• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International >Image segmentation using wavelet coefficients and geodesic distance between elliptical distributions for applications in street view
【24h】

Image segmentation using wavelet coefficients and geodesic distance between elliptical distributions for applications in street view

机译：使用小波系数和椭圆分布之间的测地距离对图像进行街景应用

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The geodesic distance on the manifold of multivariate zero-mean Generalized Gaussian Distributions (GGD) has been shown a strong similarity measure for texture classification. Recent works demonstrates that the GGD can be employed for texture identification in the wavelet domain with more accuracy than other measures, like the Kullback Leibler Divergence. The wavelet coefficients of an image can be grouped considering color and spatial dependence. The Laplacian distribution is one of various possible elliptical distributions and is the choice of this work for modeling these coefficients. A street view application of this technique is presented. First, a wavelet decomposition of the image is done. Then, the coefficients of smaller regions (windows) are grouped, and a Laplacian distribution is computed for each coefficients group at each subband. The geodesic distance between these distributions can be computed. This can be viewed as a similarity measure between the regions of the image, and a spectral clustering is employed, using the k-means method for the segmentation. Thus, regions with different textures, as the streets, can be discriminated from each other. The main contribution of this paper is the use of the geodesic distance between GGDs in a segmentation context.
机译：多元零均值广义高斯分布（GGD）流形上的测地距离已显示出用于纹理分类的强大相似性度量。最近的工作表明，GGD可以比其他方法（如Kullback Leibler Divergence）更准确地用于小波域的纹理识别。可以考虑颜色和空间依赖性对图像的小波系数进行分组。拉普拉斯分布是各种可能的椭圆形分布之一，是对这些系数建模的这项工作的选择。介绍了此技术的街景应用。首先，完成图像的小波分解。然后，将较小区域（窗口）的系数分组，并在每个子带为每个系数组计算拉普拉斯分布。可以计算这些分布之间的测地距离。可以将其视为图像区域之间的相似性度量，并使用k均值方法对图像进行光谱聚类。因此，可以将具有不同纹理的区域（例如街道）彼此区分开。本文的主要贡献是在分割上下文中使用GGD之间的测地距离。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号