| 引用本文: | 向浩鸣,夏晓华,葛兆凯,曹雨松.仿人眼双目图像特征点提取与匹配方法[J].哈尔滨工业大学学报,2024,56(4):92.DOI:10.11918/202305044 |
| XIANG Haoming,XIA Xiaohua,GE Zhaokai,CAO Yusong.Feature point extraction and matching method of humanoid-eye binocular images[J].Journal of Harbin Institute of Technology,2024,56(4):92.DOI:10.11918/202305044 |
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| 摘要: |
| 模仿人眼的视觉特性已成为机器迈向智能感知、智能认知的研究热点和难点。图像边缘蕴含着丰富的信息,因此人眼对场景中物体的边缘更加敏感。为在机器上实现这一视觉特性,提出了一种仿人眼双目图像特征点提取与匹配方法。首先选择边缘特征提取能力突出的SUSAN（small univalue segment assimilating nucleus）算子作为特征检测器；然后改进尺度不变特征变换（scale-invariant feature transform,SIFT）描述子的采样邻域,减少远离特征点的梯度信息因视点和视角差异带来的匹配误差,保留靠近特征点的主要梯度信息；随后对输入图像建立多尺度结构,在不同尺度上计算同一特征的主要梯度信息；最后利用平方根核比较梯度信息的相似性,生成多尺度描述子,增强描述向量的独特性。实验采用多种评价指标分别对提出的多尺度描述子和整体算法进行评估,并与经典的SIFT、SURF（speeded up robust features）、Root-SIFT等算法和先进的BEBLID (boosted efficient binary local image descriptor)、SuperGlue、DFM等算法进行对比。结果表明:提出的多尺度描述子能够提高边缘特征点的匹配准确率,并对光照变化具有更强的适应能力,体现出更好的匹配稳定性；与其他算法相比,本算法具有更高的匹配准确性。 |
| 关键词: 特征点提取 特征点匹配 仿人眼双目图像 多尺度结构 特征描述子 |
| DOI：10.11918/202305044 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61901056)； 中央高校基本科研业务费专项资金(300102251203)；机器人技术与系统国家重点实验室开放基金(SKLRS-2021-KF-10) |
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| Feature point extraction and matching method of humanoid-eye binocular images |
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XIANG Haoming,XIA Xiaohua,GE Zhaokai,CAO Yusong
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(Key Laboratory of Road Construction Technology and Equipment (Changan University), Ministry of Education, Xian 710064, China)
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| Abstract: |
| Imitating the human visual characteristics has become a research hot and challenging research topic for machines to move towards intelligent perception and intelligent cognition. Human eyes are more sensitive to edge of objects in the scene because edge contains abundant information. To realize this visual characteristic in machines, a feature point extraction and matching method of humanoid-eye binocular images is proposed. Firstly, smallest univalue segment assimilating nucleus (SUSAN) operator with outstanding edge feature extraction capability is selected as feature detector. Then, the sampling neighborhood of scale invariant feature transform (SIFT) descriptor is improved to reduce the matching error of gradient information far away from feature points due to viewpoint and view direction differences, and the main gradient information close to feature points is retained. Whereafter, a multi-scale structure is established for the input image, and the main gradient information of the same feature is computed at different scales. Finally, the square root kernel is used to compare the similarity of the gradient information, and the multi-scale descriptor is generated to enhance the uniqueness of the description vector. In the experiment, a variety of evaluation indexes are used to evaluate the proposed multi-scale descriptor and overall algorithm respectively, and compared with the classical SIFT, speeded up robust features (SURF), Root-SIFT and the advanced boosted efficient binary local image descriptor (BEBLID), SuperGlue, DFM algorithms. The results show that the proposed multi-scale descriptor improves the matching accuracy of edge feature points and has stronger adaptability to illumination changes, thereby demonstrating better matching stability. Compared with other algorithms, the proposed algorithm has higher matching accuracy. |
| Key words: feature point extraction feature point matching humanoid-eye binocular images multi-scale structure feature descriptor |
