The Center for Signal & Image Processing       Georgia Institute of Technology         Enhanced adaptive filter-          ...
Outline                                                  2                        The Center for Signal & Image Processing...
3Background                                    The Center for Signal & Image Processing•   Most of the state departments o...
Background                                             4                      The Center for Signal & Image Processing    ...
5Background                                           The Center for Signal & Image Processing•   Popular Filter-bank-base...
6Background                                            The Center for Signal & Image Processing•   Disadvantages to both p...
7Design Challenges                        The Center for Signal & Image Processing   Pavement Distress                    ...
8Proposed Segmentation System   The Center for Signal & Image Processing
9Proposed System: Preprocessing                     The Center for Signal & Image Processing                              ...
10Proposed System: Time-Varying Filtering                       The Center for Signal & Image Processing                  ...
11Proposed System: Time-Varying Filtering                 The Center for Signal & Image Processing                        ...
12Proposed System: Time-Varying Filtering                            The Center for Signal & Image Processing       An int...
13Proposed System: Segmentation                              The Center for Signal & Image Processing                     ...
14Proposed System: Clustering and HVS            The Center for Signal & Image Processing•   Since current ground truths a...
15Results GDOT image #1D579384                               The Center for Signal & Image Processing (a) Ground Truth   (...
16Results GDOT image #1D579384                                The Center for Signal & Image Processing (a) Ground Truth   ...
17Results S + P SPIHT Compressed Images    The Center for Signal & Image Processing  (a) GDOT image #1D579384    (a) GDOT ...
18Conclusion                                         The Center for Signal & Image Processing    We focused on incorporat...
19Bibliography                                                                   The Center for Signal & Image Processing1...
Upcoming SlideShare
Loading in …5
×

Enhanced adaptive filter bank-based automated pavement

613 views

Published on

We incorporate, evaluate, and assess the feasibility of
using filter banks in automated pavement distress systems from a
system level. We integrate a novel filter-bank-based distress segmentation
method, which, unlike previously researched methods, does not
depend on highpass data. In addition, we incorporate the standard
Said Pearlman set partitioning in hierarchical trees compression
coder into the automated pavement distress system, which is a
first in this area of research. A third contribution of the research is
a statistical detection algorithm that assists in overall system performance.
Preliminary testing using images provided by the Georgia
Department of Transportation demonstrate the promise of the proposed
method.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
613
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Enhanced adaptive filter bank-based automated pavement

  1. 1. The Center for Signal & Image Processing Georgia Institute of Technology Enhanced adaptive filter- bank-based automated pavement crack detection and segmentation system By Clyde A. Lettsome, Yi-Chang Tsai, and Vivek Kaul
  2. 2. Outline 2 The Center for Signal & Image Processing• Background• Design Challenges• Proposed System• Results• Conclusion
  3. 3. 3Background The Center for Signal & Image Processing• Most of the state departments of transportation (DOT) use either visual or manual distress inspection systems, which are costly, dangerous, time-consuming, labor-intensive, and subjective.• Desire – Develop effective and cheap automated pavement distress system collects pavement images or video and detects distress without human intervention.
  4. 4. Background 4 The Center for Signal & Image Processing • Zhou1 proposed a popular automated distress detection and segmentation structure with two main sections.
  5. 5. 5Background The Center for Signal & Image Processing• Popular Filter-bank-based systems. • Zhou1 proposed distress detection method that compared the nonzero values in the highpass subbands to predetermined thresholds. • Li2 proposed a distress segmentation method that combined threshold selection method of Mallat and Zhong3 with Gaussian filtering to remove noise and detect edges in images.• Advantage filter bank methods allow both spatial and frequency domain analysis.
  6. 6. 6Background The Center for Signal & Image Processing• Disadvantages to both proposals. 1. Filter-bank decomposition, distress detection done on highpass data. Overlap and add due to row and column filtering causes construction and destruction of highpass data. 2. If standard compression coders (S+P SPIHT coder or JPEG 2000), segmentation would be performed on degraded high-low, low-high, and high-high subbands.
  7. 7. 7Design Challenges The Center for Signal & Image Processing Pavement Distress Row 140 of Pavement Distress Image image
  8. 8. 8Proposed Segmentation System The Center for Signal & Image Processing
  9. 9. 9Proposed System: Preprocessing The Center for Signal & Image Processing •Values larger than the mean minus one standard deviation are normalized to the mean of the image. •Other values remain the same. An image preprocessed to remove surface texture.
  10. 10. 10Proposed System: Time-Varying Filtering The Center for Signal & Image Processing Complimentary filters • G00(z) low-delay lowpass filter •G01(z) linear-phase lowpass filter • G02(z) high-delay lowpass filter Proposed System: Time-Varying Filtering
  11. 11. 11Proposed System: Time-Varying Filtering The Center for Signal & Image Processing Why these filters? (a) Low-delay lowpass filter step response (b) High-delay lowpass filter step response.
  12. 12. 12Proposed System: Time-Varying Filtering The Center for Signal & Image Processing An internal block diagram of the time-varying filtering block.
  13. 13. 13Proposed System: Segmentation The Center for Signal & Image Processing A window function of Li × Li, where Li is the length of the linear phase filter used in the development of the mask.An edge detection mask developed from row filtering.
  14. 14. 14Proposed System: Clustering and HVS The Center for Signal & Image Processing• Since current ground truths are determined empirically it is important to consider the human visual system (HVS).• Relationship between intensity and brightness is not linear.• Ernst Weber4 found that a perceived change in intensity occurs when
  15. 15. 15Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
  16. 16. 16Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
  17. 17. 17Results S + P SPIHT Compressed Images The Center for Signal & Image Processing (a) GDOT image #1D579384 (a) GDOT image #1D579384
  18. 18. 18Conclusion The Center for Signal & Image Processing  We focused on incorporating, evaluating, and assessing the feasibility of using wavelet/filter banks from a system level.  The advantage of the proposed method is that, despite the compression rate, it can be used on raw or compressed images.  The proposed system exhibited significant improvement versus existing filter-bank-based pavement distress segmentation methods.
  19. 19. 19Bibliography The Center for Signal & Image Processing1. J. Zhou, P. S. Huang, and F.-P. Chiang, “Wavelet-based pavement distress detection and evaluation,” Opt. Eng. 45(2), 027007 (2006).2. J. Li, “A Wavelet Approach to Edge Detection,” Master Thesis, Mathematics Sam Houston State University, Huntsville, Texas (2003).3. S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992).4. M. J. T. Smith and A. Docef, A Study Guide for Digital Image Processing, Scientific Publishers Inc., Riverdale, GA (1999).

×