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how can i set the pixel intensity values (I) that fall in the range, 200> I >30 to I = 200.

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dp sb
dp sb 2015 年 7 月 17 日
編集済み: dp sb 2015 年 7 月 24 日
I am dealing with large images

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Image Analyst
Image Analyst 2015 年 7 月 20 日
Try adapthisteq(). Try it yourself first. It will get rid of the dark background and normalize all the writing. try different parameters until you get it correct. Come back with your code if you need help.
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dp sb
dp sb 2015 年 7 月 24 日
編集済み: dp sb 2015 年 7 月 24 日
Here's two images.
The above binary image picked up line which i didnt want to but i could eliminate them by looking for circularities <5 until <50. this particular image has two lines with circularities 60 and 90.
But the problem comes here, have a look at this image,
This image has a small blob which i want my code to pick up coz it's blacked out and has information to read in it. this blob has circularity of 15. so when i try to go <5 i could eliminate lines but also am eliminating blobs like these which i dont want to. i could also go < 50 and which will not elimate a blob like this but the problem is this is a small blob and there are images with large blobs and circularities upto 200. i can't come with an optimum parameter which can give me desired results. Is there an other way with which i could eliminate lines and only look at blobs with shapes like blocks or small circles.
Plz help, Thank You.
Image Analyst
Image Analyst 2015 年 7 月 24 日
You might try bwconvhull() on the binary image before calculating the area and perimeter. This will round out the blobs and make that bottom one have not so high a circularity.

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その他の回答 (1 件)

David Young
David Young 2015 年 7 月 17 日
Img(200 > Img & Img > 30) = 200;
  3 件のコメント
Walter Roberson
Walter Roberson 2015 年 7 月 17 日
That is probably more general than you intended, but your question was very general. What kinda functions to achieve what goal?
Image Analyst
Image Analyst 2015 年 7 月 17 日
Here's some help: http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862, especially the Image Segmentation Tutorial.

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