Shuihua Wang and Yingli Tian

{swang15, ytian}@ccny.cuny.edu

Presented by: Shizhi Chen

Department of Electrical Engineering

The City College of New York

OutlineMotivation

Proposed algorithm

Experimental results

Motivation Access unfamiliar environment

Recognize restroom signage

Available technology

(a) (b) (c)

Proposed Algorithm

Image Preprocess

Original

Image

Gray

Image

Binary

Image

Connected

Components

Signage Detection: Head Based on shape of Connected Components (CC)

Head shape is circle

Signage Detection: Body More variations

Close to head

Based on shape

Signage Detection Results Scale invariant

Rotation invariant

Illumination invariant

Signage Recognition: Find Corners Search within detected signage region

SIFT (Scale Invariant Feature Transform) detector

Search over all scales

Template

Signage

Detected

Signage

Signage Recognition: Match Corners SIFT descriptor

Histogram of gradients

Rotation and scale invariant

Matching pair of corners: minimal Euclidean distance

Find the template with maximum matching pairs

Template

Signage

Detected

Signage

Signage Database 102 Signage: Men (50); Women(42); Disabled(10)

Experiment Results 89.2% detection rate (91 out of 102 images)

84.3% recognition rate (86 out of 102 images)

Confusion matrix: column is the ground truth

Original

Image

Binary

Image

Connected

Component

Signage

Recognition

W MDW M D

Intermediate Results

MM

MM W

W

WW

W

W

W

W

M

MD

D DD

Recognition Success

Detection Fails

Significant view angle changes

Complex Background

Acknowledgement Supported:

NIH 1R21EY020990,

NSF grants IIS-0957016

EFRI-1137172.

16

Thank you!

Author Contact

Shuihua Wang and Yingli Tian{swang15, ytian}@ccny.cuny.edu