An Iterative Thresholding Algorithm for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation and uncertainty
Image segmentation and uncertainty
Digital image processing
Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A technique for fuzzy document binarization
DocEng '01 Proceedings of the 2001 ACM Symposium on Document engineering
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation for Human Tracking Using Sequential-Image-Based Hierarchical Adaptation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Adaptive Local Thresholding with Fuzzy-Validity-Guided Spatial Partitioning
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A two-step method for preprocessing volume data
Computer Vision and Image Understanding
Variational Image Binarization and its Multi-Scale Realizations
Journal of Mathematical Imaging and Vision
A multistage adaptive thresholding method
Pattern Recognition Letters
A Threshlod Selection Method Based on Multiscale and Graylevel Co-occurrence Matrix Analysis
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Morphological segmentation and classification of underground pipe images
Machine Vision and Applications
A hidden Markov model-based character extraction method
Pattern Recognition
A multi-plane approach for text segmentation of complex document images
Pattern Recognition
A new binarization method for non-uniform illuminated document images
Pattern Recognition
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The thresholding method involves first locating objects in an image by using the intensity gradient, then noting the levels that correspond to the objects in various areas of the image, and finally using these levels as initial guesses at a threshold. This method is capable of thresholding images that have been produced in the context of variable illumination. The thresholding method, called the local intensity gradient (LIG) method, was implemented in C using a Sun4 host running UNIX. The LIG method was compared against iterative selection (IS), gray level histograms (GLHs) and two correlation based algorithms on a dozen sample images under three different illumination effects. Overall, the LIG method, while it takes significantly longer, properly thresholds a larger set of images than does any other method examined over the sample images tested.