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