旋转不变梯度直方图目标描述方法

谌德荣; 王文斌; 刘丙太; 姜威; 俞达; 宫久路

doi:10.11999/JEIT150546

旋转不变梯度直方图目标描述方法

doi: 10.11999/JEIT150546

1.
(北京理工大学机电工程与控制国家重点实验室北京 100081) ②(北京宇航系统工程研究所北京 100076)

基金项目:

国家部委基金，北京理工大学基础研究基金

计量
- 文章访问数: 1656
- HTML全文浏览量: 214
- PDF下载量: 1077
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-11
- 修回日期: 2015-09-16
- 刊出日期: 2016-01-19

Rotation-invariant Histogram of Oriented Gradients for Target Description

1.
(National Laboratory for Mechatronic and Control, Beijing Institute of Technology, Beijing 100081, China)
2.
(Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China)

Funds:

The Foundations of General Armament Department, Funds of Beijing Institute of Technology

摘要

摘要: 论文为解决旋转目标图像匹配问题，提出旋转不变梯度直方图(RI-HOG)目标描述方法。RI-HOG描述方法首先将目标区域等间隔划分为多个同心圆环并统计每个圆环的梯度直方图(HoG)，各圆环HoG累加的结果作为目标区域的主方向，再将各圆环HoG根据主方向旋转相应角度作主方向归一化处理，最后把旋转后的各圆环HoG按空间顺序连接后即生成RI-HOG。对实际采集图像的仿真结果表明，基于RI-HOG的目标匹配算法在目标旋转任意角度时依然能够准确检测到目标。RI-HOG具有很好的旋转不变性。
- 自动目标识别 /
- 特征描述方法 /
- 梯度直方图 /
- 旋转不变性
Abstract: A rotation-invariant feature descripts method called Rotation Invariant Histogram of Oriented Gradients (RI-HOG) is proposed for automatic target recognition. RI-HOG calculates gradient of image first, then the image window is divided into a set of un-overlapped annular regions, called sells, and the Histogram of Gradient (HoG) is used to calculate a feature vector for each cells. After that the HoG of each circle is accumulated to get the main angle of the target area, and then it is rotated due to the main angle to make a normalization of the main angle. At last, the HoG of each circle after rotating is linked to generate the rotation-invariant target feature vector. Experiment results show that target detection method using RI-HOG can find the target under arbitrary rotations. RI-HOG is a rotation-invariant target feature descriptor.
- Automatic target recognition /
- Feature description method /
- Histogram of Gradient (HoG) /
- Rotation invariant

HTML全文

参考文献(17)

颜雪军, 赵春霞, 袁夏. 一种鲁棒的基于图像对比度的局部特征描述方法[J]. 电子与信息学报, 2014, 36(4): 882-887. doi: 10.3724/SP.J.1146.2013.00846.

YAN Xuejun, ZHAO Chunxia, and YUAN Xia. A robust local feature descriptor based on image contrast[J]. Journal of Electronics Information Technology, 2014, 36(4): 882-887. doi: 10.3724/SP.J.1146.2013.00846.

TUYTELAARS T and MIKOLAJCZYK K. Local invariant feature detectors: a survey[J]. Foundations and Trends? in Computer Graphics and Vision, 2008, 3(3): 177-280.　

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

BAYA H, ESSA A, TUYTELAARS T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.

DALAL N and TRIGGS B. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 2005: 886-893.

FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.

SADEGHI M A and FORSYTH D. 30 Hz object detection with DPM V5[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 65-79.

HARIHARAN B, ZITNICK C L, and DOLL?AR P. Detecting objects using deformation dictionaries[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, 2014: 1995-2002.

AHMED E, SHAKHNAROVICH G, and MAJI S. Knowing a good HOG filter when you see it: efficient selection of filters for detection[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 80-94.

PONCE C and SINGER A. Computing steerable principal components of a large set of images and their rotations[J]. IEEE Transactions on Image Processing, 2011, 20(11): 3051-3062.

SCHMIDT U and ROTH S. Learning rotation-aware features: from invariant priors to equivariant descriptors[C]. IEEE Conference on Computer Vision and, Pattern Recognition, Providence, RJ, USA, 2012: 2050-2057.

TAKACS G, CHANDRASEKHAR V, TSAI S, et al. Unified real-time tracking and recognition with rotation-invariant fast features [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010: 934-941.

TAKACS G, CHANDRASEKHAR V, TSAI S S, et al. Fast computation of rotation-invariant image features by an approximate radial gradient transform[J]. IEEE Transactions on Image Processing, 2013, 22(8): 2970-2982.

谢志江, 吕波, 刘琴, 等. 旋转不变性图像模板匹配快速算法[J]. 吉林大学学报(工学版), 2013, 43(3): 711-717.

XIE Zhijiang, LU Bo, LIU Qin, et al. Rotation-invariant and fast image template matching algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(3): 711-717.

GAUGLITZ S, TURK M, and HLLERER T. Improving keypoint orientation assignment[C]. British Machine Vision Conference, Dundee, Scotland, 2011: 93.1-93.11.

施引文献

资源附件(0)

访问统计