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  • 2017 | Volume  | Number 6

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摘 要
目的:现实生活中的彩色图像往往因噪声、色彩不均匀、有较多弱边界等问题的存在导致难以准确分割,本文结合分水岭变换与形态学重构的优势,提出了一种基于同态滤波与形态学分层重构的分水岭分割算法。方法:算法首先提取彩色图像的梯度图,接着对该梯度图采用同态滤波修正梯度图。然后利用形态学开闭重构的方法,对滤波后的梯度图进行分层重构。根据梯度图像的累积分布函数及滤波后的梯度像素直方图的分布信息,给出了梯度分层数的计算公式,同时确定了形态学结构元素尺寸。最后对修正后的梯度图像应用标准分水岭变换实现了图像分割。结果:对不同类型的四幅彩色图像进行分割实验,采用区域一致性与差异性相结合的综合指标对分割结果进行无监督评价。这四幅图像的综合评价指标分别为0.6333、0.6656、0.6293、0.6484,均高于现有分水岭算法的分割结果,分割性能较好。结论:本文提出了一种新的彩色图像分割算法,应用同态滤波保留了图像的弱边界,采用自适应形态学重构,抑制了分水岭变换中过分割。算法的分割结果更加接近人眼对图像的感知,无论从评价指标还是分割性能看,均表现出色。算法对噪声不敏感,鲁棒性较好,可广泛应用于计算机视觉、交通控制、生物医学等方面的目标分割。
关键词

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Abstract
Objective: Color image segmentation is an important image analysis technology, which has important applications in image recognition system. The quality of image segmentation directly affects the effect of image processing. However, because of the noise, the uneven color and the weak boundary, color images in real life are usually difficult to be segmented precisely. In this paper, we proposed a watershed segmentation algorithm based on the homomorphic filtering and morphological hierarchical reconstructions. By combining the advantages of homomorphic filtering, morphological operations and watershed transform, the qualities of the color images’ segmentations are improved. Method: The watershed transform algorithm has been widely used for segmentations of images, because of its advantages such as low computational burden, high accuracy and continuous extraction. However, due to the fact that there are a lot of irregular regions and noises in the image, segmenting the image only relying on a watershed transform algorithm is easy to form a large number of false contours. In order to improve the quality of image segmentation by watershed transform, we get help from the homomorphic filtering and the morphological reconstruction. Firstly, the proposed algorithm used the “sobel” edge operator to compute the gradient of each color component according to the image’s R, G and B values, and the maximum value was selected as the gradient of the color image. After the gradient map of a color image was extract, it was modifies by the homomorphic filtering using Fourier transform. The filtering helps to highlight the foreground contour information on one hand and removes the detail texture noise on the other side. Since there were still some irregular details and noise in the gradient image after filtering, especially at the boundary and background, and the morphological reconstruction operators were able to improve this shortcoming, the modified gradient map was then reconstructed hierarchically by using the operators of open and close morphological reconstructions. According to the cumulative distribution function of the gradient map and the distribution information of the gradient histogram after filtering, the formula for calculating the number of gradient layers was given and the sizes of morphological structure elements, which were decreasing with the increase of the gradient value in each layer, were then calculated adaptively. Finally, the algorithm applies the standard watershed transform to the reconstructed gradient map, and the image segmentation was realized. Result: In order to verify the effectiveness of the algorithm, this paper select four color images of different features to segment in the experiment. Results indicate that the proposed algorithm can effectively restrain the over segmentation and keep the weak boundary, hence the segmentations are more accurate compared with other watershed algorithms. Furthermore, for objectively evaluating the performance of different segmentation methods, this paper quantified the experimental results by unsupervised evaluation of segmentations, which applied the synthesize index combined with regional consistency and diversity indexes. The evaluation index values of our algorithm in the four test images are 0.6333、0.6656、0.6293 and 0.6484 respectively,higher than the results other watershed algorithms, meanwhile the segmentation performances are also better. From the point of view of timeliness, this algorithm takes a little longer time, but it has little difference with the other two algorithms. Conclusion: The watershed transform is a widely used algorithm for image segmentation, but it often leads to over segmentation. Many methods have focused on solving this problem, while ignoring the weak boundaries of images, which are also important in segmentations for application. This paper proposed a new improved watershed algorithm for color images. In the algorithm, the homomorphic filtering is used to preserve the weak boundary of the image, and an adaptive morphological reconstruction is applied to suppress the over segmentation of watershed transform. A balance is found between under segmentations and over segmentations. Segmentation results of the algorithm are closer to the human perception of the images. No matter from the evaluation index or segmentation performance, the proposed algorithm performs better. This algorithm is not sensitive to noise and has good robustness with widely application in computer vision, traffic control, biomedical and other aspects of the target segmentations.
Keywords
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