How to improve image segmentation using the watershed?

A description of how to apply the watershed algorithm in OpenCV is here, although it is in Python. The documentation also contains some potentially useful examples. Since you already have a binary image, all that’s left is to apply the Euclidean Distance Transform (EDT) and the watershed function. So instead of Imgproc.grabCut(srcImage, firstMask, rect, bg, fg, iterations, Imgproc.GC_INIT_WITH_RECT), you would have:

Mat dist = new Mat();
Imgproc.distanceTransform(srcImage, dist, Imgproc.DIST_L2, Imgproc.DIST_MASK_3); // use L2 for Euclidean Distance 
Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);
Imgproc.watershed(dist, markers); # apply watershed to resultant image from EDT
Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
markers.convertTo(mark, CvType.CV_8UC1);
Imgproc.threshold(mark, firstMask, 0, 255, Imgproc.THRESH_BINARY + Imgproc.THRESH_OTSU); # threshold results to get binary image

The thresholding step is described here. Also, optionally, before you apply Imgproc.watershed, you may want to apply some morphological operations to the result of EDT i.e; dilation, erosion:

Imgproc.dilate(dist, dist, Mat.ones(3, 3, CvType.CV_8U));

If you’re not familiar with morphological operations when it comes to processing binary images, the OpenCV documentation contains some good, quick examples.

Hope this helps!

Leave a Comment