Auto-Exposure Mehtod Based On Image Segmentations And Fuzzy Logic

Digital imaging technology has made never-ending changes and improvements these years, but the fundamental principle of which does not change a lot, such as the exposure method. As one of the most important imaging elements, exposure effect is the chief mark to evaluate the quality of digital images.Most of the recent digital cameras are equipped with auto-exposure function whose principle is to get the optimal exposure quantity through photometric system and adjust the aperture and shutter automatically. In the ordinary course of events, users can get a good image just by pressing the shutter button. However, it's not so easy for auto-exposure system to get the best exposure effect every time. The reason is the photometric system can not suit complicated photo environment. It's always a big problem to select a suitable exposure value for ordinary users. To solve this problem, the algorithm mentioned in this thesis first improves the existing face detection method, dividing the image into 6 regions according to the position of human beings; secondly, makes statistics based on the relationship between exposure effect and histogram; finally, pulls in fuzzy logic algorithm to give weights to different regions and determine the exposure quantity by image information and experts'experience. The main task of this thesis lies in the following four aspects:(1) Fast, accurate face detection & tracking algorithm. A core classifier based on Haar-like feature and AdaBoost learning algorithm. Before face detection, there are some pretreatments including ray and color compensation. After that, use skin model to validate the face area and ensure face location. Find human's body according to the proportion of the width of head to that of shoulder. Divide the image with this conclusion.(2) To show the image's exposure condition, design a statistics method based on the relationship between image's exposure effect and its histogram. It also can be applied in different areas of the image and takes little time and resource.(3) Introduced the fuzzy logic algorithm. With image information and experts'experience, find input and output, design the fuzzy logic rules base and the fuzzy inference engine. Input the output quantity of fuzzy system into the weights formula...