An improved Canny algorithm based on adaptive 2D-Otsu and Newton Iterative

As traditional Canny operator is manually determined in the threshold for edge detection, we presented an improved Canny algorithm based on adaptive two-dimensional Otsu and Newton Iterative. The algorithm bases on gray distribution characteristics of underwater suspended particles, it uses inter-class variance (Otsu) adaptive algorithm to determine the optimal threshold to connect and trace edge. To reduce computational complexity and traversal cycle, improve real-time capability in Otsu, we employ the Newton iteration method to quickly converge to the optimal high and low threshold of Canny Algorithm. The experimental results show that it can solve the contradiction between the noise filtering and edge maintenance. Therefore, it has strong robustness and adaptability.