Automatic Linguistic Indexing of Pictures

This application demonstrates how computers may be used in intelligent annotation of the audio, video or image media data content.

In this demo experiment we annotated the simple natural image categories. There are 5 ANN classifiers in the project corresponding to:

  • Pictures that might contain animals
  • Pictures that might contain flowers
  • Pictures that might contain landscapes
  • Pictures that might contain sunsets
  • Others pictures that do not contain the above categories or simply unknown image type

 

animals

animals

flowers

flowers

landscapes

sunsets

unknown category?

 

Edge Detection in Color Images

Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions.

This applications demonstrates an edge detection algorithm for general images in color or monochrome. This algorithm uses 1-dimensional lines/functions/textures/images generated from input image to find edges in the image.

Compared to results generated from some known methods, this has marked some unseen edges. The algorithm is adaptive and parameter-izable allowing scope to increase or reduce detail and tolerance. This gives a straight forward approach to refine edges within the image with greater accuracy.

The parameters are mostly visual and observable based on human psychological perception (namely angle, color density, subtraction strategy and detail) rather than existing objective and quantitative methods of mathematical transforms.

Since the algorithm does not use heavy matrix calculations / transforms, it delivers more performance. The algorithm is progressive, as it can be used for noisy signals. It can also be used during progressive loading or partly received image. The algorithm can work with as few linear samples as available or as many.

Existing methods are applied to image as a whole and cannot be applied to different parts of the image with different parameters without breaking the image in different parts and later stitching them back.

Fast_EdgeDetection/4.JPG

Fast_EdgeDetection/5.JPG

BotZilla

Sample Image - maximum width is 600 pixels

A chatterbox application with speech synthesis and speech recognition tacked on to it. Demonstrating how to build increasingly sophisticated recognition rules.

Vision on Virtual Commands

 

This application is used to show how a computer interacts with a human and converts his/her motion in command actions. It detects motion and converts that motion into the respective command actions.

 

To detect motion, it uses a simple algorithm of subtracting two images. Every image is made up of three layers: red, blue, and green. Thus, each of the pixels in an image holds three values corresponding to its RGB.

 

Sample Image - maximum width is 600 pixels

Sample Image - maximum width is 600 pixels

 

 

Computer Vision: Virtual Buttons

 

Wouldn't it be great if you could control your computer with your hands? In this demonstration, you will see a small application to control the Windows Media Player with my hand motion and an ordinary web-cam. You can see the picture above; this application creates three "hot-spots" in the web-cam view, and you can assume that these hot-spots are virtual buttons, and they get activated when you make a click movement in them.

 

Image Recognition with Neural Networks

Screenshot - screen211.png

 

 

Shape Detection

In this application, we demonstrate Hough Line and Circle detection, as well as using the Image Processing to detect Triangles and Rectangles in the image.

 

 

Chess

PatternRecognizer is a fast machine learning algorithm library . It contains support vector machine, neural networks, bayes, boost, k-nearest neighbor, decision tree, ..., etc.

This sample demonstrates learning chess board pattern with support vector machine, chess board pattern is a 640 random points formed synthetic dataset.

2120514_80843662.png

legend: squares are support vectors.

 

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