AUTOMATIC IMAGE ORIENTATION
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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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Presentation Transcript

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.





Color difference rgb
Color difference - RGB






Feature vector
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





Future work
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



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