Description: extract dense sift for each image patch, because no salient keypoint detection and rotation normalization, it is very efficient. Platform: |
Size: 2048 |
Author:郭恺 |
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Description: Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。-Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. Aiming at the problem, an approach is presented for rotation invariant medical texture classification based on Gabor wavelet transform. Rotation normalization is achieved by circular shift of the feature elements, so that all images have the same dominant direction. Experimental result shows that Gabor wavelet transform with circular operator of rotation normalization has well precision to classify the medical CT image. Platform: |
Size: 407552 |
Author:li |
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Description: A digital image watermarking scheme must be robust against a
variety of possible attacks. In the proposed approach, a new
rotation and scaling invariant image watermarking scheme is
proposed based on rotation invariant feature and image
normalization. The rotation invariant features are extracted
from the segmented areas and are selected as reference points.
Sub-regions centered at the feature points are used for
watermark embedding and extraction. Image normalization is
applied to the sub-regions to achieve scaling invariance. In the
scheme, first, the image is segmented into a number of
homogeneous regions and the feature points are extracted. Then
the circular regions for watermark embedding or extraction are
defined. Based on the image normalization and orientation
assignment, the rotation, scaling, and translation invariant
regions can be used for watermark embedding and extraction. Platform: |
Size: 1475584 |
Author:prasannakumar |
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Search - rotation normalization