Category Archives: Artificial Intelligence

Single color detection using OpenCV

Live and Laugh

After a few days of learning OpenCV, I started to write my first OpenCV program, the requirement is to detect single color and find three largest color objects, it’s a very basic program, but you can know a lot about computer vision if you understand the code. OpenCV provide so many functions, you have to know what you want and what the function does.

This program still run on the Raspberry Pi, frame per second is about 8.5, if you want to find more or less than 3 biggest objects, you can change N. Here is code(You also can get it in my Github) sg_color.c:

Here is makefile:

LIBS= `pkg-config --libs opencv`
CFLAGS= `pkg-config --cflags opencv`
objects= sg_color.o

sg_color: $(objects) 
	gcc $(LIBS)$(CFLAGS) -o sg_color $(objects)

.PHONY: clean
clean:
	rm sg_color $(objects)

Here is processed frame and threshold image:
frame

Threshold

View original post

Advertisements

Visual Intelligence : Human vs Machine

Blog

Ever wondered how visually intelligent our brain is ? or How much has machine vision achieved in mimicking human vision till now ? Lets start by observing a picture

Human Vision (1) Human Vision (1)

Each of these children is observing the world surrounding him/her. They can identify shape and color of various patches in the room. They can also classify objects, the actions of teacher and most importantly ,identify the visually related social behavior of objects in the environment.

By the age of two, our visual cortex becomes so well trained that we can understand any scene without rationalizing its pixel space. This becomes clear from the following example :-

Famous Ponzo Optical Illusion Famous Ponzo’s Optical Illusion

Observe how quickly your brain understands the scene , albeit it falters in guessing that the size of three vehicles are equal. On the other hand, machine vision is still in its infancy. There are algorithms that accurately…

View original post 157 more words

Researchers are using deep learning to predict how we pose. It’s more important than it sounds

Gigaom

A team of New York University researchers that includes Facebook AI Lab Director Yann LeCun recently published a paper explaining how they built a deep learning model capable of predicting the position of human limbs in images. That field of computer vision, called human pose estimation, doesn’t get as much attention as things like facial recognition or object recognition, but it’s actually quite difficult and potentially very important in fields such as human-computer interaction and computer animation.

Computers that can accurately identify the positions of people’s arms, legs, joints and general body alignment could lead to better gesture-based controls for interactive displays, more-accurate markerless (i.e., no sensors stuck to people’s bodies) motion-capture systems, and robots (or other computers) that can infer actions as well as identify objects. Even in situations where it’s difficult or impossible to see or distinguish a part of somebody’s body, or even an entire side, pose-estimation systems should be smart…

View original post 381 more words

Machine Learning, Deep Learning and Scientific Understanding

Simant Dube

Machine learning is super hot in silicon valley these days! It has emerged as a very useful discipline in computer science and statistics. If you have skills in Machine Learning, you are likely to get a nice job. But what exactly is machine learning? With growing popularity of the field, engineers and scientists know the technical answer, but can it be explained to everyone in simple language? What are current trends in machine learning and what could come next?

In this article, we will focus on statistical learning and discuss state-of-the-art, trends and future directions.

Decisions, Decisions, Decisions!

Why is machine learning hot? Well, there are so many decisions to be made everywhere. Suppose you want to predict, recommend, classify or rank something in an automated data driven manner. Then your best bet is to use statistical machine learning.

Netflix wants to recommend movies to you which you may like…

View original post 2,913 more words

When will you be domainted by Artificial Intelligence?

Post All Junk Here

In 1997 IBM challenged the World’s number one chess Grandmaster, Garry Kasparov to a friendly chess match against their newly built Deep Blue chess playing computer.  After seven long matches, IBM Deep Blue defeated Kasparov 2-1.  At the time, Garry Kasparov accused the IBM engineers of cheating, and using human players to assist Deep Blue back stage;  indeed Kasparov’s was a normal human reaction.  But what Garry didn’t realise at the time, he was mearly going through the normal set of human emotions, which billions of other humans will face in the coming Artificial Intelligence Age.

Machines can already run faster, lift heavier, dive deeper, climb higher than any human.  What makes the thinking domain so untouchable and special!?  Indeed, IBM Deep Blue proved that humans can be outsmarted too.

Even though Garry Kasparov will be remembered as a great chess player, he will also be remembered as one of…

View original post 310 more words

neuro-Cell: A cognitive computing System-on-Chip

Injecting intelligence into the dna of the city

This document provides an introduction to the neuro Cell Technology (nCell). nCell represents a  revolutionary extension of conventional multi-processor architecture. This post discusses the problem, design concept, the architecture and programming models, and the implementation.

Problem
Over the last 20 years digital electronics has evolved and improved exponentially. The performance of devices is roughly doubling every 18 months because transistor size and the cost of chips have shrunk at an impressive pace. Unrelenting advances in the transistor density of integrated circuits have resulted in a large number of engineered systems with diversified functional characteristics to meet various demands of the human life, ranging from micro-embedded devices, implantable medical devices, and smart sensors. However, the complexity of system-level design for these increasingly evolved engineered systems is further compounded when interdisciplinary requirements are included, for example, massive integration and interconnection between components and subsystems, feedback and redundancy.

The increasingly shrinking electronic technology…

View original post 1,594 more words

AI: Revelations

Brian D. Buckley

python

For a long time, my AI strategy has been:

  • First, figure out the AI’s knowledge structure – the way knowledge is stored inside its mind. You’d think this would be easy, but the problem of knowledge representation turns out to be nontrivial (much like the Pacific Ocean turns out to be non-dry).
  • Once I know how to represent knowledge, I will begin work on knowledge acquisition, or learning.

To me, this order made sense. A mind must have a framework for storing information before you can help it learn new information.

Right?

Well, for the past week, I’ve tackled the problem from the opposite direction. I’ve pushed aside my 5,000+ lines of old code (for the moment) and started from scratch, building an algorithm that’s focused on learning.

The result is a little program (less than 200 lines long) that reads in a text file and searches for patterns…

View original post 419 more words

Introduction to Artificial Intelligence

Cognitive Introductions

Introduction: What is AI?

My colleague Russell Beale once suggested a useful introductory definition of Artificial Intelligence (AI) for people who know nothing about it: “AI can be defined as the attempt to get real machines to behave like the ones in the movies.”

This may give an inkling of what a lot of AI research involves, but it leaves out important facets of AI, especially its scientific aspects. No short definition adequately captures the variety of research goals and topics covered by AI, so I’ll offer a description rather than a definition.

AI is a relatively new discipline (born in the middle of the 20th century). It is increasingly frequently mentioned in newspapers, magazines, on TV, in films, and in various kinds of computer entertainments, yet it is not widely understood. Some people even foolishly think it has already failed and been abandoned, whereas in fact it is steadily…

View original post 2,263 more words

Future of Artificial Intelligence for Smartphones

technondva

The world is indeed a global village now, and conceivably, it is getting even smaller with the passage of time. The major reasons for the “Shrinking” of this world is of course globalization, and one of the things which contributes the most in the process of globalization is the brisk augmentation of Technology. So, it can be concluded that the growth of technology is the main reason why the world has turned out to be a global village. However, as far as our today’s topic is concerned, we are only going to take into account the growth of Smartphones in the world of technology.

Artificial Intelligence for Smartphones

Overall Growth in Smartphones

The growth of Smartphones has been in constant rapidity ever since the release of iPhone about 5 years ago. It started with the ability to replace the need of a PC for internet usage and is now able to replace even those…

View original post 754 more words

Logic Programming