Difference between revisions of "Publications/crozet.14.icip.inc"

From LRDE
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__TOC__
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== Figures ==
  
== Figures ==
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=== Fig. 1 ===
   
 
{| class="wikitable" border="1"
  
{| class="wikitable" border="1"
 
|-
  
|-
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| [[File:Crozet14icip_Intronoise.png|150px|x]]
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| [[File:Crozet14icip_Intronoise.png|100px|x]]
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| [[File:Crozet14icip_Intronoiseout.png|150px|x‎]]
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| [[File:Crozet14icip_Intronoiseout.png|100px|x‎]]
 
|-
  
|-
 
! colspan="2" | (a) Denoising (self-dual grain removal).
  
! colspan="2" | (a) Denoising (self-dual grain removal).
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! colspan="2" | (b) Shape Filtering (keep round objects).
  
! colspan="2" | (b) Shape Filtering (keep round objects).
 
|-
  
|-
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| [[File:Crozet14icip_Introsynthetic.png|300px|x]]
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| [[File:Crozet14icip_Introsynthetic.png|none|x]]
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| [[File:Crozet14icip_Introsyntheticout.png‎|300px|x]]
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| [[File:Crozet14icip_Introsyntheticout.png‎|none|x]]
 
|-
  
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! colspan="2" | (c) Object Detection (energy-based method).
  
! colspan="2" | (c) Object Detection (energy-based method).
 
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|-
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| [[File:Crozet14icip_Introplane.png|thumb‎|x]]
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| [[File:Crozet14icip_Introplane.png|none|x]]
 
| [[File:Crozet14icip_Introplanehierarchy.png|thumb|x]]
  
| [[File:Crozet14icip_Introplanehierarchy.png|thumb|x]]
 
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! colspan="2" | (d) Hierarchical Segmentation (saliency-based).
  
! colspan="2" | (d) Hierarchical Segmentation (saliency-based).
 
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|-
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| [[File:Crozet14icip_Introplanesegmentationfine.png|thumb‎|x]]
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| [[File:Crozet14icip_Introplanesegmentationfine.png|none|x]]
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| [[File:Crozet14icip_Introplanesegmentationcoarse.png|thumb‎|x]]
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| [[File:Crozet14icip_Introplanesegmentationcoarse.png|none|x]]
 
|-
  
|-
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! colspan="2" | (d') Hierarchical Segmentation (saliency-based).
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! colspan="2" | (d') Hierarchical Segmentation: fine (left), coarse (right).
 
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|}
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'''Fig. 1: Sample uses of the tree of shapes (left column: input images; right column: state-of-the-art results).'''
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'''Fig. 1: Sample uses of the tree of shapes.'''
   
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  ⚫
=== Fig. 2 ===
   
 
{| class="wikitable" border="1"
  
{| class="wikitable" border="1"
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! (b) Tree of shapes
  
! (b) Tree of shapes
 
|}
  
|}
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'''Fig. 2: An image (a) and its tree of shapes (b). The propagation of the level line λ ended, meaning that the nodes O and A have already been visited. The hierarchical queue contains the interior contour of B and C. Thus it can be partitioned in two sets S⁺λ</math> = ∂B and S⁻λ = ∂C. The propagation can proceed on both parts in parallel.'''
⚫
'''Fig. 2'''
  
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=== Fig. 4 ===
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{| class="wikitable" border="1"
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|-
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| [[File:Crozet14icip_Immerse_f.png|90px]]
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| ~ [[File:Crozet14icip_Immerse_f_subdivided‎.png|160px]] ~
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| ~ [[File:Crozet14icip_Immerse_f_immersed‎.png|160px]] ~
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|-
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! (a) Input
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! (b) Subdivided
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! (c) Immersed
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|}
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'''Fig. 4: (a) is the input image. (b) is the result of the subdivision. (c) is the result of the immersion into the Khalimsky grid. 0-faces are represented by dots, 1-faces by segments and 2-faces by squares.'''
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=== Fig.8 ===
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{| class="wikitable" border="1"
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|-
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| [[File:Crozet14icip_Simpleimage_levels.png]]
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| [[File:Crozet14icip_Simpleimage_revalued.png]]
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|-
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! (a) Original image
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! (b) Re-valued image
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|}
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'''Fig. 8: The original image (a) and the associated F^{ord} (b); the max-tree of (b) coincides with the tree of shapes of (a).'''
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=== Fig. 10 ===
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{| class="wikitable" border="1"
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|-
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| [[File:Crozet14icip_Benchwo.png]]
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|}
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'''Fig. 10: Computation times (in seconds) on a classical image test set of the following algorithms: FLLT [3], FLST [23], Géraud et al. [2], and this paper proposal.'''
   
   
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== Images ==
  
== Images ==
 
Images used for the benchmarks: [https://www.dropbox.com/s/ff3gfhiivalcjyz/images.tar.bz2]
  
Images used for the benchmarks: [https://www.dropbox.com/s/ff3gfhiivalcjyz/images.tar.bz2]
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== Code ==
  
== Code ==

Revision as of 12:52, 18 February 2014

Figures

Fig. 1

x x‎
(a) Denoising (self-dual grain removal).
x x
(b) Shape Filtering (keep round objects).
x
x
(c) Object Detection (energy-based method).
x
x
(d) Hierarchical Segmentation (saliency-based).
x
x
(d') Hierarchical Segmentation: fine (left), coarse (right).

Fig. 1: Sample uses of the tree of shapes.


Fig. 2

Crozet14icip simpleimage.png Crozet14icip simpletos.png
(a) Image (b) Tree of shapes

Fig. 2: An image (a) and its tree of shapes (b). The propagation of the level line λ ended, meaning that the nodes O and A have already been visited. The hierarchical queue contains the interior contour of B and C. Thus it can be partitioned in two sets S⁺λ</math> = ∂B and S⁻λ = ∂C. The propagation can proceed on both parts in parallel.


Fig. 4

Crozet14icip Immerse f.png ~ Crozet14icip Immerse f subdivided.png ~ ~ Crozet14icip Immerse f immersed.png ~
(a) Input (b) Subdivided (c) Immersed

Fig. 4: (a) is the input image. (b) is the result of the subdivision. (c) is the result of the immersion into the Khalimsky grid. 0-faces are represented by dots, 1-faces by segments and 2-faces by squares.


Fig.8

Crozet14icip Simpleimage levels.png Crozet14icip Simpleimage revalued.png
(a) Original image (b) Re-valued image

Fig. 8: The original image (a) and the associated F^{ord} (b); the max-tree of (b) coincides with the tree of shapes of (a).


Fig. 10

Crozet14icip Benchwo.png

Fig. 10: Computation times (in seconds) on a classical image test set of the following algorithms: FLLT [3], FLST [23], Géraud et al. [2], and this paper proposal.


Images

Images used for the benchmarks: [1]


Code

  • Code of the serial version: [2]
  • Code of the parallel version: [3]
  • Code of the milena image processing library: [4]
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