基于空频域联合闭值分割的轮廓检测方法

传统的边缘检测算子仅在空域上对梯度图像进行阈值分割来计算二值边缘图像,当应用在自然场景图像中时,检测结果中往往含有大量的干扰边缘。为了消除干扰边缘,提高传统边缘算子的轮廓检测性能,提出了基于空频域联合阈值分割的轮廓检测方法:首先对梯度图像进行频域阈值分割消除干扰边缘,然后进行空域阈值分割得到最终的二值边缘图。结合Canny算子,利用自然场景图像对该方法进行了性能评估,结果表明,该方法大大减少了干扰边缘,有效提高了Canny算子在复杂自然场景图像中的轮廓检测性能。

Contour Detection by Thresholding the Gradient Image in Spatial-frequency Domain

Standard edge detectors compute the binary edge maps by thresholding the gradient image only in the spatial domain. When they are applied to the natural images, their results contain a lot of spurious edges because the gradient magnitudes of spurious edges are often stronger than that of object contours. To improve their performance in detection of object contours in natural scenes, we proposed a novel contour detection method by thresholding the gradient image in spatial-frequency domain in this paper. Taking Canny edge detector as an example, we used natural images with associaled subjectively defined desired output contour maps to evaluate the performance of the proposed method. Experimental results show that the proposed method can effectively improve the contour detection performance of the Canny edge delector.