Smart Traveller with Visual Translator

1. Smart Traveller with Visual Translator

2. What is Smart Traveller? • Mobile Device which is convenience for a traveller to carry • E.g. Pocket PC, Mobile Phone

3. What is Visual Translator? • Recognize the foreign text and translate it into native language • Detect the face and recognize it into name

4. Requirements • Simple (Computational low power) • Lightweight (Low Storage) • User Friendly

5. Find Each Object from the Image Quantify the object by some characteristics Assign Label for each object Image Segmentation Feature Extraction Classification Input Image Object Image Feature Vector Object Type Core Pattern Recognition Model

6. Character Recognition • Language: Korean • Target: Sign, Guidepost • Contrast in Color • Printed Character

7. Image Segmentation • Binarization • Using Color Histogram to binarize the image for the background and the character • Text Region Segmentation • User Define Method • Edge Detection with horizontal and vertical projections • Stroke Extraction • Labeling of connected component Algorithm

8. Feature Extraction • Stroke Features • Number of Junctions, Corners • Any Hole • Gabor Features

9. Recognition • Minimum Euclid Distance • Learn the Decision Tree by training examples

10. Demo

11. Face Detection Outline Find Face Region Find the potential eye region Locate the iris and eyelids

12. Find Face Region - Color-based model • We used this method because of its simplicity and robustness. • Usually RGB color model will be transformed to other color modes such as YUV (luminance-chrominance) and HSB (hue, saturation and brightness)

13. YUV • We use YUV or YCbCr color model. • Y component is used to represent the intensity of the image • Cb and Cr are used to represent the blue and red component respectively.

14. YCbCr Image • Y, Cb ,Cr component image Y Cb Cr

15. Representation of skin color • We just use a simple ellipse equation to model skin color. Cr Cb

16. Representation of skin color • The white regions represent the skin color pixels

17. Color segmentation • We distribute some agent in the image uniformly. • Then each agent will check whether the pixel is a skin-like pixel and not visited by the other agent. • If yes, it will produce 4 more agents at its four neighboring points. • If no, it will move to one of four neighboring points randomly and decrease its lifespan by 1. When its lifespan becomes zero, it will be removed from the image.

18. Color segmentation • This agent produce 4 more agents

19. Color segmentation • The advantage of this algorithm is that we need not to search the whole image. • Therefore, it is fast.

20. Color segmentation • 19270 of 102900 pixels is searched (about 18.7%) • There are 37 regions • Each color regions represent each regions searched by a father agent

21. Eye detection • After the segmentation of face region, we have some parts which are not regarded as skin color. • They are probably the region of eye and mouth • We only consider the red component of these regions because it usually includes the most information about faces.

22. Eye detection • We extraction such regions. • The red region represent the region which is not skin color.

23. Eye detection We do the following on the regions of potential eye region • Histogram equalization • Threshold • Template matching

24. Eye detection Histogram equalization Threshold with < 49 Template Matching

25. Locating the iris and eyelids We plan to use the following methods to improve the face detection We can use these methods to locate the iris and eyelid precisely. Template matching • Correlation variance filter • Deformable template

26. END