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

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.

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

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  1. A PREPROCESSING METHOD AND ROTATION INVARIANT 2D OBJECT RECOGNITION USING BPG NEURAL NETWORKS Irina Topalova

  2. Preprocessing Backpropagation NN Class Image Introduction to NN processing Quality Complex Simple Simple Complex • Accuracy

  3. The Problem • Image – Low quality web camera • Preprocessing - ? • Backpropagation NN - ? • Class – High accuracy Oblong Objects Class 1 - Hammer Class 2 - Spanner

  4. The Preprocessing Step 1: Color to grey-level conversion: For each image pixel calculate: . Hammer - color Hammer – grey-level

  5. Hammer – Sobel The Preprocessing Step 2: Sobel contour: • Utilization of the first gradient of the image function • Small amount of noise • Thick edges Hammer – grey-level

  6. The Preprocessing Step 2: Sobel contour: Image function V Sobel mask MxSobel mask My

  7. Hammer – Sobel Hammer – vectorized The Preprocessing Step 3: Contour vectorization: • Outer contour tracing • Weighted chain-code with backtracking • Edge points ordering – ordered list of coordinates

  8. Hammer – rotated Hammer – vectorized 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

  9. The Preprocessing Step 4: Contour rotation: For each calculate: for all n contour points and form the following metric: . Find and rotate the image contour by the angle φ.

  10. Hammer – rotated The Preprocessing 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 Hammer – radial profiles

  11. The Preprocessing Step 5: Radial profile function: Calculate the contour gravity center : . Form the radial profile function: and pass it to the NN.

  12. The BPG Neural Network • Good accuracy after training • Easy supervision of the training process The NeuFrame BPG NN

  13. The BPG Neural Network • 2x24 training images; 2x10 query images • 30 input and 2 output sigmoid neurons The NN Topology

  14. Results • Training error: 0,005 successfully reached • Well-formed error graph • Query accuracy: 20/20 - 100% The NN error graph

  15. 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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