An algorithm for detecting moving IR point target in complex background is proposed, which is based on the Reverse Phase Feature of Neighborhood （RPFN） of target in difference between neighbor frame images that two ...An algorithm for detecting moving IR point target in complex background is proposed, which is based on the Reverse Phase Feature of Neighborhood （RPFN） of target in difference between neighbor frame images that two positions of the target in the difference image are near and the gray values of them are close to in absolute value but with inverse sign. Firstly, pairs of points with RPFN are detected in the difference image between neighbor frame images, with which a virtual vector graph is made, and then the moving point target can be detected by the vectors＇ sequence cumulated in vector graphs. In addition, a theorem for the convergence of detection of target contrail by this algorithm is given and proved so as to afford a solid guarantee for practical applications of the algorithm proposed in this paper. Finally, some simulation results with 1000 frames from 10 typical images in complex background show that moving point targets with SNR not lower than 1.5 can be detected effectively.展开更多
文摘An algorithm for detecting moving IR point target in complex background is proposed, which is based on the Reverse Phase Feature of Neighborhood （RPFN） of target in difference between neighbor frame images that two positions of the target in the difference image are near and the gray values of them are close to in absolute value but with inverse sign. Firstly, pairs of points with RPFN are detected in the difference image between neighbor frame images, with which a virtual vector graph is made, and then the moving point target can be detected by the vectors＇ sequence cumulated in vector graphs. In addition, a theorem for the convergence of detection of target contrail by this algorithm is given and proved so as to afford a solid guarantee for practical applications of the algorithm proposed in this paper. Finally, some simulation results with 1000 frames from 10 typical images in complex background show that moving point targets with SNR not lower than 1.5 can be detected effectively.
