Title :
A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition
Author :
Zhang, Jing ; Wenyin, Liu
Author_Institution :
Dept. of Comput. Sci., Univ. of South Florida, Tampa, FL, USA
Abstract :
A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature spacestatistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.
Keywords :
computer vision; feature extraction; matrix algebra; shape recognition; statistical analysis; computer vision; feature space; histogram matrix; pixel-level statistical structural descriptor; shape measure; shape recognition; Computer science; Computer vision; Data mining; Dynamic programming; Feature extraction; Histograms; Length measurement; Shape measurement; Skeleton; Text analysis; measure; recognition; shape descriptor; statistical structural feature;
Conference_Titel :
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-4500-4
Electronic_ISBN :
1520-5363
DOI :
10.1109/ICDAR.2009.175
