diff --git a/report.md b/report.md index 70b1cb7..ba8af1c 100644 --- a/report.md +++ b/report.md @@ -68,7 +68,7 @@ I used an edge detector algorithm called Canny to preprocess the images which - It basically removes noise with a gausian filter and then finds the intentisty gradians of the image with help of some trigonometry. -I did not implement the algorithm myself, instead I used the [OpenCV implementation](http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html). +I did not implement the algorithm myself, instead I used the often used [OpenCV implementation](http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html). ### 3.1.2 Hough transform @@ -78,7 +78,7 @@ To find the lines I used the [Hough transform](https://en.wikipedia.org/wiki/Hou 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). +I did not implement it myself but used the often used and tested probabilistic [OpenCV implementation](http://docs.opencv.org/modules/imgproc/doc/feature_detection.html?highlight=houghlinesp#houghlinesp). ## 3.2. Line features