Camera auto-exposing and auto-focusing for edge-related applications using a particle filter

The use of edge-related applications is important in the field of computer vision. These applications help robots with understanding their surrounding environments; the lane or wall detection system is one of the most popular applications. Numerous studies have recently been conducted for enhancing the capabilities of robotic vision, but they typically lacked the applications that were related to coping with the environmental changes of the scenes. In this paper, we propose a method that integrates a particle filter into the process of tracking the camera's parameters (the exposure and the focus) to find the captured frame with the high edge quality. The relationship between the current sequence of frames and the previous sequence was given no consideration when all the possible parameters were scanned. Our work attempts to find that relationship and to increase the speed of the camera system. The edge results are evaluated with using a line detection algorithm - that is known as the Standard Hough Transform. A test method is applied to analyze the correctness of the line detection results. Furthermore, we propose the entropy of the Sobel gradient method for measuring the image sharpness and its contrast when the exposure and focus of a digital camera are changed. Our experimental results show that our method can be applied in real-time systems because of its low computational requirements.