VLSI Signal Processing Lab, Institute of Electronics National Chiao Tung University, Hsinchu, Taiwan...
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2007/07/19 V L S I S i g n a l P r o c e s s i n g L a b , I n s t i t u t e o f E l e c t r o n i c s N a t i o n a l C h i a o T u n g U n i v e r s i t y , H s i n c h u , T a i w a n Platform-Based Design Group Segmentation with raster sliding local window Nelson Chang Institute of Electronics, National Chiao Tung University
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Transcript of VLSI Signal Processing Lab, Institute of Electronics National Chiao Tung University, Hsinchu, Taiwan...
2007/07/19
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Platform-Based Design Group
Segmentation with raster sliding local window
Nelson ChangInstitute of Electronics,
National Chiao Tung University
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Platform-Based Design Group
Outline
• Stereo Matching with Segment• Traditional Watershed-based Segmentation• Downfall of tradition segmentation methods• Proposed Raster Sliding Local Window Method• Result• Future work
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Platform-Based Design Group
Census Diffusion WITHOUT Segmentation
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Platform-Based Design Group
Census Diffusion WITH segmentation
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Platform-Based Design Group
Mean Shift vs. Watershed
Mean Shift
Watershed
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Platform-Based Design Group
Traditional Watershed-based Segmentation
• Steps– Generate Gradient Map– Reconstruction/Global Denoise– Watershed flooding or Rain dropping– Region Merging
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Platform-Based Design Group
What makes an algorithm efficient to implement?
• Regular– Simple control– High data reusability
• Parallel– More scalable
• Small storage– Lower cost– More fast memory usage
state 0
state 1state 2
state 0
state 1state 2
state 3
state 4
PE PE PE PE PE
PE
PE
PE
4x speed up!!
Data reuse!!!
Simple FSM
Fast SRAM
Slow SDRAM
Temporary Data
Temporary Data
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Platform-Based Design Group
Traditional Watershed and Topoggan
• Watershed Flooding– Bottom-up
• Finds the minimum first
Irregular jumpy access pattern
HW: hard to reuseSW: low cache hit
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Platform-Based Design Group
Traditional Watershed and Topoggan
• Topoggan Rain Dropping– Top-down
• Finds the maximum peak first• Drop rain• Back trace the path
Back trace water flow path(tree traversal)
Irregular access start pointHW: less chance to reuse
SW: more cache missMore regular local access
pattern than watershed
High data dependencyHW: less parallelism
availableSW: not much impact
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Platform-Based Design Group
Our Goal
• Regular process and access pattern• Avoid tree traversal/tree traversal• Minimize iteration count
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Platform-Based Design Group
Overview (1/3)
• Flowchart
Read image information
gradient
Local-approach segmentation
start
Done
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Platform-Based Design Group
Gradient Generation (0th Iteration)
• Color space– YUV– Select the maximal gradient among the three components
• Gradient - Mathematical morphology – ABS(Dilation – Erosion)
• Dilation : Max in 3x3 square window
• Erosion : min in 3x3 square window
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7 10 10
6 10 5
Gradient = 16-5 = 11
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Platform-Based Design Group
Local approachLocal
approach
Local de-noise
Basin/plateau?
Assign 3x3 window to
same regionDo nothing
Done image?
yes no
no
a
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x has label?
N(x) ≥ x & N(x) has no label
Diffuse label upward
Do nothing
Done image?
Done
yes
yes no
yes
no
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Platform-Based Design Group
Local Denoise (1st Iteration)
i j k
l x m
n o p
Neighbor of x N(x) = {i, j, k, l, m, n, o, p}Gradient of x G(x)Gradient after local denoise G’
For each N(x), If | G(x) - G(N(x)) | < TH, G’(N(x) = G(x) Else G’(N(x)) = G(x)
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G
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G’
TH=10
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Platform-Based Design Group
An Example of Local Denoise
• G’ (Gradient after local denoise) of an overlapping pixel in two neighboring windows may be different
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G
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G’
TH=1042
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G’
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Platform-Based Design Group
Adaptive Bi-threshold
• TH for the next window changes– Current window is a plateau/basin
• Next TH = TH_max
– Else• Next TH = TH_min
Plateau/Basin
Use TH_max
Possible Edge/Hill
Use TH_min
Current Next
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Platform-Based Design Group
Basin/Plateau Detection (1st Iteration)
Neighbor of x N(x) = {i, j, k, l, m, n, o, p}Gradient of x G(x)Gradient after local denoise G’Segment (Region) Label of x L(x)
For all N(x), If G’(N(x)) – G’(x) ≥ 0, L(x) = all L(N(x)) Else L(x) = no label
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G’ L
No original label on N(x)
L
One N(x) already has labelBasin
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Platform-Based Design Group
Segment Label Aliasing (1st Iteration)
• For a basin/plateau
L
More than one N(x) already having different label
Set label alias
=
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Label Alias Table
update
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Platform-Based Design Group
Label Upward Diffusion (2nd~Nth Iteration)
Neighbor of x N(x) = {i, j, k, l, m, n, o, p}Gradient of x G(x)Segment (Region) Label of x L(x)
For each N(x), If L(N(x)) = no label AND G(N(x)) – G(x) ≥ 0, L(N(x))=L(x) Else L(N(x)) = no label
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G L
This can be done for more than 1 iterations
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Platform-Based Design Group
Label Replacement (N+1 Iteration)
• Update each pixel’s label– Use Label Alias Table– Similar to region (segment) merging
=
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Label Alias Table
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Platform-Based Design Group
Segmentation with Raster Sliding Local Window
Watershed
Raster Sliding Window
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Platform-Based Design Group
Results
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Platform-Based Design Group
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Platform-Based Design Group
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Platform-Based Design Group
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Platform-Based Design Group
Ranking
• CDS stereo matching performance comparison on Middleburry website
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Platform-Based Design Group
Future work
• Complexity analysis – Algorithmic Complexity– Operation Complexity– SW execution– Storage Requirement
• Hardware implementation– ISCAS’09
• Refine performance– Boundary improvement– Label alias issue
