This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to me...This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to measure the shape and deformation of the skin at the biceps brachii of a volunteer in vivo during arm wrestling.We observed the banding phenomenon of arm skin strain during muscle contraction and developed a model to evaluate the moment provided by the biceps brachii.According to this model,the strain field of the area of interest on the skin was measured,and the forearm angles most favorable and unfavorable to the work of the biceps brachii were analyzed.This study demonstrates the considerable potential of applying DIC and its extension method to the in vivo measurement of human skin and facilitates the use of the in vivo measurement of skin deformation in various sports in the future.展开更多
Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that res...Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.展开更多
可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。...可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。利用卫星获取的前、后视CCD影像,并在其上添加由计算机生成的高层目标(约300m)的图像,验证了生成的正直影像对立体测绘的可行性,试验的高层目标点的坐标量测中误差为实验影像的0.5像元之内。展开更多
基金This study was supported by the National Natural Science Foun-dation of China(NSFC)(No.11902074).
文摘This study analyzes the function of different muscles during arm wrestling and proposes a method to analyze the optimal forearm angle for professional arm wrestlers.We built a professional arm-wrestling platform to measure the shape and deformation of the skin at the biceps brachii of a volunteer in vivo during arm wrestling.We observed the banding phenomenon of arm skin strain during muscle contraction and developed a model to evaluate the moment provided by the biceps brachii.According to this model,the strain field of the area of interest on the skin was measured,and the forearm angles most favorable and unfavorable to the work of the biceps brachii were analyzed.This study demonstrates the considerable potential of applying DIC and its extension method to the in vivo measurement of human skin and facilitates the use of the in vivo measurement of skin deformation in various sports in the future.
基金National Natural Science Foundation of China(41471288).
文摘Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.
文摘可以展望利用卫星三线阵CCD影像自动采集的DEM、按共线方程将三线阵CCD影像变换为正直摄影像对(Normal case photography)提供用户立体测绘。文中着重讨论了正直摄影像中不在DEM表面上的目标点的位置误差,以及改进的立体测绘数学模型。利用卫星获取的前、后视CCD影像,并在其上添加由计算机生成的高层目标(约300m)的图像,验证了生成的正直影像对立体测绘的可行性,试验的高层目标点的坐标量测中误差为实验影像的0.5像元之内。
