Image Binarizationusing Otsu MethodXu LiangMonday.April 20th. 2009NLPR-PAL GroupCASIAOutline Image Binarization Example Principle Otsu Method ConclusionImage Binarization-Example(1)Document part.bmp,T = 179Image Binarization-Example(2)Coins.bmp, T = 126Image Binarization-Example(3)Cameraman.bmp, T = 88Image BinarizationPAPERDOCUMENT GRAY LEVELIMAGE BINARYIMAGE FEATURE VECTORSCLASSIFIED DIGITSSCANNINGFEATURE EXTRACTIONBINARIZATIONCLASSIFICATIONSteps in a digit recognition system[1]Image Binarization Segmentation for object regions Intensity property sharing Also call Image Thresholding Threshold a gray-level image to binary image A simple but effective tool to separate objects from backgroundT is some global threshold=01) , ( y x gif f(x,y) >= TOtherwiseImage Binarization-Application Document image analysis Extract printed characters, logos, Medical image process Find the illness part in the x-ray image Scene processing Detect a target .Outline Image Binarization Otsu Method Example Principle ConclusionOtsu Method Title A Threshold Selection Method from Gray-Level Histograms Author Nobuyuki Otsu Affiliation Electro-Technical Laboratory, Tokyo University(2007), Tokyo, Japan Publish IEEE Transactions on System, Man, and Cybernetics. SMC-9(1), 1979 Citation(Google Scholar) 3571Otsu Method-Example3 . 184 *) ( , 302 . 0 *) (8 . 67 *) ( , 698 . 0 *) (917 . 0 , 1264 . 3123 , 0 . 103* *2= == == == =K K wK K wKO OB Bqo Otsu Method-Principle Clustering ObjectBackground Make each cluster as tight as possible Hopefully minimize their overlapOtsu Method-Principle-Algorithm(1) Define the within-class variance) ( ) ( ) ( ) (2 2 2T T T TO O B B Withino e o e o + =) (T wB =10) (Tii p=) (2TBo) (T wO==1) (LT ii p=The variance of the pixels in the background (below threshold)) (2TOo=The variance of the pixels in the foreground (above threshold)[0, L-1] the range of intensity levelsA lot of computation!Otsu Method-Principle-Algorithm(2) Easier for the between-class variance) (2TBetweeno = ) (2 2TWithino o =2 2] ) ( )[ ( ] ) ( )[ ( + T T w T T wO O B B) (2TBetweeno =2)] ( ) ( )[ ( ) ( T T T w T wO B O B By simplifying,2o =Combined variance =Combined meanMaximizingOptimal threshold TOtsu Method-Principle-Algorithm(3) Easier using simple recurrence relations) ( ) ( ) 1 ( T p T w T wB B+ = +) ( ) ( ) 1 ( T p T w T wO O = +) 1 () ( ) ( ) () 1 (++= +T wT T p T w TTBB BB) 1 () ( ) ( ) () 1 (+= +T wT T p T w TTOO OOOtsu MethodOtsu method fails in this occasion.170 ) ( , 01 . 0 ) (90 ) ( , 99 . 0 ) (1354697 . 0 , 93163 , 91* *2= == === == =K K wK K wKKO OB Bqo Outline Image Binarization Otsu Method Conclusion Otsu Method Image BinarizationConclusion-Otsu Method Motivation Well-thresholded classes would be separated in gray levels Histogram based clustering Simplicity Effectiveness When the number of pixels in each class are close to each other Drawbacks Unimodality of Object Function may fail When the object and background pixels are extremely unbalancedConclusion-Image Binarization A lot of applications[3] Document analysis Medical image process A lot of binarization methods Histogram-based thresholding Object attribute thresholding Edge matching Connectivity Locally Adaptive thresholding Variance of pixel neighborhoodConclusion-Image Binarization Unsolved issues[3] Color image binarization Degraded document binarization Multilevel thresholding relevant to computer vision Some sophisticated method Random Markov methods Reference [1] Q.D. Trier, A.K. Jain. Goal-Directed Evaluation of Binarization Methods. IEEE Pattern Analysis and Machine Intelligence. 17(12): 1191-1201. 1995 [2] N. Otsu. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979, 9(1): 62-66 [3]Mehmet Sezgin, Bulent Sankur. Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging. 13(1):146-165, 2004