![]() ![]() ![]() I want to take advantage of this functionality update to dive into the details of image binarization in a short series of posts. What's up with this? Why were new functions needed? This MATLAB function reduces all objects in the 2-D binary image A to 1-pixel wide curved lines, without changing the essential structure of the image. The toolbox includes two new functions, otsuthresh and adaptthresh, that provide a way to determine the threshold needed to convert a grayscale image into a binary image. The toolbox includes the new function, imbinarize, that converts grayscale images to binary images using global threshold or a locally adaptive threshold. Imbinarize, otsuthresh, and adaptthresh: Threshold images using global and locally adaptive thresholds Now, suddenly, the latest release (R2016a) has introduced an overhaul of binarization. load trees Load the image data I ind2gray (X, map) Convert indexed to grayscale level graythresh (I) Compute an appropriate threshold BW im2bw (I, level) Convert grayscale to binary And here is what the original image and result BW look like: For an RGB image input, just replace ind2gray with rgb2gray in the above code. You can think of this as the most fundamental form of image segmentation: separating pixels into two categories (foreground and background).Īside from the introduction of graythresh in the mid-1990s, this area of the Image Processing Toolbox has stayed quietly unchanged. ![]() With the very first version of the Image Processing Toolbox, released more than 22 years ago, you could convert a gray-scale image to binary using the function im2bw. ![]()
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