Setting up freeglut and GLTools with Visual Studio 2010

It's good to be writing a tutorial after a long time and there are a number of reasons for that. First of all, I have been really busy with a lot of work and research (well actually I still am!). On the other hand, it is only until recently that I have been struggling with a setup which has little tutorials documented, while there seems to be a lot of beginner developers facing the same problem as I am.

This particular tutorial deals with setting up a Microsoft Visual C++ 2010 Project for use with examples found in the OpenGL Superbible 5th Edition. The book has a section which details the same process for a Visual C++ 2008 project, which is completly different than this tutorial. As always, I have tried to keep everything simple and straightforward so even a person who has no knowledge about these settings can make them work.

Information - How much do we need?

The good thing about doing research is that there is a possibility that you can come across a new technique/method everyday. Recently I have been working with a lot of information from different datasets and trying to extract the useful information content, which can be eventually used to describe the major trend in the dataset.

A major hurdle in this is to quantify that how much information is actually informative. One possible method which is widely used to achieve this is called Singular Value Decomposition (SVD). As this is a widely used technique, the details of this method can be easily found across the web. This blog post will visualize the content information and try to comprehend how much is required for computer vision applications. Here I am using one of the applications of SVD, which is to compress the content of an image. Although there exist better approaches to achieve this, the content used can be quantified easily using SVD.

Below is a sample grayscale image that I have used for this blog post. This is the original image, without any compression using SVD. Notice that there are a lot of regions in the image that look similar.

Programming Inception - Function within a function

This post is about a small but vital part in one of my projects in the past. This specific part of my project dealt with interpolation of quantized silhouette images, using a simple averaging based recursive interpolation.

 

Okay, I know its difficult to understand, but don't just stop reading yet. The most interesting part about this is that it can be related to the concept of "programming inception". You might be wondering this is something very difficult or deep. Don't concentrate too hard like cobb here, from the movie inception. This concept is as simple as it can get, its just that at first its difficult to get.

 

If you are like me and have watched "Inception" several times, then recursion in programming can be thought of having a dream within a dream. The more number of levels of dream you go into, the more specific to details you get. Likewise, a recursive function calls itself with its input and uses the outputs in multiple layers of levels to get deeper and closer to the answer, just like cobb did in the movie to plot an idea in a deep dream level.

The moment you know you are going in the right direction..

I have been working lately on a project, which has taken up a lot of time uptill now.
I admit it is really difficult to do research and I do admit that sometimes it gets you to the point of total frustration. But the reward is truly the most awesome thing.

Earlier this week, I recieved a the result of review process for the conference paper I had submitted in one of the best conference for computer vision. And to my surprise and excitment, it got accepted.

This might be the beginning but I still have a long way to go. However its good to know once in a while that what you are doing is the right direction. Certainly gets your frustration down and motivation high.

I will be posting more about the topic related to this publication. Stay tuned

The Universe is in us!

I have been away from this blog for a while, and there are a number of reasons for that. Mostly I have been really really lazy with lots of work and sleep. The good news is that I am back and I have quite a few things to post about.

While reading this, you might be wondering what this post is about? Well its about surprising similarities between two totally different worlds. The first one involves the microscopic world of DNA. The data I used is specifically cancer mutated DNA I was provided when I went to a GameJam for Cancer Research UK. One of the problems we tried to address in this gamejam was to identify the regions in DNA with cancer mutations. Being a Computer Vision Engineer, I have been really interested on representing the data in a visual way. While I might not have succeeded in creating something useful, however what I found was quite interesting.