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A Novel Tiny Object Recognition Algorithm Based on Unit Statistical Curvature Feature

  • Yimei KangEmail author
  • Xiang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9909)

Abstract

To recognize tiny objects whose sizes are in the range of 15\(\times \)15 to 40\(\times \)40 pixels, a novel image feature descriptor, unit statistical curvature feature (USCF), is proposed based on the statistics of unit curvature distribution. USCF can represent the local general invariant features of the image texture. Due to the curvature features are independent of image sizes, USCF algorithm had high recognition rate for object images in any size including tiny object images. USCF is invariant to rotation and linear illumination variation, and is partially invariant to viewpoint variation. Experimental results showed that the recognition rate of USCF algorithm was the highest for tiny object recognition compared to other nine typical object recognition algorithms under complex test conditions with simultaneous rotation, illumination, viewpoint variation and background interference.

Keywords

Object recognition Tiny object Feature descriptor Unit Statistical Curvature Feature 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of SoftwareBeihang UniversityBeijingChina

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