为提升传统目标轮廓识别算法的实时性,提出一种基于动态时间规整(dynamic time warping, DTW)的轮廓特征目标识别算法。该算法将质心高度增量特征描述符与DTW相似性度量算法相结合,首先对目标轮廓均匀提取采样点,并对目标图像以及模板...为提升传统目标轮廓识别算法的实时性,提出一种基于动态时间规整(dynamic time warping, DTW)的轮廓特征目标识别算法。该算法将质心高度增量特征描述符与DTW相似性度量算法相结合,首先对目标轮廓均匀提取采样点,并对目标图像以及模板图像轮廓点的质心高度增量特征进行提取,然后使用DTW算法寻找规整路径的方法对目标图像以及模板图像的特征矩阵进行相似性度量,最后定义形状复杂度,同时联合翻转目标的二次匹配得出最终识别结果。实验结果表明,所提出算法在MPEG-7、Kimia99数据集中对待测形状能够在保证识别率优于大多数常见的传统目标识别算法的同时提升目标识别的实时性。展开更多
Human body feature extraction based on 2D images provides an efficient method for many applications, e.g. non-contact body size measurements, constructing 3D human model and recognizing human actions. In this paper a ...Human body feature extraction based on 2D images provides an efficient method for many applications, e.g. non-contact body size measurements, constructing 3D human model and recognizing human actions. In this paper a systematic approach is proposed to detect feature points of human body automatically from its front and side images. Firstly, an efficient approach for silhouette and contour detection is used to represent the contour curves of a human body shape with Freeman’s 8-connected chain codes. The contour curves are considered as a number of segments connected together. Then, a series of feature points on human body are extracted based on the specified rules by measuring the differences between the directions of the segments. In total, 101 feature points with clearly geometric properties (that rather accurately reflect the bump or turning of the contours) are extracted automatically, including 27 points corresponding to the definitions of the landmarks about garment measurements. Finally, the proposed approach was tested on ten human subjects and the entire 101 feature points with specific geography geometrical characteristics were correctly extracted, indicating an effective and robust performance.展开更多
文摘为提升传统目标轮廓识别算法的实时性,提出一种基于动态时间规整(dynamic time warping, DTW)的轮廓特征目标识别算法。该算法将质心高度增量特征描述符与DTW相似性度量算法相结合,首先对目标轮廓均匀提取采样点,并对目标图像以及模板图像轮廓点的质心高度增量特征进行提取,然后使用DTW算法寻找规整路径的方法对目标图像以及模板图像的特征矩阵进行相似性度量,最后定义形状复杂度,同时联合翻转目标的二次匹配得出最终识别结果。实验结果表明,所提出算法在MPEG-7、Kimia99数据集中对待测形状能够在保证识别率优于大多数常见的传统目标识别算法的同时提升目标识别的实时性。
文摘Human body feature extraction based on 2D images provides an efficient method for many applications, e.g. non-contact body size measurements, constructing 3D human model and recognizing human actions. In this paper a systematic approach is proposed to detect feature points of human body automatically from its front and side images. Firstly, an efficient approach for silhouette and contour detection is used to represent the contour curves of a human body shape with Freeman’s 8-connected chain codes. The contour curves are considered as a number of segments connected together. Then, a series of feature points on human body are extracted based on the specified rules by measuring the differences between the directions of the segments. In total, 101 feature points with clearly geometric properties (that rather accurately reflect the bump or turning of the contours) are extracted automatically, including 27 points corresponding to the definitions of the landmarks about garment measurements. Finally, the proposed approach was tested on ten human subjects and the entire 101 feature points with specific geography geometrical characteristics were correctly extracted, indicating an effective and robust performance.
