融合深度信息的BRISK改进算法

doi:10.11772/j.issn.1001-9081.2015.08.2285

计算机应用 ›› 2015, Vol. 35 ›› Issue (8): 2285-2290.DOI: 10.11772/j.issn.1001-9081.2015.08.2285

融合深度信息的BRISK改进算法

张恒, 刘大勇, 刘艳丽, 聂晨曦

华东交通大学信息工程学院, 南昌 330013

收稿日期:2015-01-19 修回日期:2015-03-26 出版日期:2015-08-10 发布日期:2015-08-14
通讯作者: 张恒(1979-),男,湖北汉川人,副教授,博士,主要研究方向:移动机器人导航、机器视觉、移动传感器网络,hbzhangheng@126.com
作者简介:刘大勇(1987-),男,江西信丰人,硕士研究生,主要研究方向:移动机器人导航、机器视觉; 刘艳丽(1979-),女,湖北汉川人,副教授,博士,主要研究方向:移动机器人导航、机器视觉; 聂晨曦(1993-),男,江西南昌人,主要研究方向:移动机器人环境认知。
基金资助:
国家自然科学基金资助项目(61165007);江西省青年科学基金资助项目(20132BAB211036);江西省教育厅科技项目(GJJ14367);江西省普通本科高校中青年教师发展计划访问学者项目。

Improved binary robust invariant scalable keypoints algorithm fusing depth information

ZHANG Heng, LIU Dayong, LIU Yanli, NIE Chenxi

School of Information Engineering, East China Jiaotong University, Nanchang Jiangxi 330013, China

Received:2015-01-19 Revised:2015-03-26 Online:2015-08-10 Published:2015-08-14

摘要/Abstract

摘要：

为了有效地利用RGB-D图像的深度信息,提高BRISK算法的尺度不变性和旋转不变性,提出一种融合深度信息的BRISK改进算法。首先,采用FAST算法提取特征点,并计算每个特征点的Harris角点响应值;然后,将整个图像划分为大小相同的网格,每个网格保留Harris角点响应值最大的特征点;其次,根据图像的深度信息直接计算特征点的尺度因子;最后,计算以特征点为中心的圆的灰度矩心,通过灰度矩心和特征点的位置偏差确定特征点主方向。从尺度不变性和旋转不变性两方面对几种算法进行了对比实验分析。实验结果表明,相比BRISK算法,改进后的算法在图像尺度变化时正确匹配特征点数提高了90%以上,在图像旋转时正确匹配特征点数提高了至少70%。

关键词: BRISK算法, 深度信息, 尺度因子, 尺度不变性, 旋转不变性

Abstract:

To effectively utilize the depth information from RGB-D (Red Green Blue and Depth) images and enhance the scale invariance and rotation invariance of BRISK (Binary Robust Invariant Scalable Keypoints) algorithm, an improved BRISK algorithm combined with depth information was proposed. Firstly, the keypoints were detected by the FAST (Features from Accelerated Segment Test) algorithm and their Harris corner response values were computed. Then, the entire image was divided into the same size grids, and the keypoint with the maximum Harris corner response value was reserved by each grid. Next, the scale factor of the keypoint was directly computed with the depth information of the image. Finally, the intensity centroid of the circle centered on the keypoint was calculated, and the orientation of keypoint was computed by the offset from its intensity centroid. The comparison experiment analysis of several algorithms on the scale invariance and rotation invariance was performed. The experimental results show that, compared with the BRISK algorithm, the number of correctly matched keypoints of the improved algorithm improves by more than 90% when the image's scale is changed and raises by at least 70% when the image is rotated.

Key words: BRISK (Binary Robust Invariant Scalable Keypoints) algorithm, depth information, scale factor, scale invariance, rotation invariance

中图分类号:

TP391.413

张恒, 刘大勇, 刘艳丽, 聂晨曦. 融合深度信息的BRISK改进算法[J]. 计算机应用, 2015, 35(8): 2285-2290.

ZHANG Heng, LIU Dayong, LIU Yanli, NIE Chenxi. Improved binary robust invariant scalable keypoints algorithm fusing depth information[J]. Journal of Computer Applications, 2015, 35(8): 2285-2290.

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融合深度信息的BRISK改进算法

Improved binary robust invariant scalable keypoints algorithm fusing depth information

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