目的 超像素分割是计算机视觉领域常用的一项预处理技术，目标是将相邻像素聚集成为具有一定语义的子区域，能够大幅度降低后续处理的计算复杂度，但是对包含强梯度纹理的图像分割效果不佳，为此提出一种具有纹理感知能力的超像素分割方法。方法 提出一种能够区分强梯度噪声和纹理像素的颜色距离，其中利用带方向的1/4圆形窗口均值滤波后的颜色信息，提升包含强梯度噪声和纹理图像的超像素分割性能。利用区间梯度幅值与Sobel梯度幅值相乘得到混合梯度幅值，具有纹理抑制、结构保持以及边缘线条细的优点，能够提升超像素的贴合边缘性能，增强超像素形状规则程度。最后，利用混合梯度的幅值计算具有结构回避能力的综合聚类距离，进一步防止超像素跨越物体的边界，增强超像素的贴边性能。结果 在BSDS500（Berkeley segmentation dataset 500）图像数据集和强纹理马赛克图像等不同类型图像上的测试结果显示，与目前主流的超像素分割方法相比，本文算法在UE （undersegmentation error）、ASA （achievable segmentation accuracy）和CM （compactness measure）等性能指标上分别提高了1.5%、0.2%和4.3%。从视觉效果上看，能够在排除纹理干扰的情况下生成结构边缘贴合程度更好的形状规则超像素。结论 本文算法在包含强梯度纹理图像上的超像素分割性能优于对比方法，在目标识别、目标追踪和显著性检测等易受强梯度干扰的技术领域具有较大应用潜力。

Objective Superpixel segmentation is widely used as a preprocessing step in many computer vision applications. It groups the pixels of an image into homogeneous regions while trying to respect the object boundary. Generally, a good superpixel segmentation method would meet the following three conditions. First, the boundaries of the superpixel should adhere well to the image boundaries. Second, the boundaries of the superpixel should not wing across different objects in the image. Third, superpixels should have similar sizes and regular shapes. In recent years, various superpixel segmentation methods have been proposed; however, most of these state-of-the-art methods only use the pixel information as a clustering feature. Therefore, they can be severely impacted by high-frequency contrast variations and fail to produce equally sized regions having the same texture. To make superpixels robust to contrast variations such as strong gradient texture, we propose a texture-aware superpixel segmentation algorithm that uses patch-level features for clustering purposes. Method The main idea of our algorithm is to calculate the color distance by using a specially designed quarter-circular mean filtering operator. Because the mean filtering has the characteristics of noise suppression and texture smoothing and the rotated quarter-circular window ensures that the mean filtering sampled pixels are located inside the superpixels as much as possible, the quarter-circular mean filtering operator has the capacity to identify the texture pattern. The Sobel gradient has the advantages of fast speed and thin edge, but it is easy to be disturbed by strong gradient texture. The interval gradient is characterized by texture suppression and structure preservation, but its edge is too thick. To overcome their shortcomings while retaining their strengths, we devise a hybrid gradient based on the multiplication of the Sobel gradient and interval gradient, which has the advantages of texture suppression, structure preservation, and edge thinning; therefore, its magnitude can represent the possibility that a pixel belongs to the structure. Finally, an integrated structure-avoiding clustering distance is proposed by looking for the maximum hybrid gradient magnitude along the linear path, which can further enhance the boundary adherence of the superpixels. Result To verify the universality of our algorithm, we test the Berkeley segmentation dataset (BSDS500), which contains 500 images of indoor, outdoor, human, animal, and other scenes with five manually ground truths. To verify the particularity of our algorithm, we test two mosaic images with strong gradient texture. Unfortunately, these two mosaic images do not have ground truth, and they can only be evaluated subjectively by the human eye. All experiments of our algorithm are run on the Windows platform, which requires the mixed programming of MATLAB and Visual studio. Two main parameters need to be set in our algorithm, namely, the mean filtering window radius and the interval gradient operator radius. As both of them aim to capture the regularity of the texture, we set them the same value. Furthermore, the size of the window radius depends on the texture size; the larger the texture size, the larger the window radius required. Normally, we suggest taking the window radius between 3 and 5. We compare our algorithm with other popular superpixel segmentation methods, and all methods use the code with the optimal parameters provided by the authors to obtain their superpixel segmentation results. The superpixel segmentation performance is tested and judged in terms of the boundary recall, undersegmentation error (UE), achievable segmentation accuracy (ASA), and compactness measure (CM). By testing on the BSDS500 image dataset, our algorithm obtains a 1.5% lower UE value, 0.2% higher ASA value, and 4.3% higher CM value. By testing on many mosaic images with strong gradient textures, our algorithm generates superpixels with not only regular shapes but also better boundary adherence. The experimental results show that our algorithm surpasses the state-of-the-art methods in superpixel segmentation performance on BSDS500 and mosaic images. Conclusion To make superpixel segmentation robust to high-frequency contrast variations such as strong gradient texture and noise, we propose a texture-aware superpixel segmentation algorithm, which mainly contributes in the following three aspects. First, we design a quarter-circular mean filtering operator, which is sensitive to the texture pattern. Second, we bring forward a hybrid gradient based on the multiplication of the Sobel gradient and interval gradient, which can distinguish between texture and structure pixels. Third, an integrated structure-avoiding clustering distance is devised based on the hybrid gradient magnitude. It aims to prevent the superpixels from crossing the structure boundary and keep the superpixels with regular size. The experimental results show that our algorithm performs equally well or better than state-of-the-art superpixel segmentation methods in terms of the commonly used evaluation metrics. In the face of strong gradient textures, our method can generate superpixels with regular shape and better boundary adherence. Thus, our superpixel segmentation algorithm has great potential in target recognition, target tracking, and significance detection.