Journal Article

·2016

Impact of artificial dataset enlargement on performance of deformable part models

Bedir Yılmaz YTU , Mehmet Fatih Amasyalı YTU , Muhammet Balcılar YTU , Erkan Uslu YTU , Sırma Yavuz YTU

Abstract

There is a remarkable body of work for increasing the performance of Deformable Part Models (DPM), which is one of the most popular algorithms that are being used for object detection from digital images. The contribution that has been made to the object detection performance of the DPM algorithm via usage of larger datasets that has been created via production of artificial images from original images has been examined in this study. Various artificial dataset enlargement techniques that require no additional effort of labeling or data gathering have been compared on INRIA dataset and positive impact of artificial dataset enlargement has been observed. An increase on object detection performance has been noted both on the original INRIA dataset and its subsets.

Keywords

Computer science Artificial intelligence Object detection Object (grammar) Pattern recognition (psychology) Computer vision Digital image Image (mathematics) Image processing

Subject Areas

Advanced Neural Network Applications ·Computer Vision and Pattern Recognition ·Physical Sciences
3D Surveying and Cultural Heritage ·Geology ·Physical Sciences
Industrial Vision Systems and Defect Detection ·Industrial and Manufacturing Engineering ·Physical Sciences

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