The basics first: The Mean Shift segmentation is a local homogenization technique that is very useful for damping shading or tonality differences in localized objects. An example is better than many words: Action:replaces each pixel with the mean of the pixels in a range-r neighborhood and whose value is within a distance d. The Mean … Read more
“segmentation” is a partition of an image into several “coherent” parts, but without any attempt at understanding what these parts represent. One of the most famous works (but definitely not the first) is Shi and Malik “Normalized Cuts and Image Segmentation” PAMI 2000. These works attempt to define “coherence” in terms of low-level cues such … Read more
Very nice work on boundary detection. I used to work on similar segmentation problems. Theory: Once you obtained your edge map where e(i,j) indicates the “edge-iness” degree of pixel i,j you would like a segmentation of the image that would respect the edge map as possible. In order to formulate this “respect the edge map” … Read more
When it comes to identifying text in images you better use Stroke Width Transform. Here’s a little result I obtained on your image (the basic transform + connected component w/o filtering): My mex implementation based on code from here #include “mex.h” #include <vector> #include <map> #include <set> #include <algorithm> #include <math.h> using namespace std; #define … Read more
First of all: the function minMaxLoc finds only the global minimum and global maximum for a given input, so it is mostly useless for determining regional minima and/or regional maxima. But your idea is right, extracting markers based on regional minima/maxima for performing a Watershed Transform based on markers is totally fine. Let me try … Read more