Computer Science ›› 2016, Vol. 43 ›› Issue (10): 304-311.doi: 10.11896/j.issn.1002-137X.2016.10.057

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Object Detection Based on Geometric Evidence Collecting

TANG Fu-yu and WEI Hui   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Artificial objects usually have very stable shape feature,which has persistent and stable properties in geometry and provides evidence for object recognition.Besides,shape feature is more stable and more discriminative than appearance feature,color feature,gray feature,and gradient feature.The difficulty of object recognition based on shape feature is that objects may change in color,lighting,size,position,pose,and background interference.And we are unable to predict all possible conditions.The variety of objects and conditions make object recognition based on geometric features be a very challenging problem.This paper gave a method based on shape templates,which performs geometric evidence selection,collection,and combination discrimination for the edge segments of images,to find out the target object accurately from background,and it is able to point out the semantic attribute for each line segment of the target object.In essence,the method is solving a global optimal combinatorial optimization problem.Although the complexity of the global optimal combinatorial optimization problem seems to be very high,it is no need to define the complex feature vector and there’s no need for a high price training process.It has very good generalization ability,environmental adaptability,and more solid basis of cognitive psychology.The geometric evidence collection process,which is simple and universal,shows a great application prospect for this method.The experimental results prove that the method shows great advantages in response to the changes in the environment,invariant recognition,pinpointing the geometry of objects,search efficiency,calculation,and etc.This attempt contributes to understanding some universal processing during the process of object recognition.

Key words: Template-based method,Object detection,Evidence accumulation reasoning

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