A Neighbor-finding Algorithm Involving the Application of SNAM in Binary-image Representation
Jie He, Hui Guo, Defa Hu
In view of the low execution efficiency and poor practicability of the existing neighbor-finding method, a fast neighbor-finding algorithm is put forward on the basis of Square Non-symmetry and Anti-packing Model (SNAM) for binary-image. First of all, the improved minor-diagonal scanning way is applied to strengthen SNAM’s adaptability to various textures, thus reducing the total number of nodes after coding; then the storage structures for its sub-patterns are standardized and a grid array is used to recover the spatial-position relationships among sub-patterns, so as to further reduce the complexity of the neighbor-finding algorithm. Experimental result shows that this method’s execution efficiency is significantly higher than that of the classic Linear Quad Tree (LQT)-based neighbor-finding method.