Abstract: Compressive Sensing（CS） uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals.The Hough transform is often used to find lines and other parameterized shapes in images.This paper shows how CS can be used to find parameterized shapes in images，by exploiting sparseness in the Hough transform domain.The utility of the CS-based method is demonstrated for finding lines and circles in noisy images.

Key words: compressive sensing, Hough transform, shape detection, basis pursuit, convex optimization, line detection

摘要： 压缩感知以随机投影的形式利用较少的非传统采样，重构稀疏的或可压缩的信号。Hough变换通常用于检测图像中的直线和其他参数化形状。提出利用Hough变换域的稀疏性，用CS寻找图像中的参数化形状的方法。进行了用基于CS的方法检测噪声图像中的直线和圆的实验。

关键词: 压缩感知, Hough变换, 形状检测, 基追踪, 凸优化, 直线检测