A PREPROCESSING METHOD AND ROTATION INVARIANT 2D OBJECT RECOGNITION USING BPG NEURAL NETWORKS

Irina Topalova

2

Introduction to NN processing

Preprocessing

Backpropagation NN

Class

Image

SimpleComplex

ComplexSimple

Accuracy

Quality

3

The Problem

Image – Low quality web camera Preprocessing - ? Backpropagation NN - ? Class – High accuracy

Class 1 - Hammer Class 2 - Spanner

Oblong

Objects

4

For each image pixel calculate: .

The Preprocessing

Step 1: Color to grey-level conversion:

3ijijij

ij

BGRV

Hammer - color Hammer – grey-level

5

The Preprocessing

Step 2: Sobel contour: Utilization of the first gradient of the image function Small amount of noise Thick edges

Hammer – grey-level Hammer – Sobel

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The Preprocessing

Step 2: Sobel contour:

-1 -2 -10 0 01 2 1

-1 0 1-2 0 2-1 0 1

23 34 1850 200 226148 234 180

Sobel mask Mx Sobel mask MyImage function V22

3

1

3

1

3

1

3

1

;; yxi j

ijyijy

i jij

xijx TTTVMTVMT

379180226.21814850.223 xT687180234.21481834.223 yT

predefined

?22 T784.6687379 T

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The Preprocessing

Step 3: Contour vectorization: Outer contour tracing Weighted chain-code with backtracking Edge points ordering – ordered list of coordinates

Hammer – Sobel Hammer – vectorized

8

The Preprocessing

Step 4: Contour rotation: NN facilitation – especially effective for

oblong objects One large, loose cloud several small, tight clouds

in the parametrical space

Hammer – vectorized Hammer – rotated

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and form the following metric: .

For each calculate:

The Preprocessing

Step 4: Contour rotation:

k

k

k

k

ji

ji

cossinsincos

}360...2,1{

for all n contour points

k

k

ji

n

kkjD

1

2)(

Find and rotate the image contourby the angle φ.

DD min

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Step 5: Radial profile function: Numerical function passed to the BPG NN Contour resampling – only N of n edge points Further enhancement of the rotation invariance

The Preprocessing

Hammer – rotated Hammer – radial profiles

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Calculate the contour gravity center : .

The Preprocessing

Step 5: Radial profile function:

n

kk

n

kk

GC

GC

jn

in

ji

1

1

1

1

Radial Profiles

0153045607590

105

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

K

f(k)

HammerSpanner

Form the radial profile function:22 )()()( GCkGCk jjiikf and pass it to the NN.

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The BPG Neural Network

The NeuFrame BPG NN

Good accuracy after training Easy supervision of the training process

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The BPG Neural Network

The NN Topology

2x24 training images; 2x10 query images 30 input and 2 output sigmoid neurons

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Results

The NN error graph

Training error: 0,005 successfully reached Well-formed error graph Query accuracy: 20/20 - 100%

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Conclusions

The preprocessing stage delivers consistent input data to the NN thus facilitating its training and making the identification of the input descriptors of overlapping classes much easier.

The preprocessing stage is fast enough to be implemented in real time working systems.

Further research on noisy 2D objects could be carried out .

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