ORIENTATION DETECTION Goal Main Goal: For any given picture detect - - PowerPoint PPT Presentation

orientation detection
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ORIENTATION DETECTION Goal Main Goal: For any given picture detect - - PowerPoint PPT Presentation

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: = 0, = 90, =


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AUTOMATIC IMAGE ORIENTATION DETECTION

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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: 𝜕 = 0°, 𝜕 = 90°, 𝜕 = 180°, 𝜕 = 270°

 Efficiency: DB size, vector size, runtime.

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SLIDE 3

What is Color?

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What is Color?

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Color representation - RGB

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Color difference - RGB

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Color representation - HSV

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Classify function in MatLab

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Peripheral blocks

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Edge ratio

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Feature Vector

 Image resolution = 800X600  NXN blocks  4N-4 peripheral blocks  For each block:

  • Mean of H,S,V
  • Var of H,S,V
  • Edge density

Vector size: N=4 Block size : 16 peripheral blocks: 12 Vector size: 12*(3+3+1)+4 = 88

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Results

% T est size Vector size Feature Vector Color scheme DB size N 62% 50 24 Mean RGB 200 3 % 34 50 48 Mean+var RGB 200 3 76% 70 24 Mean HSV 300 4 79% 400 37 Mean+edge HSV 300 4 82% 400 88 Mean+Var+edge HSV 300 4 81% 200 200 Mean+Var+edge HSV 300 8

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Results

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Results

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Future work

 Improving the feature vector  T

esting new method of “machine learning”

 Add a rejection criteria  Add classifier of indoor/outdoor  Add an object recognition algorithm

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SLIDE 16

Thank you!