A New Method for Cartridge Case Image Mosaic

In the process of cartridge case marks detection, due to the limitations of microscope and the unsmoothed specimen surface, not all information can be obtained from just one image. Therefore, two kinds of images can be first obtained and then the information can be supplemented by image mosaic method which facilitates experts’ analysis and the following computer recognition. This paper proposes a new cartridge case image mosaic method by using image registration and fusion techniques. In the registration stage, the initial matching is obtained by using scale invariant feature transform (SIFT), but some incorrect matches greatly affect the registration accuracy. Therefore, in consideration of the specific characteristics of the cartridge case image, graph transformation matching, angle and scale constraint using adaptive K-means clustering are respectively applied to remove incorrect matches. In order to achieve the complementary advantages, voting mechanism is applied to integrate them; meanwhile, genetic algorithm (GA) is employed to select optimal combined parameters, making it possible to adaptively choose to integrate and registration results are optimized based on different images. After refining, the registration accuracy is further enhanced. In the fusion stage, the stitched image is obtained, and histogram matching is employed to smooth visible seams. The mosaic performance is evaluated using visual inspection and objective performance measurements, and results show the advantages of proposed method compared to conventional method. Keywords—image mosaic; image registration; image fusion; removing of incorrect feature matching; voting mechanism; genetic algorithm