Fire has caused great losses to human beings. However, there are many problems in traditional fire detection methods.Considering the instability and high rate of erroneous recognition with these methods ,a flame recognition algorithm based on LVQ neural network is proposed in this paper. The basic characteristics and some information of the flame are analyzed.Moreover,the LVQ neural network technology is used to achieve fire detection.First, the suspicious targets of the image are extracted by flame color features.After the image morphological processing,the circular value is calculated and the interference regions with larger circular degree values is eliminated.Then,the dynamic features of the flame are extracted from the continuous frame. The area of fire will increase gradually and the image shows a continuous increase in high brightness area. The sharp corners of the flame are characterized by elongate and its number changes irregularly.Finally,the structure of the LVQ neural network, the designed of the input and output layers have been concluded. On this basis, a flame recognition algorithm based on LVQ neural network has been designed and a series of fire image experiments have been conducted.The experiment shows that the recognition accuracy of the algorithm reaches 96%.
A new method is proposed to measure the wavefront aberration of human eyes based on annular radial shearing interference technology in this paper. The feasibility and accuracy of the scheme are verified by implementing simulations of four typical model eyes on ZEMAX. The proposed scheme consists of an illumination light path and an annular radial shearing interference system. The light path focuses the laser with a diameter of about 1 millimeter on the macula of the simulated human eye to form a point. Afterwards, the reflected light passes through the simulated eye refraction system to be incident in the annular radial shearing interference system in the form of wavefront aberration. Then the interference system contracts and expands the beam that carries wavefront aberration of the simulated eye to generate interference within their common area to obtain annular radial shearing interference patterns. Zernike polynomials are used as the basis functions to calculate the Zernike coefficients of the phase difference wavefront after image processing. Accordingly, the trial wavefront is reconstructed by iterative method, and is compared with that of corresponding model eye constructed by ZEMAX to calculate the error and validate the exactness eventually.
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