From 3961153e69c3ef7c7e01dea53969203271c7ce15 Mon Sep 17 00:00:00 2001 From: Jeena Date: Tue, 15 Oct 2013 21:09:47 +0200 Subject: [PATCH] added comma --- report.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/report.md b/report.md index 538a9b0..cdc3a51 100644 --- a/report.md +++ b/report.md @@ -76,7 +76,7 @@ To find the lines I used the [Hough transform](https://en.wikipedia.org/wiki/Hou ![Hough lines](https://jeena.net/images/2013/catdog/hough.png) -It essentially groups edges, which can be imperfect, to object candidates by performing an explicit voting procedure. Detecting straight lines can be done by describing them as y = mx + b where m is the slope of the line and b is the intercept. The line is not represented by descrete points (x1,y1)(x2,y2) but instead as a point(x,y) in the parameter space, which makes detection of lines which are a bit off possible. In practice it is still more complicated, please read the [Wikipedia article](https://en.wikipedia.org/wiki/Hough_transform) about it. +It essentially groups edges, which can be imperfect, to object candidates by performing an explicit voting procedure. Detecting straight lines can be done by describing them as y = mx + b where m is the slope of the line and b is the intercept. The line is not represented by descrete points (x1,y1)(x2,y2) but instead as a point(x,y) in the parameter space, which makes detection of lines, which are a bit off, possible. In practice it is still more complicated, please read the [Wikipedia article](https://en.wikipedia.org/wiki/Hough_transform) about it. Because of lack of time I did not implement it myself but used the probabilistic [OpenCV implementation](http://docs.opencv.org/modules/imgproc/doc/feature_detection.html?highlight=houghlinesp#houghlinesp).