1. Leaves Recognition By Zakir Mohammed 991698689 Indiana State University Computer Science

2. Link for the website Link for the website http://cs.indstate.edu/~zmohammed1/

3. Divided Into three major parts Image Processing Neural Networks Recognition

5. Image Processing It is classified into 3 main parts Image edge detection Thinning Leaf Image Token

6. Image edge detection We use the Prewitt edge detection algorithm. Prewitt edge is used within edge detection algorithm to define the gradient If A is the source image we use a 3*3 matrix for combining the two functions and to get the edge or the difference. So G x and G y would be horizontal and vertical derivations. *-Convolution which is a combination of two functions to get a new function

7. In order to make a G x we would use a separable filter which is an image process technique which would make two convolution into 1 2-D image X coordinate would give the right direction and y coordinate would give the down direction. Then we find the gradient by So now the image would be like this

8. Grayscale image of a brick wall & a bike rack Gradient with Prewitt operator of grayscale image of a brick wall & a bike rack

9. Thinning Algorithm This algorithm is used to convert binary shapes into edges or boundary to 1-pixel. Iteratively delete pixels inside the shape to shrink it without shortening it or breaking it apart.

10. Leaf image token Green line: The shape of the leaf image after a successfull edge detection & thinning. Red Square: This square represents a point on the shape of the leaf image from which we are going to draw a line to the next square Blue line: The compound of the center of two squares from which we are going to calculate the cosinus and sinus angle. Such a blue line is a representation of a leaf token.

11. On the left hand side you see a small image of the right-angled triangle which represents a token of a single leaf image. Here it should be clear now that the angles A and B are the two necessary parts which will be fit into the neuronal network layers. With this two angles we can exactly represent the direction of the hypotenuse from point P1 to P2 which is absolutly necessary for the representation of a leaf image.

12. Neural Networks

14. Node Connection Input

15. Input Nodes Output Nodes

16. Input Nodes Output Nodes

17. Input Nodes Output Nodes A B c

19. Transfer function would determine the which color has to be elected. As all the signals in a computer are taken as numbers so the transfer function would be a mathematical equation.

20. After it sets the value then it triggers the next node to set value with that value. Choosing whether the triggered value is most useful for the output or not. As these are nodes which produce the result what we want. Output Nodes

22. Input Nodes Output Nodes Hidden nodes

23. Input Nodes Output Nodes ? ? ? ? ? ? ? ?

24. Back Propagation Input Nodes Output Nodes Random weights Random weights

25. Input Nodes Desired Output

26. Input Nodes Calculated Outputs

27. Desired Output Calculated Outputs = ?

28. Desired Output Calculated Output - = 35 45 17 14 29 41 Difference 21 16 -24

44. Recognition This would also use the same two techniques In here we give a new picture of the leaf and it does give us the name if we have stored the name of the leaf earlier It use the neural network to match the leaf. Hence the name of the leaf is given.