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AUTOMATIC IMAGE ORIENTATION DETECTION

AUTOMATIC IMAGE ORIENTATION DETECTION. Goal. Main Goal: For any given picture detect its orientation. Sub Goals: How to deal with color images Define criteria for images to separate them to 4 groups: Efficiency: DB size, vector size, runtime. What is Color?. What is Color?.

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AUTOMATIC IMAGE ORIENTATION DETECTION

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  1. AUTOMATIC IMAGE ORIENTATION DETECTION

  2. Goal Main Goal: • For any given picture detect its orientation. Sub Goals: • How to deal with color images • Define criteria for images to separate them to 4 groups: • Efficiency: DB size, vector size, runtime.

  3. What is Color?

  4. What is Color?

  5. Color representation - RGB

  6. Color difference - RGB

  7. Color representation - HSV

  8. Classify function in MatLab

  9. Peripheral blocks

  10. Edge ratio

  11. Feature Vector Vector size: N=4 • Image resolution = 800X600 • NXN blocks • 4N-4 peripheral blocks • For each block: • Mean of H,S,V • Var of H,S,V • Edge density Block size : 16 peripheral blocks: 12 Vector size: 12*(3+3+1)+4 = 88

  12. Results

  13. Results

  14. Results

  15. Future work • Improving the feature vector • Testing new method of “machine learning” • Add a rejection criteria • Add classifier of indoor/outdoor • Add an object recognition algorithm

  16. Thank you!

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