Revision history [back]

First, you split the image vertically in the middle and process both sides individually. You then use cv::Houghlines (http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html) to find the horizontal lines that separate the segments. cv::Houghlines will give you a lot of lines, but you can easily filter them by angle and length.

a) First, you split the image vertically in the middle and process both sides individually. You then use cv::Houghlines (http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html) to find the horizontal lines that separate the segments. cv::Houghlines will give you a lot of lines, but you can easily filter them by angle and length.

a) First, you split the image vertically in the middle and process both sides individually. You then use cv::Houghlines (http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html) to find the horizontal lines that separate the segments. cv::Houghlines will give you a lot of lines, but you can easily filter them by angle and length.

b) (again, split vertically first). Binarize the image, e.g. with cv::adaptiveThreshold. Divide the image in horizontal strips with a height of 2-3 pixels. Then count the number of columns in this strip in which there is at least one black pixel. The strips that contain the horizontal lines should have a significant higher number of columns with a black pixel than the other strips. However, finding good parameters for the thresholding could be difficult.