This video shows the neural network project of a student of ETSII ULL university.
http://www.diegocerdan.es/gestos-de-raton/
Duration : 0:1:33
May 20
MouseGestures - Neural Network Project ETSII ULL 01
Archived in the category: neural networks
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12 comments for “MouseGestures - Neural Network Project ETSII ULL 01”
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botrules
The code pseudocode …
The code pseudocode is:
gestureDataX[i] = moveData[i].getX() - moveData[i - 1].getX()
gestureDataY[i] = moveData[i].getY() - moveData[i - 1].getY()
May 20th, 2009 at 11:36 pm
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AndrianGo
Are you making …
Are you making vectors from each consecutive pairs of pixels and then feed them to the NN? Is that how you think of it as derivative?
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botrules
Direct input is not …
Direct input is not good because Neural Network would recognice only the same movement. I mean, the same movement size in the same 2D coordinates. Thats not really what we mean with gesture.
Working with the derivate gives you more or less the gesture data from the movement. Size and 2D coordinates are not important.
May 20th, 2009 at 11:36 pm
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botrules
For recognizing …
For recognizing gestures I have used the Neural Network approach. There is sure other ways to do it but I found NN very interesting .
If you take the path of NN you have to learn about it first to catch the most important concepts. I thinks little teory its important before trying something. After that you are ready to code something you want be resolved by a NN.
Hope being helpful.
May 20th, 2009 at 11:36 pm
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AndrianGo
I don’t get it. …
I don’t get it. What does the derivative give you? why is that better than direct input?
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darkdragon7099
Amazing, I wanna …
Amazing, I wanna get started on this subject. Any tips?
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biorythme232
Your choice about …
Your choice about the data to use is a very interesting and efficient approach. Thanks for the link !
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botrules
I have used a Multi …
I have used a Multi Layer Perceptron.
The structure is 50 float inputs, 25 middle layer neurons and a dinamic size output depending the number of different gestures.
The input data are not the pixel positions. Its more or less the difference of one pixel respect to the next one. Math concept of derivative.
You can take a look at the source code, linked on the description.
May 20th, 2009 at 11:36 pm
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biorythme232
Nice work. What is …
Nice work. What is the type of neuronal network ? Perceptron ? how many layers and neurons ?
And what’s the input data, the pixel positions ?
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