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

Yimei Kang, Xiang Li

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.