This document discusses recognition and enhancement of traffic signs in computer-generated images. It presents a multi-step process including image preprocessing, recognition, and enhancement. Image preprocessing involves smoothing and enhancing. Recognition identifies traffic signs like road markings and dark roads using techniques like scan line algorithms and filtering. Enhancement improves identified signs by increasing contrast. The process is effective but further improvement is needed to handle complex situations. Applications include traffic control and forensic analysis.

1. RECOGNITION AND ENHANCEMENT
OF TRAFFIC SIGN FOR COMPUTER-
GENERATED IMAGES
PRESENTED BY :
Shailesh kumar

2. CONTENT
 INTRODUCTION
 TECHNOLOGY
 WORKING
 APPLICATION
 FUTURE ASPECT
 CONCLUSION

3. INTRODUCTION
 As technology advanced day by day image processing gains huge
development in recent year.
 Image enhancement technology is often used to improve image
quality.
 Compared with the natural image, significant characteristics for
computer-generated image is simple in overall.
 The difficulty is how to accurately recognition and enhance specific
target and maintaining other information of object do not been
changed.

4. INTRODUCTION(CONTINUED)
 Here we consider 256-color indexed image that is generated based o
rendering 3D model.
 Combined with the characteristics of the computer-generated image ,
the recognition and enhancement takes series of steps
A. Image preprocessing.
B. Image recognition.
C. Image enhancement.

5. TECHNOLOGY
 Red light camera technology

6. WORKING
 Recognition and Enhancement take series of steps.
A. Image preprocessing
B. Image recognition
C. Image enhancement
A. Image Pre-processing
What is an image?
 Image is an array or a matrix, of a square pixels arranged in columns
And rows.
What it includes(Image preprocessing)?
 Smoothing and enhancing the image.

7. WORKING(CONTINUED)
Recognition of Traffic Sign
Traffic sign include mark line and dark road.
 Recognition of Mark Line.
 Recognition of Dark Road.
Fig 3.1 It shows the mark line and the dark road.
Mark line.
Dark road.
Slope part.

8. Fig 4.1 Image corrupted
with noise.
Fig 4.2 Result smoothed
by smoothing
Recognition of mark line
 There are two methods first is scan line algorithm and another is
a series of filtering operation.
 Scan line algorithm is used because of its simpleness and high
efficiency.
 The 1st binary image is form by performing the algorithm in horizontal
direction and 2nd in vertical direction.

9. STEPS
1.
2. There are still some mistakes object.
Figure 4.4 The left image is the result
of the step 1, the Right image is
result of the step 3, and the contrastive
Effect shows in the red circle.
3.

10. 4.The most likely object of the dark road are recognized.
Figure 4.5. The left image is
result of the step 1, the right
Image is result of the step 4.
The red part within the
Green circle is missing mark
line in the step 1
5. The object of the road surface.
6. The object of non road surface recognized.
7. Figure 4.6.The left image is
the integrated result in the
step 5.
The right image is the
precise result

11. 8.The object of typical dark road.
9. The object of the mark line.
C. Enhancement of traffic sign
B. The recognition of dark road
It having two parts i.e. recognition of flat part and recognition of slope
part

12. ADVANTAGES AND ITS APPLICATION
 Effectively stored and efficiently transmitted.
 Digital image processing is easy to implement.
 We can remove unwanted objects, adjust exposure,
saturation, hue, levels, sharpness and more.
APPLICATIONS:
 Image processing is use in generating images and for
removing noise for the corrupted image.
 It is used in detection and enhancement of traffic sign.
 For controlling the traffic light.
 Used in various forensic cases Eg. fingerprint detection

13. Fig.6.1.Unenhanced image of Fig.6.2.The result of one enhancement Latent
print. Technique use on image to increase the contrast.

14. FUTURE ASPECT
Social
x-ray

15. CONCLUSION
 In order to enhance the specific target in the computer-generated image
for the actual production requirement.
 Experiment and practice demonstrate this project and algorithm are
very effective.
 However, in the complex situation, there are some errors. The follow
work is to improve and research enhancement for specific target in
complex situation.
 It seems that image recognition and image enhancement is still not
simple process. Therefore, we imagine that image recognition for
complex real scene image also is not simple process. But the idea of
stepwise refinement which is proposed here provides a methodological
reference for complex Image recognition.

16. REFERENCES
[1]Li Yaping , Zhang Jinfang, Xu Fanjiang“The recognition and
enhancement of traffic sign for the computer-generated image” IEEE
paper, 2012 Fourth International Conference on Digital Home.
[2] “Introduction to computer vision and image processing”, Pdf
from the link www.uotechnology.edu.iq.
[3] Online information provider www.google.com and
www.wikipedia.com.
[4] “Digital Image Processing for Image Enhancement and
Information Extraction” an IEEE international general.