Robust texture features for blurred images using Undecimated Dual-Tree Complex Wavelets
Robust texture features for blurred images using Undecimated Dual-Tree Complex Wavelets
复制标题
DOI：
10.1109/icip.2014.7026152
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
N. Anantrasirichai;J. F. Burn;D. Bull
中科院分区：
文献类型：
--
作者：
N. Anantrasirichai;J. F. Burn;D. Bull
本文提出了一种新的纹理分类描述子。该描述符是旋转不变的和模糊不敏感的，这为遭受失焦内容或涉及快速移动或抖动相机的各种应用提供了很大的好处。我们采用非抽取双树复小波变换（UDT-CWT）[1]来提取纹理特征。由于UDT-CWT充分提供了尺度和子带方向之间的局部空间关系，我们可以直接创建表示小波系数局部相位的图像位平面。我们还丢弃了一些最受模糊影响的最精细的分解级别。生成的码字的直方图被创建并用作纹理分类中的特征。实验结果表明，我们的方法优于现有的方法高达40%的合成模糊和高达30%的自然视频内容，由于相机运动时行走。
This paper presents a new descriptor for texture classification. The descriptor is rotationally invariant and blur insensitive, which provides great benefits for various applications that suffer from out-of-focus content or involve fast moving or shaking cameras. We employ an Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) [1] to extract texture features. As the UDT-CWT fully provides local spatial relationship between scales and subband orientations, we can straightforwardly create bit-planes of the images representing local phases of wavelet coefficients. We also discard some of the finest decomposition levels where are most affected by the blur. A histogram of the resulting code words is created and used as features in texture classification. Experimental results show that our approach outperforms existing methods by up to 40% for synthetic blurs and up to 30% for natural video content due to camera motion when walking.