The vast diversity of morphologies,body size,and lifestyles of snakes represents an important source of information that can be used to derive bio-inspired robots through a biology-push and pull process.An understandi...The vast diversity of morphologies,body size,and lifestyles of snakes represents an important source of information that can be used to derive bio-inspired robots through a biology-push and pull process.An understanding of the detailed kinematics of swimming snakes is a fundamental prerequisite to conceive and design bio-inspired aquatic snake robots.However,only limited information is available on the kinematics of swimming snake.Fast and accurate methods are needed to fill this knowledge gap.In the present paper,three existing methods were compared to test their capacity to characterize the kinematics of swimming snakes.(1)Marker tracking(Deftac),(2)Markerless pose estimation(DeepLabCut),and(3)Motion capture were considered.(4)We also designed and tested an automatic video processing method.All methods provided different albeit complementary data sets;they also involved different technical issues in terms of experimental conditions,snake manipulation,or processing resources.Marker tracking provided accurate data that can be used to calibrate other methods.Motion capture posed technical difficulties but can provide limited 3D data.Markerless pose estimation required deep learning(thus time)but was efficient to extract the data under various experimental conditions.Finally,automatic video processing was particularly efficient to extract a wide range of data useful for both biology and robotics but required a specific experimental setting.展开更多
为了有效增强指纹,提出了一种改进的基于曲率的自适应非接触式指纹图像增强算法.首先,对局部平稳的指纹图像信号进行STFT（short time Fourier transform）分析,确定纹理模式频谱所在的主要区域,对频谱进行根滤波和形态学平滑后,用均值...为了有效增强指纹,提出了一种改进的基于曲率的自适应非接触式指纹图像增强算法.首先,对局部平稳的指纹图像信号进行STFT（short time Fourier transform）分析,确定纹理模式频谱所在的主要区域,对频谱进行根滤波和形态学平滑后,用均值-门限法提取STFT主频谱成分;然后,提出一种角度和径向估计方法,计算指纹图像在频域的方向图和频率图;最后,根据指纹的曲率,构造相应的巴特沃斯陷波带通滤波器和高斯带通滤波器分别对低曲率区域和高曲率区域进行滤波,有效地滤除奇异点区域和非奇异点区域的噪声模式频率并保留纹线模式的主频率.将提出算法和同类算法进行主客观的实验比较,实验结果表明：提出的算法优于现有算法,能有效滤除非接触式指纹的噪声、增强脊线和谷线之间对比度,同时修复并增强正确的脊线结构.展开更多
基金Agence Nationale de la recherche(Grant no.ANR-20-CE02-0010).
文摘The vast diversity of morphologies,body size,and lifestyles of snakes represents an important source of information that can be used to derive bio-inspired robots through a biology-push and pull process.An understanding of the detailed kinematics of swimming snakes is a fundamental prerequisite to conceive and design bio-inspired aquatic snake robots.However,only limited information is available on the kinematics of swimming snake.Fast and accurate methods are needed to fill this knowledge gap.In the present paper,three existing methods were compared to test their capacity to characterize the kinematics of swimming snakes.(1)Marker tracking(Deftac),(2)Markerless pose estimation(DeepLabCut),and(3)Motion capture were considered.(4)We also designed and tested an automatic video processing method.All methods provided different albeit complementary data sets;they also involved different technical issues in terms of experimental conditions,snake manipulation,or processing resources.Marker tracking provided accurate data that can be used to calibrate other methods.Motion capture posed technical difficulties but can provide limited 3D data.Markerless pose estimation required deep learning(thus time)but was efficient to extract the data under various experimental conditions.Finally,automatic video processing was particularly efficient to extract a wide range of data useful for both biology and robotics but required a specific experimental setting.
文摘为了有效增强指纹,提出了一种改进的基于曲率的自适应非接触式指纹图像增强算法.首先,对局部平稳的指纹图像信号进行STFT（short time Fourier transform）分析,确定纹理模式频谱所在的主要区域,对频谱进行根滤波和形态学平滑后,用均值-门限法提取STFT主频谱成分;然后,提出一种角度和径向估计方法,计算指纹图像在频域的方向图和频率图;最后,根据指纹的曲率,构造相应的巴特沃斯陷波带通滤波器和高斯带通滤波器分别对低曲率区域和高曲率区域进行滤波,有效地滤除奇异点区域和非奇异点区域的噪声模式频率并保留纹线模式的主频率.将提出算法和同类算法进行主客观的实验比较,实验结果表明：提出的算法优于现有算法,能有效滤除非接触式指纹的噪声、增强脊线和谷线之间对比度,同时修复并增强正确的脊线结构.
