fixed the image display problem

README.md

@@ -2,8 +2,8 @@
 
 Requires [PyTorch](http://pytorch.org), [OpenCV](http://www.opencv.org), Python 2 or 3, NVIDIA GPU + CUDA CuDNN.
 
-![](https://github.com/atapour/styleDepth-Inference/blob/master/imgs/pipeLine.png)
-
+![General Pipeline](https://github.com/atapour/styleDepth-Inference/blob/master/imgs/pipeLine.png)
+General Pipeline of the approach
 ## Method:
 
 _"Monocular depth estimation using learning-based approaches has become relevant and promising in recent years. However, most monocular depth estimators either need to rely on large quantities of ground truth depth data, which is extremely expensive and difficult to obtain or predict disparity as an intermediary step using a secondary supervisory signal, leading to blurring and other artefacts. Training a depth estimation model using pixel-perfect synthetic environment data can resolve most of these issues, but introduces the problem of domain bias. This is the inability
@@ -25,7 +25,7 @@ Produces a depth map output image based on a a monocular color image input.
 
 
 ![](https://github.com/atapour/styleDepth-Inference/blob/master/imgs/sample.png)
-
+Example of the results of the approach
 
 ## Instructions to run the inference code:
 
@@ -42,8 +42,9 @@ The output results are written in the 'results' directory:
 
 ---
 
+
 ## Example:
-[![Video Example](https://github.com/atapour/styleDepth-Inference/blob/master/imgs/thumbnail.png)](https://vimeo.com/260393753 "Video Example - Click to Play")
+[![Video Example](https://github.com/atapour/styleDepth-Inference/blob/master/imgs/thumbnail.jpg)](https://vimeo.com/260393753 "Video Example - Click to Play")
 
 Video Example - click image above to play.
 
@@ -58,15 +59,15 @@ Video Example - click image above to play.
 @InProceedings{abarghouei18monocular,
   author = 		{Atapour-Abarghouei, A. and Breckon, T.P.},
   title = 		{Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation},
-  booktitle = 	{Proc. Computer Vision and Pattern Recognition},
+  booktitle = 		{Proc. Computer Vision and Pattern Recognition},
   pages =		{1-8},
   year = 		{2018},
   month = 		{June},
-  publisher = 	{IEEE}, 
-  keywords = 	{monocular depth, generative adversarial network, GAN, depth map, disparity, depth from single image},
+  publisher = 		{IEEE}, 
+  keywords = 		{monocular depth, generative adversarial network, GAN, depth map, disparity, depth from single image},
   url = 		{http://community.dur.ac.uk/toby.breckon/publications/papers/abarghouei18monocular.pdf},
   OPTdoi = 		{},
-  comment = 	{<a class="demolink" href="https://vimeo.com/260393753">demo</a>},
+  comment = 		{<a class="demolink" href="https://vimeo.com/260393753">demo</a>},
 }
 
 ```