The document presents an algorithm for detecting and recognizing restroom signage in images. It proposes using shape detection to identify the head and body of signage, and then using SIFT features to match corners and recognize the specific signage template. An experiment on 102 images achieved 89.2% detection and 84.3% recognition rates. Challenges included view angle changes and complex backgrounds. The work was supported by NIH and NSF grants.

1. Shuihua Wang and Yingli Tian
{swang15, ytian}@ccny.cuny.edu
Presented by: Shizhi Chen
Department of Electrical Engineering
The City College of New York

2. Outline
 Motivation
 Proposed algorithm
 Experimental results

3. Motivation
 Access unfamiliar environment
 Recognize restroom signage
 Available technology
(a) (b) (c)

4. Proposed Algorithm

5. Image Preprocess
Original Gray Binary Connected
Image Image Image Components

6. Signage Detection: Head
 Based on shape of Connected Components (CC)
 Head shape is circle

7. Signage Detection: Body
 More variations
 Close to head
 Based on shape

8. Signage Detection Results
 Scale invariant
 Rotation invariant
 Illumination invariant

9. Signage Recognition: Find Corners
 Search within detected signage region
 SIFT (Scale Invariant Feature Transform) detector
 Search over all scales
Template
Signage
Detected
Signage

10. 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

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

12. 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

13. Intermediate Results
Original
Image
Binary
Image
Connected
Component
W D M D
W M
Signage
Recognition

14. Recognition Success
M
M M W
M
M
W
W W
W
W W D
M D D
D W

15. Detection Fails
 Significant view angle changes
 Complex Background

16. Acknowledgement
 Supported:
 NIH 1R21EY020990,
 NSF grants IIS-0957016
 EFRI-1137172.
16

17. Thank you!

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