In the field of 3D model matching and retrieval,an effective method for feature extraction is spherical harmonic or its mutations,and is acted on the spherical images.But the obtainment of spherical images from 3D mod...In the field of 3D model matching and retrieval,an effective method for feature extraction is spherical harmonic or its mutations,and is acted on the spherical images.But the obtainment of spherical images from 3D models is very time-consuming,which greatly restrains the responding speed of such systems.In this paper, we propose a quantitative evaluation of the whole process and give a detailed two-sided analysis based on the comparative size between pixels and voxels.The experiments show that the resultant optimized parameters are fit for the practical application and exhibit a satisfactory performance.展开更多
基金the National Natural Science Foundation of China(No.60903111)
文摘In the field of 3D model matching and retrieval,an effective method for feature extraction is spherical harmonic or its mutations,and is acted on the spherical images.But the obtainment of spherical images from 3D models is very time-consuming,which greatly restrains the responding speed of such systems.In this paper, we propose a quantitative evaluation of the whole process and give a detailed two-sided analysis based on the comparative size between pixels and voxels.The experiments show that the resultant optimized parameters are fit for the practical application and exhibit a satisfactory performance.
文摘针对高光谱图像全色锐化中的光谱失真和纹理细节提升不足问题,结合交叉皮层神经网络模型(intersecting cortical model,ICM),提出一种自适应高光谱图像全色锐化算法。该算法采用ICM分割,先将高光谱图像与空间分辨率较为接近的多光谱图像进行匹配融合,再将结果与高分辨率的全色图像融合,以获得同时具有全色图像的高空间分辨率和高光谱图像的光谱分辨率融合结果。同时,在锐化融合中采用灰狼优化算法(grey wolf optimizer,GWO)自适应优化ICM模型参数,生成最优非规则分割区域,为高光谱图像提供更精准全面的细节和光谱信息。采用2组资源一号02D卫星高光谱数据集进行实验验证,结果表明,提出的新的锐化融合算法在空间细节和光谱信息评价指标上均表现最优,验证了该算法有效性。
