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.



Recognition of hand gestures in real time

Recognition of hand gestures in real time, based on neural networks (multilayer perceptron, Hopfield NN)

As part of the application occurs pattern recognition (hand gestures), taken with the camera. Hand position in the image is determined by the color of pixels hand. For pattern recognition using neural networks (multilayer perceptron, Hopfield neural network). Recognition occurs in real time

This screenshot shows the main window of the application, as well as the recognition result is shown


This shows the settings window colors and choice of color spaces.

2D Organizing

2D Organizing sample application

This very simple application demonstrates self organizing feature of Kohonen artificial neural networks. Initially the application creates a neural network with neurons' weights initialized to coordinates of points in rectangular grid. After that the network is continuously fed by coordinates of previously generated random points. While these points are presented to the network, it does organization of its structure. Visualizing the neural network by treating neurons' weights as coordinates of points shows a picture, which is close to the picture of randomly generated map, which was fed to the network.


SOM Coloring

SOM Coloring sample application

This application represents another sample showing self organization feature of Kohonen neural networks and building color clusters. All network's neurons have 3 inputs and initially 3 corresponding weights of each neuron are initialized randomly in the [0, 255] range. Weights of each neuron may be treated as RGB tuple, which means that initially neural network represents a rectangle of random colors. During training phase, the network is fed by random colors, which results to network's self organizing and forming color clusters.


Financial Prediction

This application demonstrates the usage of Neural Networks for predicting Market Share Values.

This "forecasting" capability makes them a perfect tool for several types of applications:

  • Function interpolation and approximation
  • Prediction of trends in numerical data
  • Prediction of movements in financial markets

All the examples are actually very similar, because in mathematical terms, you are trying to define a prediction function F(X1, X2, ..., Xn), which according to the input data (vector [X1, X2, ..., Xn]), is going to "guess" (interpolate) the output Y. The most exciting domain of prediction lies in the field of financial market.


Results of prediction


Visual Surveillance

Sample Image - maximum width is 600 pixels


Visual surveillance is an attempt to detect, recognize and track certain objects from image sequences, and more generally to understand and describe object behaviors.

Motivation for Surveillance Systems

Anomaly Detection

The ability to learn what normal behaviors and anomalies are.
For example it is known that in an office building entrance, people usually go straight to the lobby after entering the building. Thus individuals that go in a different direction can be considered harmful.

Automated Security

Increases effectiveness of the entire surveillance process by paying attention only to certain events instead of watching and analyzing several different surveillance cameras as happens today. Such a system can also decrease costs.

Crowed Flux Statistics

For example, by knowing how many cars are using a certain road and how many are coming from road A or B we can decide which road to widen.

Blood Squirting Halloween skull

Just a nice usage found on coding4fun, not all reasons have to be dead serious.



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