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Robust superpixels using color and contour features along linear path

2019-03-17 23:00:13
Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis

Abstract

Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decrease of the number of elements to process can drastically reduce the computational burden. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose a framework that jointly enforces all these aspects and provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP). During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the decomposition accuracy and the robustness to noise, we propose to integrate the pixel neighborhood information, while preserving the same computational complexity. SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods. SCALP is also extended for supervoxel decomposition on MRI images.

Abstract (translated)

超像素分解方法广泛应用于计算机视觉和图像处理等领域。通过对均匀像素进行分组,可以提高精度,减少要处理的元素的数量可以大大减少计算负担。对于大多数超像素方法,在1)颜色均匀性、2)图像轮廓的依附性和3)分解的形状规则性之间进行权衡。在本文中,我们提出了一个框架,共同实施所有这些方面,并提供准确和规则的超像素轮廓坚持使用线性路径(头皮)。在分解过程中,我们建议考虑像素和对应的超像素重心之间的线性路径上的颜色特征。轮廓优先还用于在将像素关联到超级像素时防止图像边界的交叉。最后,为了提高分解精度和对噪声的鲁棒性,我们提出在保持相同计算复杂度的同时,对像素邻域信息进行集成。头皮在标准分割数据集上进行了广泛的评估,获得的结果优于最先进的方法。头皮也可以扩展到MRI图像上的超体素分解。

URL

https://arxiv.org/abs/1903.07193

PDF

https://arxiv.org/pdf/1903.07193.pdf


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