When a pair of airborne SAR images differs with look angles obviously, shadow sizes and shapes of same objects will differ obviously. In main and slave SAR images, key-points around shadows often match as tie-points, although they are not homologous points. The phenomenon worsens the performance of SIFT on SAR images. On the basis of SIFT, a modified matching method is proposed to decrease the number of incorrect tie-points. High-resolution airborne SAR images are used in Experiments. Experiment results show that the proposed method is very effective to extract correct tie-points for SAR images.
文摘IR （Image Registration） is one of the important operation of image processing system which is the process of aligning two or more images into one coordinate system that are taken at different times, from different sensors, or from different viewpoints. It has a lot of applications especially medical imaging and remote sensing. The main purpose of this paper is to provide a comprehensive review of existing literatures available on image registration system and proposed a new feature-based IR technique using edge of images. We used edges as a feature of images for registration. It will be a useful document for researchers who will work on feature-based image registration regardless for specific applications.
基金Supported by the National Natural Science Foundation of China(60905012)
文摘To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, 3 rotation-invariant concentric-ring grids around the key-point location are used instead of 16 square grids used in the original SIFT. Then, 10 orientations are accumulated for each grid, which results in a 30-dimension descriptor. In descriptor matching, rough rejection mismatches is proposed based on the difference of grey information between matching points. The per- formance of the proposed method is tested for image mosaic on simulated and real-worid images. Experimental results show that the M-SIFT descriptor inherits the SIFT＇ s ability of being invariant to image scale and rotation, illumination change and affine distortion. Besides the time cost of feature extraction is reduced by 50% compared with the original SIFT. And the rough rejection mismatches can reject at least 70% of mismatches. The results also demonstrate that the performance of the pro- posed M-SIFT method is superior to other improved SIFT methods in speed and robustness.
基金supported by the National Key Basic Research and Development (973) Program of China (No. 2013CB329006)the National Natural Science Foundation of China (Nos. 61471220 and 61021001)
文摘In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.
文摘An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.
文摘Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessment has been crack detection, rather than direct analysis of image features such as aggregate loss, changes in surface texture or deterioration of road markings. Any attempt to assess pavement condition change from features in a sequence of such images captured months or years apart requires image registration. A method for registering road pavement images is presented that makes use of an affine transformation based on pseudo-features within images. An affine trans- formation is considered suitable for registering road pavement images because of the linear way in which pavements are surveyed. Pseudo feature points are found using a modified corner detector, and then matching points between reference and template im- ages established via a correlation analysis of pavement image texture. With 4 such points it is possible to establish an affine transformation between the images. The method is tested on pavement images captured on three UK sites between winter 2014/15 and 2015/16. The method successfully registered 98% of images captured on sites typical of the UK＇s strategic road network, and 65% of images captured on a site typical of the UK＇s minor road network.
