Research On Image Processing And Control Method Based On Fuzzy Set Theory

Due to the influence of the development of computers,sensors,optics and mathematics,human factors,environmental scenes,etc.,sometimes the image information obtained is not optimal.This is owing to the physical characteristics of the imaging device and the characteristics of the link when the image is transmitted.The image sometimes has poor contrast,and various noises or blur caused by different degrees of damage.Although the image itself is stored in digital form,it is visually ambiguous.Fuzzy techniques are non-linear and knowledge-based.If such defects arise from ambiguity rather than randomness,fuzzy techniques can handle imperfect data.That being said,how to obtain more valuable information from digital images and make the images effectively serve our various industries is an important topic in the field of image processing.The application of digital images has experienced rapid development in the 1960 s with the emergence of the third generation of computers,especially in the medical field and aviation field.Nowadays,computer images have penetrated into various activities of national production,and the images exposed through various media every day will provide us with extremely valuable information and knowledge.The acquisition methods of these important information hidden in images have aroused extensive interest from scholars all over the world.Since the fuzzy set theory was proposed by Professor L.A.Zadeh of American cybernetic expert in 1965,it has shown its powerful ability in solving various problems of ambiguity and uncertainty.Fuzzy set theory expands the classical mathematics theory and forms a more systematic mathematical branch.In the past fifty years,scholars all over the world have conducted various explorations and researches in this field and achieved remarkable results,especially the combination of fuzzy theory and artificial intelligence big data.Its scope of application has involved computers,multimedia,A series of high-tech industries such as automatic control,information collection and communication have contributed to promoting social progress.The main work and results of this dissertation are as follows:(1)The basic characteristics of digital images stored in computers,the definition of edge detection and the physical mechanism of edge generation of each image are studied.An image enhancement algorithm based on membership function correction is proposed.The algorithm first transforms the sample image into domains,and then divides the target image into multiple gray levels according to the principle of maximum fuzzy entropy.According to the characteristics of different gray layers,the modified membership function is used to enhance.It can effectively suppress noise,improve image contrast,and avoid loss of gray information.While realizing the optimization of fuzzy membership function criterion,the edge position and detailed information of the image are kept as much as possible,and the selection of optimization parameters is determined to ensure the enhancement of the image quality and the feasibility and efficiency of the algorithm.From the experimental simulation results,it can be seen that whether it is subjective analysis or objective judgment,the experimental results show that the method in this paper is effective and feasible for image enhancement.(2)Using intuitionistic fuzzy sets(IFS),a new method for calculating divergence and entropy measures,namely the edge detection method of intuitionistic fuzzy divergence measure,is presented,and its effectiveness is experimentally proved.The existing entropy measurement formula is parameterized and integrated,and a new divergence measure and entropy measure depending on the order ? are obtained.According to the method proposed in this paper,multiple test sample images were tested,and IFS was used to achieve the construction and application of the proposed edge detection method.The edges detected by this method are clear and smooth,and the peak signal-to-noise ratio(PSNR)is always equal to or larger than other existing methods,and the effect is good.The final results are compared with the research results of Canny,Sobel,Chaira and others,and it is found that the method in this paper can obtain better results,and the detected edges of each sample image are also closer to the real,and the lines are smoother and clearer.This shows that this method has better robustness and effectiveness than other methods.(3)The fuzzy control and fuzzy rules are studied,and an image-based fuzzy control method is proposed and verified by an example.In many practical control systems,the controlled system needs mutually coordinated subsystems to ensure the normal operation of the system.Non-linear large-scale systems are composed of many interconnected subsystems.Unlike the classic control technology,large-scale systems are not only controlled by a single controller,but also consist of independent subsystems to form a group of corresponding discrete controllers,which greatly increases the difficulty of controller design.In this paper,a fuzzy controller is designed based on the image detection fuzzy control method,and the controller is further applied to a four-wheel moving mechanism with a camera to realize an automatic material stacking system.The experimental results show that the controller has certain practicality.The control system based on image processing has been greatly expanded.