A deformation measurement method based on surface texture information of rocks and its application

Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock surfaces.The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability.Based on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks.The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone.Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks.The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

Automatic image segmentation method for cotton leaves with disease under natural environment

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.