Olivier Colot

Olivier Colot
Université de Lille · CRIStAL Centre de Recherche en Informatique, Signal et Automatique de Lille

PhD, HDR

About

93
Publications
7,301
Reads
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1,457
Citations
Additional affiliations
September 2011 - December 2014
ED SPI - University of Lille Nord de France
Position
  • Managing Director
January 2004 - December 2014
Université de Lille
Position
  • Professor (Full) - Deputy director
September 2002 - present
Université de Lille
Position
  • Professor (Full)

Publications

Publications (93)
Chapter
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowin...
Article
We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions i...
Preprint
We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions i...
Conference Paper
Full-text available
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowin...
Article
Full-text available
We evaluated the feasibility of using the kinetic of diffusion-weighted MRI (DWI) and the normalized apparent coefficient diffusion (ADC) map value as an early biomarker in patients treated by external beam radiotherapy (EBRT). Twelve patients were included within the frame of a multicenter phase II trial and treated for intermediate risk prostate...
Preprint
Full-text available
We examine a network of learners which address the same classification task but must learn from different data sets. The learners can share a limited portion of their data sets so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach in which the shared data and the trained m...
Article
Full-text available
Idempotence is a desirable property when cautiousness is wanted in an information fusion process, since in this case combining identical information should not lead to the reinforcement of some hypothesis. Idempotent operators also guarantee that identical information items are not counted twice in the fusion process, a very important property in d...
Chapter
In this paper, a new technique based on interval-valued fuzzy sets and scale-space smoothing is proposed for image analysis and restoration. Interval-valued fuzzy sets (IVFS) are associated with type-2 semantic uncertainty that makes it possible to take into account usually ignored (or difficult to manage) stochastic errors during image acquisition...
Conference Paper
When combining multiple belief functions, designing a combination rule that selects the least informative belief function among those more informative than each of the combined ones is a difficult task. Such rules, commonly depicted as “cautious”, are typically required to be idempotent, since when one is cautious, combining identical information s...
Article
Full-text available
Distances between mass functions are instrumental tools in evidence theory, yet it is not always clear in which situation a particular distance should be used. Indeed, while the mathematical properties of distances have been well studied, how to interpret them is still a largely open issue. As a step towards answering this question, we propose to i...
Article
Models based on interval-valued fuzzy sets make it possible to manage numerical and spatial uncertainty in grey-scale values of image pixels. In a recent paper, we proposed a method that links the ultrafuzziness index (that makes it possible to take into account some uncertainty, like noise, and inherent to image capture) with impulse noise removal...
Article
Full-text available
As part of the theory of belief functions, we address the problem of appraising the similarity between bodies of evidence in a relevant way using metrics. Such metrics are called evidential distances and must be computed from mathematical objects depicting the information inside bodies of evidence. Specialization matrices are such objects and, ther...
Article
Full-text available
In this paper we introduce an evidential multi-source segmentation scheme for the extraction of prostate zonal anatomy using multi-parametric MRI. The Evidential C-Means (ECM) classifier was adapted to a segmentation scheme by introducing spatial neighbourhood-based relaxation step in its optimisation process. In order to do so, basic belief assign...
Article
In this paper we introduce an evidential multi-source segmentation scheme for the extraction of prostate zonal anatomy using multi-parametric MRI. The Evidential C-Means (ECM) classifier was adapted to a segmentation scheme by introducing spatial neighbourhood-based relaxation step in its optimisation process. In order to do so, basic belief assign...
Conference Paper
Full-text available
In a recent paper [12], we introduced a new family of evidential distances in the framework of belief functions. Using specialization matrices as a representation of bodies of evidence, an evidential distance can be obtained by computing the norm of the difference of these matrices. Any matrix norm can be thus used to define a full metric. In parti...
Article
Full-text available
Distances in evidence theory are useful tools for belief function approximation or clustering. Efficient approaches are found in the literature, especially full metrics taking focal element interactions into account. In this paper, another aspect is investigated: the ability to detect common evidence pertaining to two different states of belief. Th...
Article
Full-text available
A mammogram is the standard modality used for breast cancer screening. Computer-aided detection (CAD) approaches are helpful for improving breast cancer detection rates when applied to mammograms. However, automated analysis of a mammogram often leads to inaccurate results in the presence of the pectoral muscle. Therefore, it is necessary to first...
Article
Full-text available
Grouping 3D objects into (semantically) meaningful categories is a challenging and important problem in 3D mining and shape processing. Here, we present a novel approach to categorize 3D objects. The method described in this article, is a belief-function-based approach and consists of two stages: the training stage, where 3D objects in the same cat...
Conference Paper
Fuzzy sets which capture the meaning representation of linguistic variables have been widely used in image processing in the last decades. Fuzzy sets are associated with vagueness which is type 1 uncertainty. Interval-valued fuzzy sets (IVFS) are associated with type 2 semantic uncertainty. Indeed, the length of the interval provides the "'non-spec...
Article
A novel method for three-dimensional (3-D) shape retrieval using bag-of-feature techniques (BoF) is proposed. This method is based on vector quantization of invariant descriptors of 3-D object patches. Firstly, it starts by selecting and then describing a set of points from the 3-D object. Such descriptors have the advantage of being invariant to d...
Article
Singular sources mining is essential in many applications like sensor fusion or dataset analysis. A singular source of information provides pieces of evidence that are significantly different from the majority of the other sources. In the Dempster–Shafer theory, the pieces of evidence collected by a source are summarized by basic belief assignments...
Article
Multimodality image registration is a critical issue in image-guided cancer ablation techniques. Focal therapies of prostate cancer are usually monitored using ultrasound imaging, while the dose planning is performed on MRI. In this study, a new multimodality images registration and deformation method, based on the Thin Plate Splines-Robust Point M...
Article
To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland. The proposed method is based on a modified version of the evidential C-means clustering algorithm. The evidential C-means optimization process was modified to introd...
Article
Recent progress in magnetic resonance imaging (MRI) has enabled new prostate cancer diagnosis techniques. The newest challenges in this field are to enhance image-based tumours detection. In such a context, the extraction of prostate's contours is a crucial step in the interpretation of MR images, and is usually carried out by an expert radiologist...
Conference Paper
Full-text available
In this paper, we present a new method for 3D-shape catego- rization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D- object patches. We analyze the performance of two well- known classifiers: the NaBayes and the SVM. The re- sults show the effectiveness of our approach and prove tha...
Article
Full-text available
We present a novel method for 3D-shape matching using Bag-of-Feature techniques (BoF). The method starts by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form...
Article
Full-text available
The 3D-shape matching problem plays a crucial role in many applications, such as indexing or modeling, by example. Here, we present a novel approach to matching 3D objects in the presence of nonrigid transformation and partially similar models. In this paper, we use the representation of surfaces by 3D curves extracted around feature points. Indeed...
Article
Full-text available
We present a novel method for 3D-object retrieval using Bag of Feature (BoF) approaches [8]. The method starts by selecting and then describing a set of points from the 3D-object. The proposed descriptor is an indexed collection of closed curves in R3 on the 3D-surface. Such descriptor has the advantage of being invariant to different transformatio...
Article
Multimodality image registration is a critical issue in image-guided cancer ablation techniques. Focal therapies of prostate cancer are usually monitored using ultrasound imaging, while the dose planning is performed on MRI. In this study, a new multimodality images registration and deformation method, based on the Thin Plate Splines -Rigid Point M...
Article
A new fuzzy image filter controlled by interval-valued fuzzy sets (IVFS) is proposed for removing noise from images. The proposed approach is based on IVFS entropy application. IVFS makes it possible to take into account the total uncertainty inherent to image processing, and particularly noise removal is considered. Interval-valued fuzzy sets entr...
Article
Full-text available
Accurate localization and contouring of prostate are crucial issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. We introduce a new method for automatic 3D segmentation of the prostate gland from magnetic resonance im...
Conference Paper
Full-text available
In this paper, we establish a link between belief functions on real numbers and the maximal coherent sets obtained in the framework of possibilistic distributions. Having proposed an original disjunctive rule of combination in the framework of continuous belief functions, we demonstrate theoretically that maximal coherent sets can be viewed as a pa...
Conference Paper
This paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. A statistical geometric model is used as a priori knowledge. Prostate boundaries are then optimized by a Bayesian classification based on Markov fields modelling. We compared the accuracy of this algorithm, free from an...
Article
Accurate localization and contouring of prostate are important issues in prostate cancer diagnosis and/or therapies. Although several semi-automatic and automatic segmentation methods have been proposed, manual expert correction remains necessary. Our paper introduces an original method for automatic 3D segmentation of the prostate gland from Magne...
Conference Paper
This paper focuses on application of fuzzy sets of type 2 (FS2) in color images segmentation. The proposed approach is based on FS2 entropy application and region merging. Both local and global information of the image are employed and FS2 makes it possible to take into account the total uncertainty inherent to the segmentation operation. Fuzzy ent...
Conference Paper
Full-text available
This paper focuses on application of fuzzy sets of type 2 in color images segmentation. It is well-known that images segmentation is one of the most difficult low-level image analysis tasks. Many disturbing factors, or image vagueness may corrupt this task. So we propose a new color image segmentation scheme based on type-2 fuzzy sets, that allows...
Chapter
Full-text available
This paper presents a similarity-based adaptive neighborhood (SBAN) dense stereovision algorithm which uses color for comparing pixels. In SBAN methods, the neighbor pixels which are not similar to the central one are excluded of the window when computing the correlation index, which corresponds to adapting the equivalent size and shape of the corr...
Chapter
In this paper, we propose a method dedicated to classification between benign and malignant lesions in Dermatology in the aim to help the clinicians for melanoma diagnosis. The proposed methodology reduces the very numerous informations contained in the digitized images to a finite set of parameters giving a description of the colour and the shape...
Conference Paper
Full-text available
In this paper, we propose a color image quantization algorithm based upon TBM. In this context, we consider that the color quantization problem can be viewed as clustering problem of the color-space into P clusters. Using TBM, we define a top-down evidential clustering algorithm which iteratively decreases the number of clusters of the color space...
Conference Paper
Full-text available
This paper is concerned with the use of belief theory to resolve the data association problem in the context of tracking and identifying people using audio and video data. In order to associate measurements with targets, the proposed method exploits different features such as color, position and acoustic parameters. This has the advantage of provid...
Conference Paper
Full-text available
When associating data in the context of multiple target tracking, one is faced with the problem of handling the target emergence and disappearance. In this paper we show that we are able to handle this issue using belief theory based data association method without the introduction of an additional hypothesis to the frame of discernment. Using a sp...
Conference Paper
Full-text available
In this paper we propose a method for solving the data association problem within the framework of multi-target tracking, given a set of environmental measurements obtained by complementary and redundant sensors. The proposed method exploits belief theory, which is a powerful tool for handling imperfect data. We applied the method to situations whe...
Article
Nous proposons un algorithme de quantification des images couleur fondé sur la théorie de l'évidence. Dans ce cadre, le problème de quantification est vu comme un problème de classification non supervisée dans l'espace couleur. Partant d'un nombre de classes égal au nombre de couleurs de l'image originale, l'algorithme de classification crédibilist...
Conference Paper
Full-text available
In this paper, we present a new method for dense stereo matching. In area-based methods, the similarity between one pixel of the left image and one pixel of the right image is measured using a correlation index computed on neighborhoods of these pixels. In our method, the neighbor pixels not similar to the center one are excluded when computing the...
Article
In this paper we propose and study an evidential segmentation scheme of multi-echo MR images for the detection of brain tumors. We show that the modeling by means of evidence theory is well suited to the processing of redundant and complementary data as the MR images. Moreover neighborhood relationship between voxels is taken into account via Demps...
Article
This paper presents an evidential segmentation scheme of multi-echoes magnetic resonance (MR) images for the detection of brain tumors. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. In our approach, the neighborhood relationship between the voxels are ta...
Article
Full-text available
This paper presents an evidential segmentation scheme of multi-echoes magnetic resonance (MR) images for the detection of brain tumors. The segmentation is based on the mo-deling of the data by evidence theory which is well suited to re-present such uncertain and imprecise data. In our approach, the neighborhood relationship between the voxels are...
Conference Paper
We propose an original scheme for the D segmentation of multi-echo MR brain images into white matter, gray matter and cerebro-spinal fluid. To take into account complementary, redundancy and eventual conflicts provided by the different echoes, a fusion process based on Evidence theory is used. Such theory, well suited to imprecise and uncertain dat...
Conference Paper
We propose a method to introduce spatial information within the context of pattern recognition by the mean of evidence theory. Indeed, we can consider that each neighbor brings some information useful to determined the class of a pattern to classify. We propose to introduce such information through the Dempster's (1967) combination rule. This combi...
Article
This paper aims to present a complete methodology based on a multidisciplinary approach, that combines the extraction of low-level features to describe images in a high-level concept or formalism dedicated to Computer-Aided Categorization of ornamental stones (granite, marble). The problem is resolved thanks to a Content-Based Image Retrieval schem...
Article
Full-text available
Résumé Dans cette contribution, nous nous sommes intéressé a la modélisation des connaissances et du savoir-faire des experts dans l'optique d'indexation par le contenu d'une base d'images de roches ornementales. Cette application estt es intéressante pour l'aidè a la classification des roches afin de forme a la sor-tie descarrì eres, des lotshomo...
Article
Within the framework of evidence theory, data fusion consists in obtaining a single belief function by the combination of several belief functions resulting from distinct information sources. The most popular rule of combination, called Dempster's rule of combination (or the orthogonal sum), has several interesting mathematical properties such as c...
Conference Paper
Full-text available
Information fusion introduces special operators o in probability theory and fuzzy theory. Some serious data certify in each case these two quite distinct techniques. The article shows that four postulates are the unique aim of these two theories. Evidence theory and fuzzy set theory often replace probabilities in medicine, economy and control. Fuzz...
Conference Paper
This paper is dedicated to Computer-Aided Diagnosis CAD for skin cancers in order to help the expert (dermatologist) to diagnose a dermatological lesion as benign or malignant. The need of this kind of tools has largely expressed because of the difficulties that have the expert to distinguish benign lesion from melanoma. One way to help him without...
Conference Paper
In this paper, we propose a segmentation scheme for magnetic resonance (MR) images based on a two step algorithm. The first step consists of a classification based on an evidential k-NN rule initially proposed by Denoeux (1995). The second step allows to take into account the spatial dependence of each voxel of the MR volume in order to lead the se...
Article
This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very differ...
Conference Paper
Full-text available
Using graphical models to represent independence structure in multivariate probability model has been studied since a few years. In this framework, Bayesian networks have been proposed as an interesting approach for uncertain reasoning. Within the framework of pattern recognition, many methods of classification were developed based on statistical d...
Conference Paper
Full-text available
Within the framework of Dempster-Shafer theory of evidence, the data fusion is based on the building of single belief mass by combination of several mass functions resulting from distinct information sources. This combination called Dempster's combination rule (or orthogonal sum) has several interesting mathematical properties like commutativity or...