In the research of identity recognition based on lip motion features, there are limitations for the existing algorithms of lip characteristic parameters extraction. This paper uses the strategy of lip static/dynamic geometric features fusion, designs the lip feature parameter extraction program based on interpolation, and implements the major aspects of processing algorithm of the program. The solution is based on the speaker's key six primitives spelling lip sequence image, firstly generates the lip key point coordinates in the image, then based on Lagrange interpolation obtains function curve coefficient of upper and lower lips' key points , lastly the two curve coefficients are combined to form lip motion feature information of human speaker's some specific sounds; Simulation results show that the extraction of characteristic parameters of the program not only have a high efficiency and availability, but also have the advantages of good storage.

The research of the existing speech recognition is based on speech feature parameter, acco-rding to the shortage of poor anti noise and larger storage capacity, etc. So, curve interpolation has been introduced into speech feature parameter extraction to enhance that. Refer to the speech spectrum dynamic changes and the short-time energy smooth stationary characteristics of speech signal, this paper puts forward and designs an arithmetic of speech feature parameter extraction based on interpolation, constructs the feature parameter extraction and personal identification scheme based on speech, and also designs critical modules algorithm. The detail process of feature parameter extraction: firstly, it creates two-dimensional coordinate for each frame data. Then, according to two-dimensional coordinate, it performs Lagrange cubic interpolation for segmentation the data in a signal frame. Get the interpolation coefficient, average the interpolation coefficient for a signal frame, here the average value is seen as the feature parameter for each frame. Lastly, the each frame’s feature parameter is connected in series to form feature parameter of the speech segment. The arithmetic has been simulated an experiment, in order to confirm the applicability and feasibility. The results illustrates the method has preferable anti noise performance, especially expression and storage for overall speech segment feature parameter show more obvious advantages.

The spatial-based method has become the most widely used method in improving the visibility of images. The visibility improving is mainly to remove the noise in the image, in order to trade off denoising and detail maintaining. A novel adaptive non-local means-based nonlinear fitting method is proposed in this paper. Firstly, according to the smoothness of the intensity around the central pixel, eight kinds of templates with different precision are exploited to approximate the central pixel through a novel adaptive non-local means filter design; the approximate weight coefficients of templates are derived from the approximation credibility. Subsequently, the fractal correction is used to smooth the denoising results. Eventually, the Rockafellar multiplier method is employed to generalize the smooth plane fitting to any geometric surface, thus yielding the optimal fitting of the center pixel approximation. Through a large number of experiments, it is clearly elucidated that compared with the classical spatial iteration-based methods and the recent denoising algorithms, the proposed algorithm is more robust and has better effect on denoising, while keeping more original details during denoising.

Based on each unit of Nong'an County,With ArcGis software platform, the establishment of a part of the total nitrogen in the soil attribute data Nong'an topsoil fertility resources, phosphorus, potassium and organic matter and effective nutrient database,Global Clustering and outlier analysis and hot spot analysis of the autocorrelation method, the county topsoil fertility resources were analyzed and evaluated. The results for farmland protection and optimal layout policy to provide a reference for the theory and methods, but also on the implementation of the agricultural work provides precise basis for decision making.

With the application of IOT technology in maize disease images for monitoring and collecting, timely detection of the types and characteristics of identification of disease has become a hot research in the diagnosis and treatment of diseases and insect pests. In order to improve the recognition accuracy of maize leaf, achieve rapid diagnostic purposes, this paper takes the leaf spot of maize gray leaf spot and image as the research object, use the computer image processing technology is studied on the effective segmentation and recognition of color and shape features. The genetic algorithm was adopted to optimize the selection of maize disease images real-time filtering; 3*3 mode noise suppression of the image selected by value smoothing; then select the HSV component of the color feature extraction of the disease; the maximum between class variance (OTSU) disease shape character segmentation and recognition. The results show that, based on genetic algorithm optimization based on image In HSV and Otsu method can be more accurate segmentation and recognition of the disease of color and shape features, and enhance the real-time and accuracy of the image of maize disease detection and recognition and oriented under the condition of things plant diseases and insect pests of maize and provide technical support.