基于多尺度特征融合的边界检测算法

doi:10.13229/j.cnki.jdxbgxb20180411

摘要/Abstract

摘要： 针对以往边界检测算法提取的噪声边界过多,导致最终检测的效果提高不多的问题,本文提出了一种基于多尺度特征融合的边界检测算法。在单一图像尺度下,该算法利用图像抠图领域的Trimap和抠图算法生成图像中物体层次特征,并结合提取多尺度的像素层次特征得到边界信息。为了进一步减少过多的噪声边界,该算法提取多尺度图像下的边界信息,并通过融合这些边界信息来过滤噪声边界。在美国加州伯克利大学的BSDS500数据集上进行实验,实验结果表明该算法比单一图像尺度下的边界检测算法具有更高的准确率。

关键词: 计算机应用, 边界检测, 多尺度, 图像抠图, 图像处理, 计算机视觉

Abstract: Prior contour detectors based on single scale input images usually extract multi-scale low-level cues to increase the detection accuracy, however, this kind of methods do not improve accuracy much due to the extracted noisy boundaries. The reason behind this is the lack of object-level features. To address this problem, this paper proposes a boundary fusion method which is capable of combining low-level and object-level cues on multiple scales. For a single scale image, at the beginning, we use Trimaps and an image matting method to generate object-level features, then we combine the object-level features with extracted low-level cues. For multi-scale images, we fuse the boundary maps extracted from each single scale image to reduce noisy boundaries. Experiments show that our method outperforms the single-scale boundary detector on the BSDS500 dataset.

Key words: computer application, boundary detection, multi-scale, image matting, image processing, computer vision

中图分类号:

TP391.4

车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.

CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.

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