Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
To find out the best infrared and visible fusion system of fusion algorithm which has excellent target detection characteristics in different environment, we proposed a new fusion algorithm selective rule. We also defined new concepts: fusion algorithm coefficient and the equivalent transmissivity of system. Using local-target contrast, local-target articulation to calculate fusion algorithm coefficient, we can estimate the target detection performance of fusion system when it working in different air humidity environment. Also, we make use of infrared and visible fusion system designed by ourselves to verify this method. Besides fusion algorithm coefficient, we also use subjective evaluation to evaluate the target detection performance of fusion algorithm. At last, the best algorithm or the method which is most consistent with human visual in different conditions were found. Through this work, we can provide the basis for the algorithm of choice in the fusion system.
A new method for unsupervised segmentation of moving objects in infrared videos is presented. This method consists of two steps: difference image quantization and spatial segmentation. In the first step, the changed pixels in the difference image are quantized to several classes by using Bayes decision. It can be used to cluster the changed pixels belonging to the same moving object together. The pixels of the difference image are replaced by their corresponding class labels, thus forming a class-map of the difference image. In the second step, each class in the class-map is considered as a subset of the possible seeds of moving objects. A self-adaptive region growing method is then used to image segmentation on the basis of these different subsets. One of the focuses of this work is on spatial segmentation, where a criterion is proposed for evaluation of moving object segmentation without ground truth in infrared videos. This criterion is used to evaluate the performance of the segmentation masks grown from different subsets of the possible seeds. The best segmented image is determined to be the final segmentation result. Experiments show the advantage and robustness of the proposed algorithm on real infrared videos.