COMPUTER VISION, MACHINE LEARNING AND DEEP LEARNING

Computer Vision

Computer Vision

The goal of computer vision is to understand the story unfolding in a picture. As humans, this is quite simple. But for computers, the task is extremely difficult. So why bother about computer vision? Well, images are everywhere! Whether it be personal photo albums on your smartphone, public photos on Facebook, or videos on YouTube, we now have more images than ever – and we need methods to analyze, categorize, and quantify the contents of these images. For example, have you recently tagged a photo of yourself or a friend on Facebook lately? How does Facebook seem to “know” where the faces are in an image? Facebook has implemented facial recognition algorithms into their website, meaning that they cannot only find faces in an image, they can also identify whose face it is as well! Facial recognition is an application of computer vision in the real world.

We have first-hand experience in developing Computer Vision Applications.
Machine Learning

Machine Learning

Recently Internet of Things(IoT) is growing rapidly, various applications came out from academia and industry. Machine learning can also help machines, millions of machines, get together to understand what people want from the data made by human beings. Also machine learning plays an essential role in IoT aspect for handle the huge amount of date generated by those machine. Machine learning gives IoT and those machines a brain to think, which is called "embedded intelligence".

We are currently involved in projects which focus on Machine Learning Applications to Internet of Things (IoT).
Deep Learning

Deep Learning

Deep learning is often thought of as a set of algorithms that ‘mimics the brain’. A more accurate description would be an algorithm that ‘learns in layers’. Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts. The obscure world of deep learning algorithms came into public limelight when Google researchers fed 10 million random, unlabeled images from YouTube into their experimental Deep Learning system. They then instructed the system to recognize the basic elements of a picture and how these elements fit together. The system comprising 16,000 CPUs was able to identify images that shared similar characteristics (such as images of Cats). This canonical experiment showed the potential of Deep learning algorithms. Deep learning algorithms apply to many areas including Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition etc Also, we have first hand exerience with H2O ( http://www.h2o.ai/). H2O feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use.