Fractal Theory And Its Application In Environmental Image Processing Research

Fractal geometry produced in the early 1980 of the 20th century, fractal theory study since Mandelbrot proposed’fractal’the word. Now, it has become a branch of learning that is associated with many areas and an irregular geometric shape as the object of study of science, which to solve the limitations of traditional geometrical science of irregular geometry cannot describe. In fractal theory, fractal can be generated by the iterative function of complex natural environment image, the fractal dimension can effectively measure the roughness and complexity of the object, which has a certain relationship between fractal theory and image.The environment problem is a global problem, with the rapid development of intelligent information, due to the increase of environmental image processing requirements and intelligent protection, irregular and uncertain environment exists in the image, it should be analysis with environmental image, identification, establish the database information which used machine in different environmental image to carry on the corresponding processing. Fractal is one of the most crucial step which to handle of environmental image. The most of environment images are heterogeneous, complexity, information.Difficulty and precision edge detection and identification are required to improve. Fractal can be accurately described. It proves that the fractal theory used in image processing, and has a good processing effect.This article first introduces the background of fractal theory and the development of the situation, and then analysis the two important characteristics of fractal, iterative function and fractal dimension, and according to the present the application of fractal theory in the environmental image, introduced by fractal theory to the environment image segmentation problem. Using fractal theory and gradient method of various operator, such as threshold value method, and Sobel operator, and Candy operator, and blanket algorithm, and more scale box dimension number algorithm for image segmentation compared. Analysis simulation results, summary above two class method processing environment image advantages and disadvantages, which proposed the improved difference box dimension number threshold value of natural image segmentation method. The experimental results show that the improved algorithm in image segmentation accuracy is improved, but still need to improve the speed of image segmentation. Finally, the multifractal spectrum method is applied for recognition of images, using the method of multifractal spectrum feature extraction from image data included in the BP neural networks, training, experiments show that this network can meet the requirements, environmental sample images can be identified.