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Smart Traveller with Visual Translator. What is Smart Traveller?. Mobile Device which is convenience for a traveller to carry E.g. Pocket PC, Mobile Phone. What is Visual Translator?. Recognize the foreign text and translate it into native language Detect the face and recognize it into name.

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What is smart traveller
What is Smart Traveller?

  • Mobile Device which is convenience for a traveller to carry

  • E.g. Pocket PC, Mobile Phone


What is visual translator
What is Visual Translator?

  • Recognize the foreign text and translate it into native language

  • Detect the face and recognize it into name


Requirements
Requirements

  • Simple (Computational low power)

  • Lightweight (Low Storage)

  • User Friendly


Core pattern recognition model

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


Character recognition
Character Recognition

  • Language: Korean

  • Target: Sign, Guidepost

    • Contrast in Color

    • Printed Character


Image segmentation
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


Feature extraction
Feature Extraction

  • Stroke Features

    • Number of Junctions, Corners

    • Any Hole

  • Gabor Features


Recognition
Recognition

  • Minimum Euclid Distance

  • Learn the Decision Tree by training examples



Face detection

Face Detection

Outline

Find Face Region

Find the potential eye region

Locate the iris and eyelids


Find face region color based model
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)


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.


Yc b c r image
YCbCr Image

  • Y, Cb ,Cr component image

Y Cb Cr


Representation of skin color
Representation of skin color

  • We just use a simple ellipse equation to model skin color.

Cr

Cb


Representation of skin color1
Representation of skin color

  • The white regions represent the skin color pixels


Color segmentation
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.


Color segmentation1
Color segmentation

  • This agent produce 4 more agents


Color segmentation2
Color segmentation

  • The advantage of this algorithm is that we need not to search the whole image.

  • Therefore, it is fast.


Color segmentation3
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


Eye detection
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.


Eye detection1
Eye detection

  • We extraction such regions.

  • The red region represent the region which is not skin color.


Eye detection2
Eye detection

We do the following on the regions of potential eye region

  • Histogram equalization

  • Threshold

  • Template matching


Eye detection3
Eye detection

Histogram equalization

Threshold with < 49

Template Matching


Locating the iris and eyelids
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



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