Publications/crozet.14.icip.inc

Figures

Fig. 1

Sample uses of the tree of shapes.

(a) Denoising (self-dual grain removal).


(b) Shape Filtering (keep round objects).

(c) Object Detection (energy-based method).
	x
(d) Hierarchical Segmentation (saliency-based): click on the thumbnail (right).

(e) Hierarchical Segmentation: fine (left), coarse (right).

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 = ∂B and S⁻λ = ∂C. The propagation can proceed on both parts in parallel.

(a) Image	(b) Tree of shapes

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.

(a) Input	(b) Subdivided	(c) Immersed
	~ ~	~ ~

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).

(a) Original image	(b) Re-valued image

Fig. 10

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

Images

Images used for the benchmarks: [1]

Source Code

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

Useful links

The Olena platform for image processing: http://olena.lrde.epita.fr (containing the Milena C++ image processing library)

Reproducible research:

Contact

Homepage: http://www.lrde.epita.fr/wiki/User:Theo
Email: thierry.geraud@lrde.epita.fr