Added Introductory Blog Post

doc/doxygen/tutorials_doxygen/gsoc2016_blog_abhishek.md

@@ -7,4 +7,34 @@ CNN based object localization and recognition for openDetection library  {#gsoc2
 ====
 
   - [Link to Proposal](https://summerofcode.withgoogle.com/serve/6621875723567104/) - [Link to GSoC2016 Project Page](https://docs.google.com/document/d/1-rq4BFcc_SgZLpQrA26_8hQYpSgUnGMcgNHbe7ipYuc/edit?pref=2&pli=1)
-  - \ref gsoc2016_blog_abhishek "Link to blog"    
+<<<<<<< HEAD
+  - \ref gsoc2016_blog_abhishek "Link to blog"    
+=======
+
+
+**About me**
+
+I am a final year engineering student from India, pursuing Electrical and Electronics Engineering at Bits-Pilani Goa Campus. Since my first year at the college, I have been interested in the fields of Computer Vision, Machine Learning and Artificial Intelligence. I have completed my undergraduate thesis at Research and Division Labs of Tata Elxsi Pvt Ltd, on the topic "Scene-understanding and object classification using neural networks for autonomous robot navigation". Over the tenure of engineering in the past three and a half years I have worked over a few projects, 
+
+  - Using Weber Local Descriptors to match forensics sketeches with their image counterparts
+  - Implementing a new course, on biomedical image processing, which is supposed to be added to the college's academic curriculum
+  - Vehicle detection and tracking
+  - Analysing haar-cascades on face detection application
+
+**Project**
+
+The project is revolved over integrating object detection and classification module using Convolutional Neural Networks. The following shows the basic components of the work to be completed during the term of Google Summer Of Code, 2016
+
+  - Implement a way to invoke Caffe open source library from the OpenDetection module with a user-friendly code based way ( this will include a tinge of GUI support for instant access)
+  - Implement open source guidance and codes for state-of-the art object localization problems(hypothesis generation) specifically based on selective-search and convolutional neural network (CNN) approaches. 
+  - Adding a ground-truth annotation tool to the module with a graphical-user-interface support. 
+  - Implementing short, but effective modules like mixed-pooling, recurrent networks to the Convolutional Neural Networks Training dependent on the invoked caffe library. 
+  - Adding context based learning CNNs. 
+  - Adding user-interface to train and test CNN based classifiers and object detectors.
+  - Adding documentation for the above
+
+All the completed and on-going work will be explained in detail here, as the process moves forward.
+
+Happy Coding!!!!
+
+