a preprocessing method and rotation invariant 2d object recognition using bpg neural networks
DESCRIPTION
A PREPROCESSING METHOD AND ROTATION INVARIANT 2D OBJECT RECOGNITION USING BPG NEURAL NETWORKS. Irina Topalova. Preprocessing. Backpropagation NN. Class. Image. Introduction to NN processing. Quality. Complex Simple. Simple Complex. Accuracy. The Problem. - PowerPoint PPT PresentationTRANSCRIPT
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A PREPROCESSING METHOD AND ROTATION INVARIANT 2D OBJECT RECOGNITION USING BPG NEURAL NETWORKS
Irina Topalova
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Introduction to NN processing
Preprocessing
Backpropagation NN
Class
Image
SimpleComplex
ComplexSimple
Accuracy
Quality
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The Problem
Image – Low quality web camera Preprocessing - ? Backpropagation NN - ? Class – High accuracy
Class 1 - Hammer Class 2 - Spanner
Oblong
Objects
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For each image pixel calculate: .
The Preprocessing
Step 1: Color to grey-level conversion:
3ijijij
ij
BGRV
Hammer - color Hammer – grey-level
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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
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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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