Particles explained using Gifs!!

Over the past few months, I have been reading, understanding and implementing a number of existing algorithms in Computer Vision domain. Implementing particles and particle based algorithms have really had me excited and almost on the edge of my seat. One may ask what makes particles so interesting?? Let me try to get the concept through.

Particles, just like most existing algorithms in computer science, are inspired by nature. Have you ever seen a beam of sunlight coming through a window and illuminate a bunch of floating particles (impossible in London though I have seen it before)? When you see these tiny particles, you notice that they are suspended in air and that it's very difficult to predict their motion unless you disturb the surrounding air. This simple concept is vital for many computer algorithms that model motion/dynamics of an object.

Particles, along with their randomness, can be simulated inside a computer program. The simplest of such algorithm is called Random Walk, where a particle is modelled with its current position/state alone and a random displacement/jump determines its next position in time. Here I have shown one Random Walk particle:

A Single Random Walk Particle

Different types of Randomness..

Randomness is an integral part of a number of programming algorithms. In fact most of my research until now has been based on a computer algorithm that only uses random numbers for learning a specific task.

A while back I was trying to write some code to define and visualize different Random number generation models. For this task I was playing with a number of different algorithms and visualizing the output on a (kind of a ) probability map. [Not to mention that I use OpenCV for this project as well].

Some of these maps looked pretty cool, so I am just embedding them in this post - also if anyone is interested I have shared the link to this project at the bottom of this post, check that and modify --> make your own random distributions :D

So the input to each of my functions is a Uniform Distribution which looks like this:

A 2D Uniform Distribution

One Image hiding over eight thousand different stories...

Working with large datasets has its own pros and cons. Whatever the implementation or field might be, there is always a need for training a machine learning algorithm to recognize the pattern in that data. We often discuss this "Pattern" in many different instants and a big chunk of literature addresses this recognition problem. However it is often not considered important to get to know how this pattern looks like? why is it even called "Pattern" in the first place??

Interestingly the answer lies in the above image which shows a collection of 8000 different samples, arranged in columns. Here the first thing to notice is that there actually is a repeating pattern in the data. This is the exact pattern which we are trying to learn. It may not make sense when looking at it, however with correct label representation, each sample can be used to build a model which is able to identify each class with high accuracy.

Mind == Blown!

 So sometime back I saw this video presentation of a new and, what I like to call it, novel method for extracting 3D structures from a single image. Part of the reason why this blows my mind, is that this approach is well defined for a specific scenario and it utilizes the best of both human brain and computer's processing power.

We have a great sense of depth perception of objects. Our brains are well trained to construct an object's three dimensional model, by just looking at pictures. This, however, is a trivial and a highly challenging task for computer algorithms. On the other hand, computers are capable of computing and interpolating data at a much faster rate than humans, given that the task is simple and fairly straightforward.

Computer Vision is everywhere...

As most of the android developers, I am a big fan of google nexus tablets and smartphones. Have been using a google nexus 4 for a while now and I am impressed by all kinds of cool stuff you can do with it. A number of cool applications are based on different computer vision techniques. In this post I will be discussing these applications.

 

 

To list just a few obvious ones, the android based smartphones have face recognition based unlocking, camera app which can pick up faces, creating panoramas, editing photos and using readings from a number of inertial sensors to stitch multiple picture into one 3D picture called Photo Sphere.

 

Future of Music == Gesture + AI + Singing

While doing some research, I found this talk in which a musician talks about how she was able to use different gestures to compose a song. The actual talk can be seen below:

What is the first thing that comes to your mind after watching this? yes, it is amazing to see such a performance for a music fanatic. But for me it is even more interesting to see the different aspects of data fusion involved. By looking at her performance, there are a number of things that comes to my mind:

1. Hand gestures recognition using data gloves.
2. Body posture recognition using Kinect Sensor.
3. Localization of the person on stage using Kinect Sensor.

You might have noticed, that there are different hand gestures which are used to start the editing or instrument playing sequence. While the hand gestures are used to play specific notes as well, body posture specifies the different after effects/post processing. Similarly the location of the singer is used to relate it to different music effects.

This really shows the potential of natural interaction technology, and what might be achieved if new ideas are integrated into these natural interaction methods.

Reference:
http://www.kinecthacks.com/imogen-heap-talks-ableton-controlling-gloves/

How to train your dragon?

toothless

No! this post is not about the dragon from the animated movie (although it's one of my favourite). However this picture explains almost everything there is about the topic. For those of you who have seen this movie, computer vision machines can be thought to be like the dragon which can not fly. You have to train it about every single incident and how it should react to each one of them in order to fly.

Yup! that's right, this post is about training. Training a computer!