Determining noise performance of co-occurrence GMuLBP on object detection task

Nuh Alpaslan; Mehmet Murat Turhan; Davut Hanbay

doi:10.1117/12.2053138

24 December 2013 Determining noise performance of co-occurrence GMuLBP on object detection task

Nuh Alpaslan, Mehmet Murat Turhan, Davut Hanbay

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671Y (2013) https://doi.org/10.1117/12.2053138
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Object detection is currently one of the most actively researched areas of computer vision, image processing and analysis. Image co-occurrence has shown significant performance on object detection task because it considers the characteristic of objects and spatial relationship between them simultaneously. CoHOG has achieved great success on different object detection tasks, especially human detection. Whereas, CoHOG is sensitive to noise and it does not consider gradient magnitude which significantly effects the object detection accuracy. To overcome these disadvantages the CoGMuLBP was proposed. CoGMuLBP uses a new statistical orientation assignment method based on uniform LBP instead of using the common gradient orientation. In this study, detection accuracies of CoGMuLBP and CoHOG are calculated on three different datasets with NN classifier. In addition, to evaluate the noise performance of the methods, gaussian noises were added to test images and performances were recalculated. Numerical experiments performed on three different datasets show that 1) CoGMuLBP has higher detection accuracy than CoHOG; 2) using uniform LBP based gradient orientation improves detection accuracy; and 3) CoGMuLBP is more robust to gaussian noise and illumination changes. These results provide the effectiveness of CoGMuLBP for object detection.

Citation Download Citation

Nuh Alpaslan, Mehmet Murat Turhan, and Davut Hanbay "Determining noise performance of co-occurrence GMuLBP on object detection task", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671Y (24 December 2013); https://doi.org/10.1117/12.2053138

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KEYWORDS

Binary data

Image analysis

Facial recognition systems

Image segmentation

Machine vision

3D image processing

Analytical research

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