计算机科学 ›› 2014, Vol. 41 ›› Issue (10): 310-316.doi: 10.11896/j.issn.1002-137X.2014.10.065

• 图形图像与模式识别 • 上一篇    下一篇

稀疏二值图像特征提取的数据场方法

吴涛,陈一祥,杨俊杰   

  1. 湛江师范学院信息学院 湛江524048;武汉大学遥感信息工程学院 武汉430079;湛江师范学院信息学院 湛江524048
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受广东省自然科学基金(S2013040014926,S2012010009759),广东高校优秀青年创新人才培养计划(2012LYM_0092),湛江师范学院科学研究项目博士专项(ZL1301),国家973重点基础研究发展计划(2012CB719903)等资助

Data Field-based Feature Extraction Method for Sparse Binary Image

WU Tao,CHEN Yi-xiang and YANG Jun-jie   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对图像特征自动提取问题,以稀疏二值图像为例提出了一种模拟物理学场论机制的数据场方法。该方法首先建立二值图像数据场实现图像特征空间到数据场势值空间的映射关系,搜索每个非零像素的八连通区域;然后计算势值及其主方向角,生成势值矩阵和方向角矩阵;最后通过势值归一化和主方向归一化输出以势值和主方向为基础的特征向量及其对应的可视化曲线。新方法利用数据场解决图像特征提取问题,能兼顾图像灰度空间的局部性和数据场势值空间的全局性。手写数字图像的定性和定量实验表明,该方法特征提取效果较好、性能稳定,具有合理性和有效性。

关键词: 数据场,认知物理学,图像特征,二值图像

Abstract: In order to extract the image feature automatically,a novel data field-based method for sparse binary image was proposed from the point of view of the physics-like field theory.First,the method constructs a map from grayscale space to potential space by producing the data field for a given binary image.Next,it calculates the potential value and the principal direction for each pixel with non-zero value by scanning its 8-connected regions,and then obtains the potential matrix and the direction angle matrix.Finally,it generates the feature vectors and its corresponding visual curve after the normalization of potential value and principal direction.The proposed method solves the issue on image feature extraction using data field,and it can keep a balance between the locality of image grayscale space and the globality of potential space in data field.The quantitative and qualitative experiments with the handwritten digital images indicate that the proposed method yields accurate and robust feature extraction results,and is reasonable and effective.

Key words: Data field,Cognitive physics,Image feature,Binary image

[1] 王永明,王贵锦.图像局部不变性特征与描述 [M].北京:国防工业出版社,2010
[2] 曹健.图像目标的表示与识别 [M].北京:机械工业出版社,2012
[3] Li Jing,Allinson N M.A comprehensive review of current local features for computer vision [J].Neurocomputing,2008,71(10-12):1771-1787
[4] Nixon M S,Liu X U,Direkoglu C,et al.On using physical analogies for feature and shape extraction in computer vision [J].The Computer Journal,2011,54(1):11-25
[5] Liu Heng.Force field convergence map and Log-Gabor filterbased multi-view ear feature extraction [J].Neurocomputing,2011,76(1):2-8
[6] 曹传东,徐贵力,陈欣,等.基于力场转换理论的图像粗大边缘检测方法 [J].航空学报,2011,32(5):891-899
[7] Sun Gen-yun,Liu Qin-huo,Liu Qiang,et al.A novel approach for edge detection based on the theory of universal gravity [J].Pattern Recognition,2007,40(10):2766-2775
[8] Lopez-Molina C,Bustince H,Fernandez J,et al.A gravitational approach to edge detection based on triangular norms [J].Pattern Recognition,2010,43(11):3730-3741
[9] Direkoglu C,Nixon M S.On using an analogy to heat flow forshape extraction [J].Pattern Analysis and Applications,2013,16(2):125-139
[10] Cummings A H,Nixon M S,Carter J N.The image ray transform for structural feature detection [J].Pattern Recognition Letters,2011,32(15):2053-2060
[11] 陈雪松,徐学军.一种二值图像特征提取的新理论 [J].计算机工程与科学,2011,33(6):31-37
[12] Tang Wen-sheng,Jiang Shao-hua,Wang Shu-lin.Gray scale potential:a new feature for sparse image [J].Neurocomputing,2013,116:112-121
[13] 陈雪松,徐学军,朱洪波.基于图像势能理论的目标轮廓特征提取方法 [J].计算机科学,2011,38(6):270-274
[14] 李德毅,杜鹢.不确定性人工智能 [M].北京:国防工业出版社,2005
[15] 淦文燕,赫南,李德毅.一种基于拓扑势的网络社区发现方法 [J].软件学报,2009,20(8):2241-2254
[16] 杨炳儒,高静,宋威.认知物理学在数据挖掘中的应用研究 [J].计算机研究与发展,2006,43(8):1432-1438
[17] 王树良,邹珊珊,操保华,等.利用数据场的表情脸识别方法 [J].武汉大学学报:信息科学版,2010,35(6):738-742
[18] 吴涛,金义富,侯睿,等.不确定性边缘表示与提取的认知物理学方法 [J].物理学报,2013,62(6):171-183
[19] Yann Le-cun,Corinna Cortes,Christopher Burges.The MNIST database of handwritten digits .http://yann.lecun.com/exdb/mnist/

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!