A Hough transform based line recognition method utilizing both parameter space and image space
A Hough transform based line recognition method utilizing both parameter space and image space
复制标题
DOI：
10.1016/j.patcog.2004.09.003
复制
发表时间：
2005-04
期刊：
影响因子：
--
通讯作者：
Jiqiang Song;Michael R. Lyu
中科院分区：
文献类型：
--
作者：
Jiqiang Song;Michael R. Lyu
霍夫变换（Hough Transform， HT）由于具有全局视野和在噪声或退化环境下的鲁棒性而被认为是一种强大的图像元素提取工具。然而，长期以来，高温成像的应用仅限于小尺寸图像。除了众所周知的累积计算量大之外，对于大尺寸图像，峰值检测和直线验证变得更加耗时。另一个限制是，大多数现有的基于ht的线条识别方法无法检测线条厚度，这对于大尺寸图像（通常是工程图纸）至关重要。我们认为这些限制是由于这些方法只适用于HT参数空间。因此，本文提出了一种新的基于HT的线识别方法，该方法利用了HT参数空间和图像空间。该方法设计了一种基于图像的梯度预测来加速积累，引入了边界记录器来消除线验证中的冗余分析，并开发了一种基于图像的线验证算法来检测线粗细并减少错误检测。它还建议使用像素去除来避免重叠线，而不是严格抑制N×N邻域。我们在不同尺寸的真实图像上进行了速度和检测精度的实验。实验结果表明，该方法在处理大尺寸图像时性能有显著提高。
Hough Transform (HT) is recognized as a powerful tool for graphic element extraction from images due to its global vision and robustness in noisy or degraded environment. However, the application of HT has been limited to small-size images for a long time. Besides the well-known heavy computation in the accumulation, the peak detection and the line verification become much more time-consuming for large-size images. Another limitation is that most existing HT-based line recognition methods are not able to detect line thickness, which is essential to large-size images, usually engineering drawings. We believe these limitations arise from that these methods only work on the HT parameter space. This paper therefore proposes a new HT-based line recognition method, which utilizes both the HT parameter space and the image space. The proposed method devises an image-based gradient prediction to accelerate the accumulation, introduces a boundary recorder to eliminate redundant analyses in the line verification, and develops an image-based line verification algorithm to detect line thickness and reduce false detections as well. It also proposes to use pixel removal to avoid overlapping lines instead of rigidly suppressing the N×N neighborhood. We perform experiments on real images with different sizes in terms of speed and detection accuracy. The experimental results demonstrate the significant performance improvement, especially for large-size images.