Study of light environment optimization and control based onimage processing and neural network
基于图像处理和神经网络的光环境优化控制研究

In order to positioning the target area of human body to achieve the optimization of the lighting energy saving, this paper put forward an image processing technique combined the improved Canny operator and mathematical morphology, detected the edge of the indoor image and noise processing, and then to get the binary image containing human as target. It took seven Hu moment invariants of binary human figure template as the input vector of LVQ neural network, which were calculated to get, and trained network to identify the human figure accurately. After that, building the matching model of indoor image characteristics and the personnel quantity and coordinates was well designed to realize the optimization control to indoor lamps according to personnel quantity and coordinates. Simulation and experimental results show that with the combination of the improved Canny operator and LVQ neural network has quite application value on the optimization control of indoor light environment.