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I'm tried to use both of the methods but it seems like Adaptive threshold seems to be giving a better result. I used 

 cvSmooth( temp, dst,CV_GAUSSIAN,9,9, 0);

on the original image then only i used the threshold.

Is there anything I can tweak with the Otsu method to make the image better like adaptive thresholding? And 1 more thing, there are some unwanted fingerprint residue on the side, any idea how i can dispose them off?

I read from a journal that by comparing the percentage of the white pixels in a self-defined square, I can get the ROI. However this method requires me to have a threshold value which can be found using OTSU method but I'm not too sure about AdaptiveThresholding. 

cvAdaptiveThreshold( temp, dst, 255,CV_ADAPTIVE_THRESH_MEAN_C,CV_THRESH_BINARY,13, 1 );

Result :

originaladaptive

cvThreshold(temp, dst, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);

original otsu

user3396218
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  • use morphological operators to get rid of the noise at the border. http://docs.opencv.org/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.html – Sebastian Schmitz Mar 18 '14 at 09:02

2 Answers2

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To get rid of the unwanted background, you can do a simple masking operation. The Otsu threshold function provides a threshold value that cuts the foreground image from the background. Use that threshold value in order to create a binary mask by iterating through the entire input image, checking if the current pixel value is greater than the threshold, and setting it to 1 if true or 0 if it is false.

Then, you can apply the binary mask to the original image by a simple matrix multiplication operation or a bitwise shift operation to remove the background.

Eagle
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1

Try dividing the image into ROIs and apply otsu individually, then merge them back. Dividing strategy can be static or dynamic depending on the max illumination.

Elebasi
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