Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with pe...Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.Methods First,this paper establishes a dataset of abnormal morphology for Chinese medicine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting.Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10.Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.展开更多
A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by an...A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.展开更多
In carbon global cycle, the relationship between the terrestrial ecosystem and the atmosphere where there are, among others, gases that contribute to the greenhouse effect, has become object of relevant scientific int...In carbon global cycle, the relationship between the terrestrial ecosystem and the atmosphere where there are, among others, gases that contribute to the greenhouse effect, has become object of relevant scientific interest. The content of organic matter in soil, expressed by its supplies as well as the organic matter degree of stability, are factors that can prevent the soil from acting as a drain and at the same time contribute for it to become a source of those gases. The variations in the way land is used in Brazil are factors responsible for the increase in emission of greenhouse effect gases. Based on these facts, this study was aimed to evaluate the CO2 and CH4 efflux using a gas retention chamber, and to associate these emissions to the organic carbon content in the soil. Two different areas were selected for the study, one in Tijuca Forest National Park, in a forest area, and the other at the Rio de Janeiro Federal Rural University campus. In the latter, the area was stratified in three sub areas according to the vegetation, use and water saturation degree. Samplings were performed during 8 months between 2013 and 2014.展开更多
基金National Key Research and Development Program of China(2022YFC3502302)。
文摘Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost.Methods First,this paper establishes a dataset of abnormal morphology for Chinese medicine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting.Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10.Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.
基金Supported by Fundamental Project of Committee of Science and Technology of Shanghai (No.03DZ14015)
文摘A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
文摘In carbon global cycle, the relationship between the terrestrial ecosystem and the atmosphere where there are, among others, gases that contribute to the greenhouse effect, has become object of relevant scientific interest. The content of organic matter in soil, expressed by its supplies as well as the organic matter degree of stability, are factors that can prevent the soil from acting as a drain and at the same time contribute for it to become a source of those gases. The variations in the way land is used in Brazil are factors responsible for the increase in emission of greenhouse effect gases. Based on these facts, this study was aimed to evaluate the CO2 and CH4 efflux using a gas retention chamber, and to associate these emissions to the organic carbon content in the soil. Two different areas were selected for the study, one in Tijuca Forest National Park, in a forest area, and the other at the Rio de Janeiro Federal Rural University campus. In the latter, the area was stratified in three sub areas according to the vegetation, use and water saturation degree. Samplings were performed during 8 months between 2013 and 2014.
