Pantograph carbon slide is an important device in power supply system of electric locomotive, the pantograph location is greatly significant for the geometric parameter measurement of the pantograph-catenary system. In order to enhance the adaptability of pantograph detection algorithm to the scene, and to reduce the false rate and missing rate of pantograph detection, this paper proposes a novel method based on the pantograph template for fast matching and horizontal edge detection projection in monocular infrared pantograph images. Firstly, the prior knowledge of the position of the pantograph and the catenary is combined with the template matching method to realize the rough location of the pantograph, and then the precise location of the pantograph by horizontal edge detection and horizontal unilateral projection. The experimental results show that this novel adaptive method realizes the non-contact detection and location of the pantograph effectively, and improve the efficiency significantly.
Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.
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