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
A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a ...A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training set.In experiments provided,the comparisons with the nearest neighbor(NN),NFL,and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy decreases.MCFL proposed is probably a better choice for the classification application tasks of large-scale dataset.展开更多
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This work was supported by the State Key Development Program for Basic Research of China(No.2007CB311006).
文摘A novel classification approach called modified center-based feature line(MCFL)is proposed to reduce the computational cost of the nearest feature line(NFL)and maintain the advantages of NFL.Unlike NFL,MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training set.In experiments provided,the comparisons with the nearest neighbor(NN),NFL,and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy decreases.MCFL proposed is probably a better choice for the classification application tasks of large-scale dataset.
