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Publications about Colour
Result of the query in the list of publications :
Article |
1 - A study of Gaussian mixture models of colour and texture features for image classification and segmentation.
H. Permuter and J.M. Francos and I. H. Jermyn. Pattern Recognition, 39(4): pages 695--706, April 2006.
Keywords : Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion.
@ARTICLE{permuter_pr06,
|
| author |
= |
{Permuter, H. and Francos, J.M. and Jermyn, I. H.}, |
| title |
= |
{A study of Gaussian mixture models of colour and texture features for image classification and segmentation}, |
| year |
= |
{2006}, |
| month |
= |
{April}, |
| journal |
= |
{Pattern Recognition}, |
| volume |
= |
{39}, |
| number |
= |
{4}, |
| pages |
= |
{695--706}, |
| url |
= |
{http://dx.doi.org/10.1016/j.patcog.2005.10.028}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2006_permuter_pr06.pdf}, |
| keyword |
= |
{Classification, Segmentation, Texture, Colour, Gaussian mixture, Decison fusion} |
| } |
Abstract :
The aims of this paper are two-fold: to define Gaussian mixture models of coloured texture on several feature paces and to compare the performance of these models
in various classification tasks, both with each other and with other models popular in the literature. We construct Gaussian mixtures models over a variety of different colour and texture feature spaces, with a view to the retrieval of textured colour images from databases. We compare supervised classification results for different choices of colour and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'colour' and 'structure' information in order to improve the classification performance. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance. |
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2 Conference articles |
1 - A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction.
P. Horvath. In Proc. Advanced Concepts for Intelligent Vision Systems, Delft, Netherlands, August 2007.
Keywords : Higher-order, Tree Crown Extraction, Colour.
@INPROCEEDINGS{Horvath07c,
|
| author |
= |
{Horvath, P.}, |
| title |
= |
{A Multispectral Data Model for Higher-Order Active Contours and its Application to Tree Crown Extraction}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. Advanced Concepts for Intelligent Vision Systems}, |
| address |
= |
{Delft, Netherlands}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Horvath07c.pdf}, |
| keyword |
= |
{Higher-order, Tree Crown Extraction, Colour} |
| } |
Abstract :
Forestry management makes great use of statistics concerning the
individual trees making up a forest, but the acquisition of this
information is expensive. Image processing can potentially both
reduce this cost and improve the statistics. The key problem is the
delineation of tree crowns in aerial images. The automatic solution
of this problem requires considerable prior information to be built
into the image and region models. Our previous work has focused on
including shape information in the region model; in this paper we
examine the image model. The aerial images involved have three
bands. We study the statistics of these bands, and construct both
multispectral and single band image models. We combine these with a
higher-order active contour model of a `gas of circles' in order to
include prior shape information about the region occupied by the
tree crowns in the image domain. We compare the results produced by
these models on real aerial images and conclude that multiple bands
improves the quality of the segmentation. The model has many other
potential applications, e.g. to nano-technology, microbiology,
physics, and medical imaging.
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2 - Unsupervised Image Segmentation via Markov Trees and Complex Wavelets.
C. Shaffrey and N. Kingsbury and I. H. Jermyn. In Proc. IEEE International Conference on Image Processing (ICIP), Rochester, USA, September 2002.
Keywords : Segmentation, Hidden Markov Model, Texture, Colour.
@INPROCEEDINGS{ijking,
|
| author |
= |
{Shaffrey, C. and Kingsbury, N. and Jermyn, I. H.}, |
| title |
= |
{Unsupervised Image Segmentation via Markov Trees and Complex Wavelets}, |
| year |
= |
{2002}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Rochester, USA}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Shaffrey02icip.pdf}, |
| keyword |
= |
{Segmentation, Hidden Markov Model, Texture, Colour} |
| } |
Abstract :
The goal in image segmentation is to label pixels in an image based
on the properties of each pixel and its surrounding region. Recently
Content-Based Image Retrieval (CBIR) has emerged as an
application area in which retrieval is attempted by trying to gain
unsupervised access to the image semantics directly rather than
via manual annotation. To this end, we present an unsupervised
segmentation technique in which colour and texture models are
learned from the image prior to segmentation, and whose output
(including the models) may subsequently be used as a content
descriptor in a CBIR system. These models are obtained in a
multiresolution setting in which Hidden Markov Trees (HMT) are
used to model the key statistical properties exhibited by complex
wavelet and scaling function coefficients. The unsupervised Mean
Shift Iteration (MSI) procedure is used to determine a number of
image regions which are then used to train the models for each
segmentation class. |
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