Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understa...Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.展开更多
An image trust root is a special type of soft trust root for trusted computing. However,image trust root generation is difficult,as it needs a corresponding stable logic feature generation model and algorithm for dyna...An image trust root is a special type of soft trust root for trusted computing. However,image trust root generation is difficult,as it needs a corresponding stable logic feature generation model and algorithm for dynamical and sustained authentication. This paper proposes a basic function of constructing new scale-spaces with deep detecting ability and high stability for image features aimed at image root generation. According to the heat distribution and spreading principle of various kinds of infinitesimal heat sources in the space medium,a multi-embed nonlinear diffusion equation that corresponds to the multi-embed nonlinear scale-space is proposed,a HARRIS-HESSIAN scale-space evaluation operator that aims at the structure acceleration characteristics of a local region and can make use of image pixels' relative spreading movement principle was constructed,then a single-parameter global symmetric proportion(SPGSP) operator was also constructed. An authentication test with 3000 to 5000 cloud entities shows the new scale-space can work well and is stable,when the whole cloud has 5%-50% behavior with un-trusted entities. Consequently,it can be used as the corresponding stable logic feature generation model and algorithm for all kinds of images,and logic relationships among image features for trust roots.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52090081)the State Key Laboratory of Hydro-science and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Geological discontinuity(GD)plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses.Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data.Inspired by recent advances in computer vision,deep learning(DL)models have been widely utilized for image-based fracture identification.The multi-scale characteristics,image resolution and annotation quality of images will cause a scale-space effect(SSE)that makes features indistinguishable from noise,directly affecting the accuracy.However,this effect has not received adequate attention.Herein,we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques.Next,we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Combining these metrics with the scale-space theory,we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition.It is found that augmenting the image's detail capacity does not always yield benefits for vision-based recognition models.In light of these observations,we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation.The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD.It also facilitates the objective understanding,description and analysis of the rock behavior and stability of slopes from the perspective of image data.
基金The national natural science foundation (61672442,61503316,61273290,61373147)Xiamen Scientific Plan Project (2014S0048,3502Z20123037)+1 种基金Fujian Scientific Plan Project (2013HZ00041)Fujian provincial education office A-class project(JA13238)
文摘An image trust root is a special type of soft trust root for trusted computing. However,image trust root generation is difficult,as it needs a corresponding stable logic feature generation model and algorithm for dynamical and sustained authentication. This paper proposes a basic function of constructing new scale-spaces with deep detecting ability and high stability for image features aimed at image root generation. According to the heat distribution and spreading principle of various kinds of infinitesimal heat sources in the space medium,a multi-embed nonlinear diffusion equation that corresponds to the multi-embed nonlinear scale-space is proposed,a HARRIS-HESSIAN scale-space evaluation operator that aims at the structure acceleration characteristics of a local region and can make use of image pixels' relative spreading movement principle was constructed,then a single-parameter global symmetric proportion(SPGSP) operator was also constructed. An authentication test with 3000 to 5000 cloud entities shows the new scale-space can work well and is stable,when the whole cloud has 5%-50% behavior with un-trusted entities. Consequently,it can be used as the corresponding stable logic feature generation model and algorithm for all kinds of images,and logic relationships among image features for trust roots.
