A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix (or matching result) reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segme...A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.展开更多
To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photograp...To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photographed by a fundus microscope. The kernel of this new algorithm is the scale-,rotation-and illumination-invariant interest point detector & feature descriptor-Scale-Invariant Feature Transform. When matched interest points according to second-nearest-neighbor strategy,the parameters of the model are estimated using the correct matches of the interest points,extracted by a new inlier identification scheme based on Sampson distance from putative sets. In order to preserve image features,bilinear warping and multi-band blending techniques are used to create panoramic retinal images. Experiments show that the proposed method works well with rejection error in 0.3 pixels,even for those cases where the retinal images without discernable vascular structure in contrast to the state-of-the-art algorithms.展开更多
Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has ...Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.展开更多
Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform(SIFT) features on 3D mes...Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform(SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis(PCA).Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.展开更多
On the basis of scale invariant feature transform(SIFT) descriptors,a novel kind of local invariants based on SIFT sequence scale(SIFT-SS) is proposed and applied to target classification.First of all,the merits o...On the basis of scale invariant feature transform(SIFT) descriptors,a novel kind of local invariants based on SIFT sequence scale(SIFT-SS) is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine(SVM) with radial based function(RBF) kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants(AMI) and multi-scale auto-convolution(MSA) in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.展开更多
In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines ...In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.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
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
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix (or matching result) reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
基金The National Natural Science Foundation of China(No60271033)
文摘A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.
基金Program for NewCentury Excellent Talents in UniversityGrant number:50051+1 种基金The Key Project for Technology Research of Ministry Education of ChinaCrant number:106030
文摘To meet the needs in the fundus examination,including outlook widening,pathology tracking,etc.,this paper describes a robust feature-based method for fully-automatic mosaic of the curved human retinal images photographed by a fundus microscope. The kernel of this new algorithm is the scale-,rotation-and illumination-invariant interest point detector & feature descriptor-Scale-Invariant Feature Transform. When matched interest points according to second-nearest-neighbor strategy,the parameters of the model are estimated using the correct matches of the interest points,extracted by a new inlier identification scheme based on Sampson distance from putative sets. In order to preserve image features,bilinear warping and multi-band blending techniques are used to create panoramic retinal images. Experiments show that the proposed method works well with rejection error in 0.3 pixels,even for those cases where the retinal images without discernable vascular structure in contrast to the state-of-the-art algorithms.
文摘Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.
基金Project(XDA06020300)supported by the"Strategic Priority Research Program"of the Chinese Academy of SciencesProject(12511501700)supported by the Research on the Key Technology of Internet of Things for Urban Community Safety Based on Video Sensor networks
文摘Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform(SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis(PCA).Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.
基金supported by the National High Technology Research and Development Program (863 Program) (2010AA7080302)
文摘On the basis of scale invariant feature transform(SIFT) descriptors,a novel kind of local invariants based on SIFT sequence scale(SIFT-SS) is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine(SVM) with radial based function(RBF) kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants(AMI) and multi-scale auto-convolution(MSA) in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.
基金the National Science Foundation of China(No.61471185)the Natural Science Foundation of Shandong Province(No.ZR2016FM21)+1 种基金Shandong Province Science and Technology Plan Project(No.2015GSF116001)Yantai City Key Research and Development Plan Project(Nos.2014ZH157 and2016ZH057)
文摘In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.
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.
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.