大部分的图像存在遮挡现象,因此只能获得对象的部分轮廓。曲率尺度空间描述子(Curvature scale space descriptor,CSSD)是MPEG-7标准采用的闭合轮廓描述子。本文研究并分析了闭合完整轮廓和部分开轮廓曲线演化过程的不同之处,提出了一...大部分的图像存在遮挡现象,因此只能获得对象的部分轮廓。曲率尺度空间描述子(Curvature scale space descriptor,CSSD)是MPEG-7标准采用的闭合轮廓描述子。本文研究并分析了闭合完整轮廓和部分开轮廓曲线演化过程的不同之处,提出了一种通用曲率尺度空间描述子(GCSSD),不仅继承了CSSD的旋转、尺度、平移不变性及抗噪能力,并较好地描述部分开轮廓的特征。本文也给出相应GCSSD的匹配算法,将其应用于彩色图像花的检索,实验结果表明其性能明显优于CSSD。展开更多
人脸具有丰富的表情变化,而且受光照强度、成像角度和成像时间等诸多因素的影响,这些因素都给人脸自动识别造成很大的困难。针对这些问题,笔者提出了一种局部线性鉴别分析(LLDA:Locally L inearD iscrim inantAnalysis)的非线性鉴别分...人脸具有丰富的表情变化,而且受光照强度、成像角度和成像时间等诸多因素的影响,这些因素都给人脸自动识别造成很大的困难。针对这些问题,笔者提出了一种局部线性鉴别分析(LLDA:Locally L inearD iscrim inantAnalysis)的非线性鉴别分析方法,其根本思想是:全局非线性数据结构可由局部线性和局部结构的线性组合表示。样本的特征矢量通过线性转换构成局部特征子空间,使类间散度最大而类内散度最小。该方法适用于多类非线性鉴别。实验表明,在低维子空间、姿态变化和单视图表示的人脸识别中是很有效的。展开更多
This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to reco...This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to recover the coordinates of 3D points from 2D images by the related coordinate conversion factor (CCF). The images are captured by a monocular camera. Second, the human body is represented by a connected three-stick model. Then the parameters of the body model are recovered by the method of projective geometry using the related CCF. Finally, the gait feature composed of those parameters is defined, and it is proved by experiments that those features can partially avoid the influence of viewing angles between the optical axis of the camera and walking direction of the subject.展开更多
文摘大部分的图像存在遮挡现象,因此只能获得对象的部分轮廓。曲率尺度空间描述子(Curvature scale space descriptor,CSSD)是MPEG-7标准采用的闭合轮廓描述子。本文研究并分析了闭合完整轮廓和部分开轮廓曲线演化过程的不同之处,提出了一种通用曲率尺度空间描述子(GCSSD),不仅继承了CSSD的旋转、尺度、平移不变性及抗噪能力,并较好地描述部分开轮廓的特征。本文也给出相应GCSSD的匹配算法,将其应用于彩色图像花的检索,实验结果表明其性能明显优于CSSD。
文摘人脸具有丰富的表情变化,而且受光照强度、成像角度和成像时间等诸多因素的影响,这些因素都给人脸自动识别造成很大的困难。针对这些问题,笔者提出了一种局部线性鉴别分析(LLDA:Locally L inearD iscrim inantAnalysis)的非线性鉴别分析方法,其根本思想是:全局非线性数据结构可由局部线性和局部结构的线性组合表示。样本的特征矢量通过线性转换构成局部特征子空间,使类间散度最大而类内散度最小。该方法适用于多类非线性鉴别。实验表明,在低维子空间、姿态变化和单视图表示的人脸识别中是很有效的。
基金the National Natural Science Foundation of China (No. 60675024)
文摘This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to recover the coordinates of 3D points from 2D images by the related coordinate conversion factor (CCF). The images are captured by a monocular camera. Second, the human body is represented by a connected three-stick model. Then the parameters of the body model are recovered by the method of projective geometry using the related CCF. Finally, the gait feature composed of those parameters is defined, and it is proved by experiments that those features can partially avoid the influence of viewing angles between the optical axis of the camera and walking direction of the subject.
