Name
Abstract | ||
|---|---|---|
We present an ensemble recognition method for graphic symbols that could be interfered by intersecting objects from the context. The symbol is first represented as a set of shape points, each of which is described by a shape context pyramid capturing the local shape characteristics of multi-scale regions surrounding the shape point. A Hough forest ensemble classifier is then employed to learn the mapping between the statistical shape feature of individual parts and the category of the whole symbol. For an unknown symbol image, the probabilistic votes on the potential symbol by each of its parts are aggregated by a generalized Hough transform to form the final recognition output for the symbol. The experimental results demonstrate the effectiveness of the proposed method, especially in handling non-segmented intersecting symbols. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | local shape characteristics,graphic symbols,statistical analysis,learning (artificial intelligence),hough forest ensemble classifier,shape context pyramid,multiscale regions,ensemble symbol recognition method,whole symbol category,nonsegmented intersecting symbol handling,image classification,object recognition,individual part statistical shape feature,generalized hough transform,shape points,learning,hough transforms,probability,probabilistic votes,learning artificial intelligence |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Symbol,Computer science,Hough transform,Artificial intelligence,Pyramid,Probabilistic logic,Classifier (linguistics),Contextual image classification,Shape context,Cognitive neuroscience of visual object recognition | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 1 | 0.37 |
References | Authors | |
3 | 3 | |