Escalera Sergio, Fornés Alicia, Pujol Oriol, Lladós Josep, Radeva Petia
Centre de Visió per Computador, Campus Universitat Autònoma de Barcelona, Edifici O, 08193 Barcelona, Spain.
IEEE Trans Syst Man Cybern B Cybern. 2011 Apr;41(2):497-506. doi: 10.1109/TSMCB.2010.2060481. Epub 2010 Aug 19.
In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.
在本文中，我们提出一种圆形模糊形状模型描述符，作为目标识别的一个特殊案例来处理符号检测与分类问题。特征提取通过在相关图结构中捕获重要目标特征的空间排列来执行。来自目标的形状信息在相关图区域之间共享，其中预先设定的模糊程度定义了符号中允许的失真水平，使得该描述符能够容忍不规则变形。此外，该描述符在定义上是旋转不变的。我们在多类符号识别和符号检测领域中验证了所提出描述符的有效性。为了执行符号检测，使用级联分类器来学习描述符。在多类分类的情况下，使用一组嵌入纠错输出码设计中的二元分类器来学习新的特征空间。在四个符号数据集上的结果表明，与现有描述符相比，所提出的描述符有显著改进。特别是，在符号遭受弹性变形的情况下，结果更为显著。
