Currently fast image mosaic method is less efficient, and most of these methods have low efficiency, poor adaptability, it can not meet the user's requirements. In this paper, a stitching algorithm is presented based on image features. In the method, firstly, the images which are to be spliced using Haar wavelet transform for image de-noising processing; secondly, Haar operator is used to extract image feature points and then uses the template to match the phase correlation method; finally, using a weighted fusion algorithm is proposed for image fusion, the smooth and seamless large completed image will be obtained after the completion of splicing. The experimental results indicate that the effect of the algorithm is better, and has a promotional value.
Because of complex reactions in Basic Oxygen Furnace (BOF) for steelmaking, the main end-point control methods of
steelmaking have insurmountable difficulties. Aiming at these problems, a support vector machine (SVM) method for
forecasting the BOF steelmaking end-point is presented based on flame radiation information. The basis is that the
furnace flame is the performance of the carbon oxygen reaction, because the carbon oxygen reaction is the major reaction
in the steelmaking furnace. The system can acquire spectrum and image data quickly in the steelmaking adverse
environment. The structure of SVM and the multilayer feed-ward neural network are similar, but SVM model could
overcome the inherent defects of the latter. The model is trained and forecasted by using SVM and some appropriate
variables of light and image characteristic information. The model training process follows the structure risk minimum
(SRM) criterion and the design parameter can be adjusted automatically according to the sampled data in the training
process. Experimental results indicate that the prediction precision of the SVM model and the executive time both meet
the requirements of end-point judgment online.
There are some problems in Basic Oxygen Furnace (BOF) steelmaking end-point control technology at present. A new
BOF end-point control model was designed, which was based on the character of carbon oxygen reaction in Basic
Oxygen Furnace steelmaking process. The image capture and transformation system was established by Video for
Windows (VFW) library function, which is a video software development package promoted by Microsoft Corporation.
In this paper, the Radial Basic Function (RBF) neural network model was established by using the real-time acquisition
information. The input parameters can acquire easily online and the output parameter is the end-point time, which can
compare with the actual value conveniently. The experience results show that the predication result is ideal and the
experiment results show the model can work well in the steelmaking adverse environment.