@inproceedings{GBD09,
author = {Romain {Goffe} and Luc {Brun} and Guillaume {Damiand}},
title = {A top-down construction scheme for irregular pyramids.},
booktitle = {Fourth International Conference On Computer Vision Theory And Applications (VISAPP'09)},
volume = {1},
pages = {163--170},
month = {February},
year = {2009},
editor = {AlpeshKumar Ranchordas and Helder Araujo},
keywords = {Segmentation; Irregular pyramid; Topological model; Combinatorial map;},
pdf = {GoffeAl09-VISAPP.pdf},
slides = {GoffeAl09-VISAPP-slides.pdf},
abstract = {
Hierarchical data structures such as irregular pyramids are used by
many applications related to image processing and segmentation. The
construction scheme of such pyramids is bottom-up. Such a scheme
forbids the definition of a level according to more global
information defined at upper levels in the hierarchy. Moreover, the
base of the pyramid has to encode any single pixel of the initial
image in order to allow the definition of regions of any shape at
higher levels. This last constraint raises major issues of memory
usage and processing costs when irregular pyramids are applied to
large images. The objective of this paper is to define a top-down
construction scheme for irregular pyramids. Each level of such a
pyramid is encoded by a combinatorial map associated to an explicit
encoding of the geometry and the inclusion relationships of the
corresponding partition. The resulting structure is a stack of
finer and finer partitions obtained by successive splitting
operations and is called a top-down pyramid.
}
}
@inproceedings{GDB09,
author = {Romain {Goffe} and Guillaume {Damiand} and Luc {Brun}},
title = {Extraction of tiled top-down irregular pyramids from large images.},
booktitle = {13th International Workshop on Combinatorial Image Analysis (IWCIA'09)},
series = {Research Publishing Services},
publisher = {RPS, Singapore},
pages = {123--137},
month = {November},
year = {2009},
editor = {Petra {Wiederhold} and Reneta P. {Barneva}},
keywords = {Irregular pyramid; Topological model; Tiled data structure; Combinatorial map;},
pdf = {GoffeAl09-IWCIA.pdf},
slides = {GoffeAl09-IWCIA-slides.pdf},
abstract = {
Processing large images is a common issue in the computer vision
framework with applications such as satellite or microscopic images.
The major problem comes from the size of those images that prevents
them from being loaded globally into memory. Moreover, such images
contain different information at different levels of resolution. For
example, global features, such as the delimitation of a tissue,
appear at low resolution whereas finer details, such as cells, can
only be distinguished at full resolution. Thus, the objective of
this paper is the definition of a suitable hierarchical data
structure that would provide full access to all the properties of
the image by representing topological information. The idea consists
in transposing the notion of tile for top-down topological pyramids
to control accurately the amount of memory required by the
construction of our model. As a result, this paper defines the
topological model of tiled top-down pyramid and proposes a
construction scheme that would not depend on the system memory
limitations.
}
}
@inproceedings{GDB10,
author = {Romain {Goffe} and Guillaume {Damiand} and Luc {Brun}},
title = {A causal extraction scheme in top-down pyramids for large images segmentation},
booktitle = {In 13th International Workshop On Structural and Syntactic Pattern Recognition (SSPR'10)},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {6218},
pages = {264--274},
year = {2010},
month = {August},
pdf = {GoffeAl10-SSPR.pdf},
poster = {GoffeAl10-SSPR-poster.pdf},
keywords = {Irregular pyramid; Topological model; Tiled data structure; Combinatorial map;},
abstract = {
Applicative fields based on the analysis of large images must deal
with two important problems. First, the size in memory of such
images usually forbids a global image analysis hereby inducing
numerous problems for the design of a global image
partition. Second, due to the high resolution of such images, global
features only appear at low resolutions and a single resolution
analysis may loose important information. The tiled top-down
pyramidal model has been designed to solve this two major
challenges. This model provides a hierarchical encoding of the image
at single or multiple resolutions using a top-down construction
scheme. Moreover, the use of tiles bounds the amount of memory
required by the model while allowing global image analysis. The main
limitation of this model is the splitting step used to build one
additional partition from the above level. Indeed, this step
requires to temporary refine the split region up to the pixel level
which entails high memory requirements and processing time. In
this paper, we propose a new splitting step within the tiled
top-down pyramidal framework which overcomes the previously
mentioned limitations.
}
}
@inproceedings{GBD11b,
author = {Romain {Goffe} and Luc {Brun} and Guillaume {Damiand}},
title = {Tiled top-down pyramids and segmentation of large histological images},
booktitle = {In 8th IAPR - TC-15 Workshop on Graph-based Representations in Pattern Recognition (GBR'11)},
publisher = {Springer},
editor = {Xiaoyi {Jiang} and Miquel {Ferrer} and Andrea {Torsello}},
series = {Lecture Notes in Computer Science},
volume = {6658},
pages = {255--264},
year = {2011},
month = {May},
pdf = {GoffeAl11-GBR.pdf},
poster = {GoffeAl11-GBR-poster.pdf},
keywords = {Irregular pyramid; Topological model; Combinatorial map;},
abstract = {
Recent microscopic imaging systems such as whole slide scanners
provide very large (up to 18GB) high resolution images. Such amounts
of memory raise major issues that prevent usual image representation
models from being used. Moreover, using such high resolution images,
global image features, such as tissues, does not clearly appear at
full resolution. Such images contain thus different hierarchical
information at different resolutions. This paper presents the model
of tiled top-down pyramids which provides a framework to handle such
images. This model encodes a hierarchy of partitions of large images
defined at different resolutions. We also propose a generic
construction scheme of such pyramids whose validity is evaluated on
an histological image application.
}
}
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