Update Advanced-Lane-Finding-P2.md

Author: prateeksawhney97

Advanced-Lane-Finding-P2.md

@@ -23,6 +23,16 @@
 
 #### 1. Provide an example of a distortion-corrected image.
 
+Some examples of distortion corrected images are :
+
+![output_1](https://user-images.githubusercontent.com/34116562/49129716-d0936f80-f2f6-11e8-9165-fbbdbc7e96ec.png)
+![output_2](https://user-images.githubusercontent.com/34116562/49129717-d1c49c80-f2f6-11e8-993a-03642ccce32a.png)
+![output_3](https://user-images.githubusercontent.com/34116562/49129719-d2f5c980-f2f6-11e8-8875-a3a3445d4d00.png)
+![output_4](https://user-images.githubusercontent.com/34116562/49129720-d4bf8d00-f2f6-11e8-9ed2-e2eb9256bbdf.png)
+![output_5](https://user-images.githubusercontent.com/34116562/49129735-e4d76c80-f2f6-11e8-9a3e-af0ace294a08.png)
+![output_6](https://user-images.githubusercontent.com/34116562/49129738-e6089980-f2f6-11e8-98a4-804e9e632a4a.png)
+
+
 #### 2. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image. Provide an example of a binary image result.
 
 Detecting edges around trees or cars is okay because these lines can be mostly filtered out by applying a mask to the image and essentially cropping out the area outside of the lane lines. It's most important that we reliably detect different colors of lane lines under varying degrees of daylight and shadow. So, that our self driving car does not become blind in extreme daylight hours or under the shadow of a tree.