TY - GEN
T1 - Neural image thresholding with SIFT-Controlled gabor features
AU - Othman, Ahmed A.
AU - Tizhoosh, Hamid R.
PY - 2011
Y1 - 2011
N2 - Image thresholding is a very important phase in the image analysis process. In all traditional segmentation schemes, statically calculated thresholds or initial points are used to binarize images. Because of the differences in images characteristics, these techniques may generate high segmentation accuracy for some images and low accuracy for other images. Intelligent segmentation by "dynamic "determination of thresholds based on image properties may be a more robust solution. In this paper, we use the Gabor filter to generate features from regions of interest (ROIs) detected by the the SIFT technique (Scale-Invariant Feature Transform). These features are used to train a neural network for the task of image thresholding. The average of segmentation accuracies for a set of test images is calculated by comparing every segmented image with its gold standard image marked by human experts.
AB - Image thresholding is a very important phase in the image analysis process. In all traditional segmentation schemes, statically calculated thresholds or initial points are used to binarize images. Because of the differences in images characteristics, these techniques may generate high segmentation accuracy for some images and low accuracy for other images. Intelligent segmentation by "dynamic "determination of thresholds based on image properties may be a more robust solution. In this paper, we use the Gabor filter to generate features from regions of interest (ROIs) detected by the the SIFT technique (Scale-Invariant Feature Transform). These features are used to train a neural network for the task of image thresholding. The average of segmentation accuracies for a set of test images is calculated by comparing every segmented image with its gold standard image marked by human experts.
UR - http://www.scopus.com/inward/record.url?scp=80054750839&partnerID=8YFLogxK
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U2 - 10.1109/IJCNN.2011.6033488
DO - 10.1109/IJCNN.2011.6033488
M3 - Conference contribution
AN - SCOPUS:80054750839
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2106
EP - 2112
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
Y2 - 31 July 2011 through 5 August 2011
ER -