Xing Suxia

Image Fusion Method Based on NSCT and Robustness Analysis

Infrared and visible image fusion technology can effectively improve the image contrast and clarity, and enhance the night vision effective. Non-sub sampled contourlet transform (NSCT) in image fusion field has made some achievements. A regional standard deviation-weighted image fusion method based on non-sub sampled Contourlet transform was proposed, and the robustness of the method was analyzed. First, the reiterated infrared and visible images from the same scene were transformed by non-sub sampled contourlet transform, followed the approximate weight averaged, high-frequency detail components in accordance with the weighted of the regional standard deviation proportion, then the fusion image is obtained by inverse non-sub sampled contourlet transform, Finally, the fusion images were compared with the results obtained by Laplace transform, wavelet transform and contourlet transform through a large number of experiments, and the robustness analysis was done through the noise test. The results show that: non-sub sampled contourlet transform can achieve better fusion effect, and high robustness.

Image fusion based on regional energy and standard deviation

Image fusion technology of infrared and visible light could enhance the drivers visual effects in low visibility circumstance, and reduce the probability of the traffic accidents. Focused on the application of wavelet transform in image fusion, a image fusion method was proposed based on a regional energy and standard deviation for the infrared and visual light images. In this method, low-frequency approximation component of wavelet transform were obtained by a regional energy-weighted fusion rule, and three high-frequency details components were composed by utilizing the regional standard deviation-weighted fusion rule. Through the two groups of infrared and visual light image fusion experiments show that: the standard deviation of the image produced by the method increased 41.8% and 67.2% and the average brightness increased from 34.6% to 55.1% compared with simple method, respectively.

Research on key quality of sausage with SVM and hyperspectral imaging full scale features

In this paper a quick and accurate detection method is proposed, which can identify whether the sausage contains excessive acid value, peroxide value and area of colony. By using hyperspectral image measurement and multi-information fusion based on support vector machine (SVM), the sausage content model is established. In order to improve the accuracy of hyperspectral image measurement predicted model and to reduce the measurement turbulence, the image information and the NIR value data that are input as the parameters of the hyperspectral image content model are introduced. The detection models RMSECV and r2 of the research are 0.251 and 0.972. The study concludes that the theory and method can be further extended to the detection of other related meat agricultural products.

The Detection of Nitrite Content in Bacon based on Hyperspectral Technique

As a common food additive, nitrite has been widely used in meet products such as bacon. However, when the content of nitrite in food is overproof, consumers health will be seriously endangered. To solve this problem, this paper takes bacon as the sample to research the feasibility of hyperspectral technique in nitrite fast detecting. The partial least- squares (PLS) is utilized to associate the nitrite content data obtained via hyperspectral technique with the data obtained via GB method, and then the nitrite content calculating model is built. After comparing several models that adopt different spectrum pre-processing methods, the research team find that the first-order derivative plus vector normalization model is the best, whose RMSECV is 0.251 and r2 is 0.972. The result proves that hyperspectral can be effectively used in nitrite content prediction.

Image Fusion Technology Based on Bio-inspired Features

Infrared and visible image fusion, as a powerful tool for the object detection and recognition, has developed with the advent of various imaging modalities. However, resulting images of traditional methods are always difficult to compromise between multimodalities. This paper has solved this problem by a variable-weight fusion rule based on the non-sub sampled contourlet transform (NSCT). The original images are combined in the multiscaled space and the fused image is obtained in the bio-inspired feature frame. Validation experiments on infrared and visible images are for two purposes: the comparison among different fusion rules and the impact of the multiscaled analysis in infrared and visible image fusion. In order to evaluate the proposed method, information entropy (IE), standard deviation (STD), spatial frequency (SF) and mutual information (MI) are adopted to compare with Laplace, wavelet, and NSCT et al. Results are shown that all evaluation value of the proposed method is higher than that of other methods, and it is a better image fusion method.

Study on the Robustness of Hydraulic Turntable Variable Structure Control

This paper establishes the mathematical model of the three degrees freedom hydraulic turntable system, and presents a new design method and procedures of the variable structure control(VSC) system, and a new control law based Lyapunov theory. In this paper, simulation experiments of three degree freedom electrical-hydraulic turntables are conducted, and researches the robustness characteristic of variable structure control in theory. Research and experiments have shown that the variable structure control system is distinguished by its excellent dynamic and steady-state characteristics, and is insensitivity to variation in the plant parameters and to external disturbances.