Dr. Davut Hanbay Profile

Dr. Davut Hanbay

at Inonu Univ

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Proceedings Article | 24 December 2013 Paper

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

Nuh Alpaslan, Mehmet Murat Turhan, Davut Hanbay

Proceedings Volume 9067, 90671Y (2013) https://doi.org/10.1117/12.2053138

KEYWORDS: Binary data, Image analysis, Machine vision, Image segmentation, Facial recognition systems, Computer vision technology, Image processing, Analytical research, Feature extraction, 3D image processing

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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.

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