I use Python, but the main idea is the same. If you directly do cvtColor: bgr -> gray for img2, then you must fail. Because the gray becames difficulty to distinguish the regions: Related answers: How to detect colored patches in an image using OpenCV? Edge detection on colored background using OpenCV OpenCV C++/Obj-C: Detecting … Read more
Change this line cvtColor( image, gray_image, CV_RGB2GRAY ); to std::vector<cv::Mat> channels; cv::Mat hsv; cv::cvtColor( image, hsv, CV_RGB2HSV ); cv::split(hsv, channels); gray_image = channels[0]; The problem seems to be that your hand in gray scale is very close to the gray background. I have applied Canny on the hue (color) because the skin color should be … Read more
MikeE‘s answer is quite good: using dilation and erosion morphological operations can help a lot in this context. I want to suggest a little improvement, taking advantage of the specific structure of the image at hand. Instead of using dilation/erosion with a general kernel, I suggest using a horizontal kernel that will connect the endpoints … Read more
Color information is often handled by conversion to HSV color space which handles “color” directly instead of dividing color into R/G/B components which makes it easier to handle same colors with different brightness etc. if you convert your image to HSV you’ll get this: cv::Mat hsv; cv::cvtColor(input,hsv,CV_BGR2HSV); std::vector<cv::Mat> channels; cv::split(hsv, channels); cv::Mat H = channels[0]; … Read more
Complete solution for arbitrary Sobel kernel sizes and angles tl;dr: skip down to section ‘Examples’ To add another solution, expanding on this document (it’s not particularly high quality, but it shows some usable graphics and matrices starting at the bottom of page 2). Goal What we’re trying to do is estimate the local gradient of … Read more
I have played a bit with your input Normalization of lighting + dynamic range normalization helps a bit to obtain much better results but still far away from needed one. I would like to try sharpening of partial derivations to boost the letters from background and treshold out small bumps before integrate back and recolor … 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
This is a big subject. You might find Depixelizing Pixel Art1 by Johannes Kopf & Dani Lischinski useful: it’s readable, recent, includes a summary of previous work, and explains their approach in detail. See also slides covering similar background and video(!). Here are some screenshots from the document of ‘nearest neighbor’ vs. ‘their technique’.
OpenCV can help you find edges using Canny Edge Detector You can refer for the implementation in this link OpenCV’s Canny Edge Detection in C++ Furthermore, I would like you to first do a bit of research on your own before actually asking for questions here.