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  • 1.
    Baudrier, Etienne
    et al.
    LSIIT, Illkirch, France.
    Busson, Sébastien
    CESR, Tours, France.
    Corsini, Silvio
    BCU, Lausanne, Switzerland.
    Delalandre, Mathieu
    CVC, Barcelona, Spain.
    Landré, Jérôme
    CReSTIC, Troyes, France.
    Morain-Nicolier, Frédéric
    CReSTIC, Troyes, France.
    Retrieval of the ornaments from the Hand-Press Period: An overview2009In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, IEEE, 2009, p. 496-500Conference paper (Refereed)
    Abstract [en]

    This paper deals with the topic of the retrieval of document images focused on a specific application: the ornaments of the Hand-Press period. It presents an overview as a result of the work and the discussions undertaken by a workgroup on this subject. The paper starts by giving a general view about digital libraries of ornaments and associated retrieval problematics. Two main issues are underlined: content based image retrieval (CBIR) and image difference visualization. Several contributions are summarized, commented and compared. Conclusions and open problems arising from this overview are twofold: 1. contributions on CBIR miss scale-invariant methods and don't provide significative evaluation results. 2. robust registration is the open problem for visual comparison.

  • 2.
    Diaw, Moustapha
    et al.
    CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France.
    Delahaies, Agnes
    CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France.
    Landré, Jérôme
    CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France.
    Retraint, Florent
    LIST3N, University of Technology of Troyes (UTT), Troyes, France.
    Morain-Nicolier, Frederic
    CReSTIC, Ea 3804, University of Reims Champagne-Ardenne, Troyes, France.
    Modeling a Local Dissimilarity Map With Weibull Distribution-Application to 2-Class and Multi-Class Image Classification2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 35750-35767Article in journal (Refereed)
    Abstract [en]

    Due to the considerable increase of images in everyday life, many applications require a study on their similarity. The main challenge is to find a simple and efficient method to compare and classify image pairs into similar and dissimilar classes. This study presents a new method to image pairs comparison and classification based on the modeling of the Local Dissimilarity Map (LDM). The LDM is a tool for locally measuring the dissimilarity between two binary or grayscale images. It is a measure of dissimilarities based on a modified version of the Hausdorff distance, which allows quantifying locally the dissimilarities between images. This measure is completely without parameters and generic. The image pairs classification (2-class classification) method is structured as follows. First, a statistical model for the LDM is proposed. The model parameters, used as descriptors, are relevant to discriminate similar and dissimilar image pairs. Second, classifiers are applied to compute the classification scores (2-class classification problem). In addition, this approach is robust with respect to geometric transformations such as translation compared to the state-of-the-art similarity measures. Although the main objective of this paper is to apply our approach to image pairs classification, it is also performed on a classification with more than two classes (multi-class classification). Experiments on the well-known image data sets NIST and on old print data set prove that the proposed method produces comparable, even better results than the state-of-the-art methods in terms of accuracy and F(1) score.

  • 3.
    Diaw, Moustapha
    et al.
    CReSTIC, EA 3804, Reims, France; Univ. de Reims Champagne-Ardenne, IUT, Troyes, France.
    Delahaies, Agnès
    CReSTIC, EA 3804, Reims, France; Univ. de Reims Champagne-Ardenne, IUT, Troyes, France.
    Landré, Jérôme
    CReSTIC, EA 3804, Reims, France; Univ. de Reims Champagne-Ardenne, IUT, Troyes, France.
    Morain-Nicolier, Frédéric
    CReSTIC, EA 3804, Reims, France; Univ. de Reims Champagne-Ardenne, IUT, Troyes, France.
    Retraint, Florent
    ICD-LM2S, Univ. de Technologie de Troyes, Troyes, France.
    Fast process for classifying structural image pairs using Weibull parameters extracted from undersampled Local Dissimilarity Maps2021In: 2021 29th European Signal Processing Conference (EUSIPCO), European Signal Processing Conference, EUSIPCO , 2021, p. 631-635Conference paper (Refereed)
    Abstract [en]

    In previous works, the Local Dissimilarity Map (LDM) was proposed to compare two binary and grayscale images. This measure is based on a Hausdorff distance, which allows to quantify locally the dissimilarities between images. In this paper, we proposed the two-parameter Weibull distribution to model the LDM and the undersampled LDMs for two structural images. To classify structural image pairs, we used the two parameters of Weibull distribution for each LDM as descriptors. They are relevant to discriminate image pairs into similar and dissimilar classes. Experiments were made on the MNIST image dataset and in our own old print image dataset. The results shown our approach is more accurate than the other measures in the literature.

  • 4.
    Diaw, Moustapha
    et al.
    University of Reims Champagne-Ardenne, Reims, France; IUT Troyes, Troyes, Franc.
    Landré, Jérôme
    University of Reims Champagne-Ardenne, Reims, France; IUT Troyes, Troyes, France.
    Delahaies, Agnès
    University of Reims Champagne-Ardenne, Reims, France; IUT Troyes, Troyes, France .
    Morain-Nicolier, Frédéric
    University of Reims Champagne-Ardenne, Reims, France; IUT Troyes, Troyes, France.
    Retraint, Florent
    University of Technology of Troyes (UTT), LIST3N, Troyes, France.
    Optical Aerial Images Change Detection Based on a Color Local Dissimilarity Map and k-Means Clustering2022In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 19, article id 6517705Article in journal (Refereed)
    Abstract [en]

    Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: mutual information (MI), disjoint information (DI), and local dissimilarity map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using, respectively, the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k-means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of recall, precision, and F-measure.

  • 5.
    Diaw, Moustapha
    et al.
    CReSTIC, EA 3804, University of Reims Champagne-Ardenne, IUT, Troyes, France.
    Landré, Jérôme
    CReSTIC, EA 3804, University of Reims Champagne-Ardenne, IUT, Troyes, France.
    Delahaies, Agnès
    CReSTIC, EA 3804, University of Reims Champagne-Ardenne, IUT, Troyes, France.
    Morain-Nicolier, Frédéric
    CReSTIC, EA 3804, University of Reims Champagne-Ardenne, IUT, Troyes, France.
    Retraint, Florent
    LIST3N, University of Technology of Troyes, UTT, France.
    Satellite Image Change Detection Using Disjoint Information And Local Dissimilarity Map2022In: Proceedings - International Conference on Image Processing, ICIP, IEEE, 2022, p. 36-40Conference paper (Refereed)
    Abstract [en]

    This paper presents a new change detection technique for images taken from the sentinel-2 satellite between 2015 and 2018 in different regions of the world. These images are widely used in recent years for change detection. This technique is based on two dissimilarity measures: the Disjoint Information and the Local Dissimilarity Map. The disjoint information quantifies the dissimilarities between textures and the Local Dissimilarity Map those between structures of images. Firstly, the disjoint information is computed across the blocks of the RGB image channels and the value is multiplied by the center value of the pixel of each block. Secondly, the Local Dissimilarity Maps over the pre-processed channels and the average of the pixel values on the Local Dissimilarity Maps are computed. Finally, an extension of the Gaussian OTSU's threshold is used to detect changes in images. Experimental results on the well-known Onera Satellite Change Detection (OSCD) dataset show the effectiveness of our proposed method compared to the state-of-the-art deep learning methods.

  • 6.
    Ech-Choudany, Y.
    et al.
    LSE2I Laboratory, National School of Applied Sciences, First Mohammed University, B.P. 669, Complexe Universitaire Hay elqods, Oujda, Morocco; CReSTIC Laboratory, Université de Reims Champagne-Ardenne, IUT de Troyes, Troyes Cedex, France.
    Scida, D.
    LISM Laboratory, Université de Reims Champagne-Ardenne, IUT de Troyes, Troyes Cedex, France.
    Assarar, M.
    LISM Laboratory, Université de Reims Champagne-Ardenne, IUT de Troyes, Troyes Cedex, France.
    Landré, Jérôme
    CReSTIC Laboratory, Université de Reims Champagne-Ardenne, IUT de Troyes, Troyes Cedex, France.
    Bellach, B.
    LSE2I Laboratory, National School of Applied Sciences, First Mohammed University, B.P. 669, Complexe Universitaire Hay elqods, Oujda, Morocco.
    Morain-Nicolier, F.
    CReSTIC Laboratory, Université de Reims Champagne-Ardenne, IUT de Troyes, Troyes Cedex, France.
    Dissimilarity-based time–frequency distributions as features for epileptic EEG signal classification2021In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 64, article id 102268Article in journal (Refereed)
    Abstract [en]

    This work aims at exploring a general framework embedding techniques from classifiers, Time–Frequency Distributions (TFD) and dissimilarity measures for epileptic seizures detection. The proposed approach consists firstly in computing dissimilarities between TFD of electroencephalogram (EEG) signals and secondly in using them to define a decision rule. Compared to the existing approaches, the proposed one uses entire TFD of EEG signals and does not require arbitrary feature extraction. Several dissimilarity measures and TFDs have been compared to select the most appropriate for EEG signals. Classifiers, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Linear Discriminate Analysis (LDA) and k-Nearest Neighbours (k-NN), have been combined with the proposed approach. In order to evaluate the proposed approach, 13 different classification problems (including 2, 3 and 5-class) pertaining to five types of EEG signals have been used. The comparison between results obtained with the proposed approach and results reported in the literature with the same database of epileptic EEG signals demonstrates the effectiveness of this approach for seizure detection. Experimental results show that this approach has achieved highest accuracy in the most studied classification problems. A high value of 98% is achieved for the 5-class problem. Further, in most classification problems with 2 and 3-class, it also yields a satisfactory accuracy of approximately 100%. The robustness of the proposed approach is evaluated with the addition of noise to the EEG signals at various signal-to-noise ratios (SNRs). The experimental results show that this approach has a good classification accuracy at low SNRs.

  • 7.
    Guo, Yu
    et al.
    CReSTIC, IUT de Troyes, France.
    Ruan, Su
    CReSTIC, IUT de Troyes, France.
    Landré, Jérôme
    CReSTIC, IUT de Troyes, France.
    Constans, Jean-Marc
    CHU de Caen, France.
    A priori knowledge based frequency-domain quantification of Magnetic Resonance Spectroscopy2009In: Proceedings of the 7th IFAC Symposium onModelling and Control in Biomedical Systems, Aalborg, Denmark, August 12-14, 2009, IFAC Secretariat , 2009, no PART 1, p. 210-215Conference paper (Refereed)
    Abstract [en]

    Because of the overlapping of the spectra of different metabolites and the interference of the baseline mainly from broad resonances of macromolecule and lipids, it is difficult to achieve the quantification of spectra of different metabolites which is important for both research and clinical applications of Magnetic Resonance Spectroscopy (MRS). In this paper, a novel MRS quantification method based on frequency a priori knowledge is proposed. Firstly, a wavelet filter is used to remove the broad components of an observed spectrum in which baseline and the relatively broad components of metabolite spectrum are included. Secondly, a linear nonnegative pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is used to decompose the filtered spectra in a dictionary which is based on a set of Lorentzian and Gaussian functions corresponding to spectrum models. Benefitting from the a priori knowledge of the peak frequency of each metabolite, the filtered metabolite spectrum can be sparsely represented with these basis functions and the spectra of different metabolites are relevant to certain basis functions. Therefore, with the corresponding relation between the basis functions and spectrum models and the estimated decomposition coefficients, a mixed spectrum without baseline can be reconstructed and spectra of different metabolites can be quantified at the same time. The accuracy and the robustness of MRS quantification are improved by the proposed method, from simulation data, compared with commonly used MRS quantification methods. Quantification on in vivo brain spectra is also demonstrated.

  • 8.
    Guo, Yu
    et al.
    Centre de Recherche en Sciences et Technologies de l'Information et de la Communication, Université de Reims Champagne-Ardenne, Troyes, France.
    Ruan, Su
    Centre de Recherche en Sciences et Technologies de l'Information et de la Communication, Université de Reims Champagne-Ardenne, Troyes, France.
    Landré, Jérôme
    Centre de Recherche en Sciences et Technologies de l'Information et de la Communication, Université de Reims Champagne-Ardenne, Troyes, France.
    Constans, Jean-Marc
    CHU de Caen, Unité d'Irm, Caen cedex, France.
    A sparse representation method for magnetic resonance spectroscopy quantification2010In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 57, no 7, p. 1620-1627, article id 5464359Article in journal (Refereed)
    Abstract [en]

    In this paper, a sparse representation method is proposed for magnetic resonance spectroscopy (MRS) quantification. An observed MR spectrum is composed of a set of metabolic spectra of interest, a baseline and a noise. To separate the spectra of interest, the a priori knowledge about these spectra, such as signal models, the peak frequencies, and linewidth ranges of different resonances, is first integrated to construct a dictionary. The separation of the spectra of interest is then performed by using a pursuit algorithm to find their sparse representations with respect to the dictionary. For the challenging baseline problem, a wavelet filter is proposed to filter the smooth and broad components of both the observed spectra and the basis functions in the dictionary. The computation of sparse representation can then be carried out by using the remaining data. Simulation results show the good performance of this wavelet filtering-based strategy in separating the overlapping components between the baselines and the spectra of interest, when no appropriate model function for the baseline is available. Quantifications of in vivo brain MR spectra from tumor patients in different stages of progression demonstrate the effectiveness of the proposed method.

  • 9.
    Guo, Yu
    et al.
    CReSTIC, University of Reims, IUT de Troyes, Troyes Cedex, France.
    Ruan, Su
    CReSTIC, University of Reims, IUT de Troyes, Troyes Cedex, France.
    Landré, Jérôme
    CReSTIC, University of Reims, IUT de Troyes, Troyes Cedex, France.
    Walker, Paul
    LE2I, UMR CNRS 5158, University of Bourgogne, Dijon, France.
    A priori knowledge based frequency-domain quantification of prostate Magnetic Resonance Spectroscopy2011In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 6, no 1, p. 13-20Article in journal (Refereed)
    Abstract [en]

    This paper proposes a frequency-domain Magnetic Resonance (MR) spectral processing method based on sparse representation for accurate quantification of prostate spectra. Generally, an observed prostate spectrum can be considered as a mixture of resonances of interest, a baseline and noise. As the resonances of interest often overlap and the baseline is unknown, their separation and quantification can be difficult. In the proposed method, based on the commonly used signal model of prostate spectra and some a priori knowledge of nonlinear model parameters, a dictionary is constructed which can sparsely represent the resonances of interest as well as the baseline in an input spectrum. The estimation of the resonances of interest is achieved by finding their sparse representations with respect this dictionary. A linear pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is proposed to estimate these sparse representations. The robustness and accuracy of prostate spectrum quantification of the proposed method are improved compared with two classical spectral processing methods: model-based time domain fitting and frequency-domain analysis based on peak integration when tested on simulation data. Quantification on in vivo prostate spectra is also demonstrated and the results appear encouraging.

  • 10.
    Guo, Yu
    et al.
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France.
    Ruan, Su
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France.
    Landré, Jérôme
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France.
    Zhang, Yu
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France.
    Ming, Xin
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France.
    Feng, Yuanming
    Univ. Reims-Champagne-Ardenne, CReSTIC IUT, Troyes Cedex, France; Radiation Oncology Department, TJMU Cancer Hospital, Tianjin, China.
    Localization of prostate cancer based on fuzzy fusion of multispectral MRI2013In: IFMBE Proceedings, Springer, 2013, p. 1844-1846Conference paper (Refereed)
    Abstract [en]

    Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy and surgery as well as to monitor disease progression [1]. Published studies have shown multispectral magnetic resonance imaging (MRI), i.e., a combination of multiple types of MR images, as a promising noninvasive imaging technique for the localization of prostate cancer.

  • 11.
    Landré, Jérôme
    et al.
    CReSTIC, IUT de Troyes, Troyes Cedex, France.
    Lebonvallet, Stéphane
    CReSTIC, IUT de Troyes, Troyes Cedex, France.
    Ruan, Su
    CReSTIC, IUT de Troyes, Troyes Cedex, France.
    Xiaobing, Li
    School of Electronic and Information Engineering, Dalian University of Technology, China.
    Tianshuang, Qiu
    School of Electronic and Information Engineering, Dalian University of Technology, China.
    Brunotte, François
    Centre G.-F. Leclerc, University of Bourgogne, Dijon, France.
    A deformable model-based system for 3D analysis and visualization of tumor in PET/CT images2008In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology", IEEE, 2008, p. 3130-3133Conference paper (Refereed)
    Abstract [en]

    This paper presents a tumor detecting system that allows interactive 3D tumor visualization and tumor volume measurements. An improved level set method is proposed to automatically segment the tumor images slice by slice. PET images are used to detect the tumor while CT images make a 3D representation of the patient's body possible. An initial slice with a seed within the tumor is firstly chosen by the operator. The system then performs automatically the tumor volume segmentation that allows the clinician to visualize the tumor, to measure it and to evaluate the best medical treatment adapted to the patient.

  • 12.
    Landré, Jérôme
    et al.
    Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.
    Morain-Nicolier, F.
    Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.
    Ruan, S.
    Univ. of Reims-Champagne-Ardenne - CReSTIC, IUT, Troyes Cedex, France.
    Ornamental letters image classification using local dissimilarity maps2009In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, IEEE, 2009, p. 186-190Conference paper (Refereed)
    Abstract [en]

    This article describes a new method for ancient books ornamental letters segmentation and recognition. The purpose of our work is to automatically determine the letter represented in an ornamental letter image. Our process is divided in two parts: a segmentation step of the ornamental letter is followed by a recognition step. The segmentation process uses multiresolution analysis to filter background decorations followed by a binarisation step and a morphologic reconstruction of the expected letter. The recognition process use the previously obtained reconstruction and compares it with capital letters images used as a dictionary of shapes with the Local Dissimilarity Map (LDM) distance.

  • 13.
    Landré, Jérôme
    et al.
    Institut Univ. de Technologie, Le2i, FRE 2309 CNRS, Le Creusot, France.
    Truchetet, Frédéric
    Institut Univ. de Technologie, Le2i, FRE 2309 CNRS, Le Creusot, France.
    A hierarchical architecture for content-based image retrieval of paleontology images2002In: Electronic Imaging, 19-25 January 2002: Proceedings volume 4676 / [ed] M. M. Yeung, C.-S. Li & R. W. Lienhart, SPIE - International Society for Optical Engineering, 2002, p. 138-147Conference paper (Refereed)
    Abstract [en]

    In this article a research work in the field of content-based multiresolution indexing and retrieval of images is presented. Our method uses multiresolution decomposition of images using wavelets - in the HSV colorspace - to extract parameters at multiple scales allowing a progressive (coarse-to-fine) retrieval process. Features are automatically classified into several clusters with K-means algorithm. A model image is computed for each cluster in order to represent all the images of this cluster, The process is reiterated again and again and each cluster is sub-divided into sub-clusters. The model images are stored in a tree which is proposed to users for browsing the database. The nodes of the tree are the families and the leaves are the images of the database. A paleontology images database is used to test the proposed technique. This kind of approach permits to build a visual interface easy to use for users. Our main contribution is the building of the tree with multiresolution indexing and retrieval of images and the generation of model images to be proposed to users.

  • 14.
    Landré, Jérôme
    et al.
    Univ. de Reims-Champagne-Ardenne, IUT Troyes, CReSTIC, France.
    Truchetet, Frédéric
    Univ. de Bourgogne, IUT Le Creusot, Le2i, France.
    Fast image retrieval using hierarchical binary signatures2007In: 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings, IEEE, 2007Conference paper (Refereed)
    Abstract [en]

    This article describes a content-based indexing and retrieval (CBIR) system based on hierarchical binary signatures. Binary signatures are obtained through a described binarization process of classical features (color, texture and shape). The Hamming binary distance (based on binary XOR operation) is used during retrieval. This technique was tested on a real image collection containing 7200 images and on a virtual collection of one million images. Results are very good both in terms of speed and accuracy allowing real-time image retrieval in very large image collections.

  • 15.
    Landré, Jérôme
    et al.
    CReSTIC, Université de Reims-Champagne-Ardenne, I.U.T, Troyes, France.
    Truchetet, Frédéric
    LE2I, Université de Bourgogne, I.U.T., Le Creusot, France.
    Image retrieval with binary hamming distance2007In: VISAPP 2007 - 2nd International Conference on Computer Vision Theory and Applications, Proceedings, 2007, no MTSV/-, p. 237-240Conference paper (Refereed)
    Abstract [en]

    This article proposes a content-based indexing and retrieval (CBIR) system based on query-by-visual-example using hierarchical binary signatures. Binary signatures are obtained through a described binarization process of classical features (color, texture and shape). The Hamming binary distance (based on binary XOR operation) is used for computing distances. This technique was tested on a real natural image collection containing 10 000 images and on a virtual collection of one million images. Results are very good both in terms of speed and accuracy allowing near real-time image retrieval in very large image collections.

  • 16.
    Landré, Jérôme
    et al.
    Inst. Univ. de Technologie - Le2i, U.M.R. 5158 C.N.R.S., Le Creusot, France.
    Truchetet, Frédéric
    Inst. Univ. de Technologie - Le2i, U.M.R. 5158 C.N.R.S., Le Creusot, France.
    Multiresolution Hierarchical Content-Based Image Retrieval of Paleontology Images2004In: Photonics Technologies For Robotics, Automation, and Manufacturing, 27-31 October 2003: Proceedings volume 5266 / [ed] F. Truchetet, SPIE - International Society for Optical Engineering, 2004, p. 75-83Conference paper (Refereed)
    Abstract [en]

    This article presents a visual browsing content-based indexing and retrieval (CBIR) system for large image databases applied to a paleontology database. The studied system offers a hierarchical organization of feature vectors into signature vectors leading to a research tree so that users can explore the database visually. To build the tree, our technique consists in transforming the images using multiresolution analysis in order to extract features at multiple scales. Then a hierarchical signature vector for each scale is built using extracted features. An automatic classification of the obtained signatures is performed using the k-means algorithm. The images are grouped into clusters and for each cluster a model image is computed. This model image is inserted into a research tree proposed to users to browse the database visually. The process is reiterated and each cluster is split into sub-clusters with one model image per cluster, giving the nodes of the tree. The multiresolution approach combined with the organized signature vectors offers a coarse-to-fine research during the retrieval, process (i.e. during the progression in the research tree).

  • 17.
    Landré, Jérôme
    et al.
    CReSTIC, Univ. de Reims-Champagne-Ardenne, I.U.T., Troves, France.
    Truchetet, Frédéric
    Le2i, Univ. de Bourgogne, I.U.T., Le Creusot, France.
    Optimizing signal and image processing applications using Intel libraries2007In: Eighth International Conference on Quality Control by Artificial Vision: Proceedings / [ed] D. Fofi & F. Meriaudeau, SPIE - International Society for Optical Engineering, 2007Conference paper (Refereed)
    Abstract [en]

    This paper presents optimized signal and image processing libraries from Intel Corporation. Intel Performance Primitives (IPP) is a low-level signal and image processing library developed by Intel Corporation to optimize code on Intel processors. Open Computer Vision library (OpenCV) is a high-level library dedicated to computer vision tasks. This article describes the use of both libraries to build flexible and efficient signal and image processing applications.

  • 18.
    Landré, Jérôme
    et al.
    Univ. de Reims-Champagne-Ardenne, France.
    Truchetet, Frédéric
    Univ. de Bourgogne, France.
    Laligant, Olivier
    Univ. de Bourgogne, France.
    Visual browsing in image collections using wavelets2006In: / [ed] F. Truchetet & O. Laligant, SPIE - International Society for Optical Engineering, 2006Conference paper (Refereed)
    Abstract [en]

    Visual browsing is an important way of searching for images in large databases. In image retrieval, a lot of problems have to be solved to get a good system: dimensionality curse, users' search context, size of the database, visual features. In this article, a method trying to attenuate these problems is proposed. Each features vector is organized into four signature vectors used in the classification process while building a fuzzy search tree that is proposed to users for visual browsing. Our system gives good results in terms of speed and accuracy by solving several problems of classical image retrieval methods.

  • 19.
    Landré, Jérôme
    et al.
    Institut Universitaire de Technologie, Le Creusot, France.
    Truchetet, Frédéric
    Institut Universitaire de Technologie, Le Creusot, France.
    Montuire, Sophie
    Université de Bourgogne, UMR CNRS 5561, Laboratoire de Biogéosciences, Dijon, France.
    Content-based multiresolution indexing and retrieval of Paleontology images2001In: Proceedings of SPIE, the International Society for Optical Engineering, ISSN 0277-786X, E-ISSN 1996-756X, Vol. 4315, p. 482-489Article in journal (Refereed)
    Abstract [en]

    In this article a research work in the field of content-based image retrieval in large database applied to the Paleontolgy image database of the université de Bourgogne, Dijon, France called "TRANS'TYFIPAL" is proposed. Our indexing method is based on multiresolution decomposition of database images using wavelets. For each kind of paleontology images we try to find a characteristic image representing it. This model image is computed using a classification algorithm on the space of parameters extracted from the wavelet transform of each image. Then a search tree is built to offer users a graphic interface for retrieving images. So that users have to navigate through this tree to find an image similar that of to their request. Our contribution in the field is the building of the model and of the search tree to make user access easier and faster. This paper ends with a conclusion on first coming results and a description of future work to be done to enhance our indexing and retrieval method.

  • 20.
    Landré, Jérôme
    et al.
    Inst. Universitaire de Technologie, Le2i FRE 2309 CNRS, Le Creusot, France.
    Truchetet, Frédéric
    Inst. Universitaire de Technologie, Le2i FRE 2309 CNRS, Le Creusot, France.
    Montuire, Sophie
    Inst. Universitaire de Technologie, Le2i FRE 2309 CNRS, Le Creusot, France.
    David, Bruno
    Inst. Universitaire de Technologie, Le2i FRE 2309 CNRS, Le Creusot, France.
    Automatic building of a visual interface for content-based multiresolution retrieval of paleontology images2001In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 10, no 4, p. 957-965Article in journal (Refereed)
    Abstract [en]

    In this article we present research work in the field of content-based image retrieval in large databases applied to the paleontology image database of the Université de Bourgogne, Dijon, France, called "TRANS'TYFIPAL". Our indexing method is based on multiresolution decomposition of database images using wavelets. For each family of paleontology images we try to find a model image that represents it. The K-means automatic classification algorithm divides the space of parameters into several clusters. A model image for each cluster is computed from the wavelet transform of each image of the cluster. Then a search tree is built to offer users a graphic interface for retrieving images. So users have to navigate through this tree of model images to find an image similar to that they are requesting. Our contribution in the field is the building of the model and of the search tree to make user access easier and faster. At the end of this article we give experimental results and a description of future work that will be done to enhance our indexing and retrieval method.

  • 21.
    Morain-Nicolier, Frédéric
    et al.
    CReSTIC-URCA, IUT, Troyes Cedex, France.
    Landré, Jérôme
    CReSTIC-URCA, IUT, Troyes Cedex, France.
    Ruan, Su
    CReSTIC-URCA, IUT, Troyes Cedex, France.
    Binary pattern matching from a local dissimilarity measure2010In: 2010 2nd International Conference on Image Processing Theory, Tools and Applications, IPTA 2010, IEEE, 2010, p. 417-420Conference paper (Refereed)
    Abstract [en]

    This communication deals with finding the position of a reference shape in a given image. The proposed matcher is constructed from local dissimilarity maps. These maps allow to efficiently and robustly measure the differences between two images. It is shown an example that the matcher potentially returns less false-positives than a reference method (chamfer matching). This is possible as the local dissimilarity measure is symmetric, which makes it more robust to noise.We show that the proposed matcher is a generalization of the chamfer matching. It also allows fast computation times. A good robustness to noise is confirmed from presented simulations.

  • 22.
    Morain-Nicolier, Frédéric
    et al.
    CRESTIC - URCA, Troyes Cedex, France.
    Landré, Jérôme
    CRESTIC - URCA, Troyes Cedex, France.
    Ruan, Su
    CRESTIC - URCA, Troyes Cedex, France.
    Gray level local dissimilarity map and global dissimilarity index for quality of medical images2009In: 7th IFAC Symposium on Modelling and Control in Biomedical Systems (including Biological Systems) MCBMS’09: Proceedings / [ed] S. Rees, D. D. Feng, E. R. Carson & S. Andreassen, Elsevier, 2009, no PART 1, p. 281-286Conference paper (Refereed)
    Abstract [en]

    In order to evaluate performance quality of coding techniques, it is needed to have a good global index and a local index allowing the localisation of the distortions. In this study, a local dissimilarity map is presented for gray-level images. Its application to the comparison of a compressed image and its reference allows an excellent visual detection of the distortions. A global dissimilarity index is computed from the local dissimilarity map. These new measures are compared to the structural similarity index (SSIM). The results of the global measure are as good as the SSIM. The results of the local measure are quite superior to the SSIM computed in a local window. We claim these good results come from the consistency of the proposed index. It is more consistent to compute a global measure from a local one, than a local measure from a global one.

  • 23.
    Sithravel, RatnaKala
    et al.
    Jönköping University, School of Engineering, JTH, Construction Engineering and Lighting Science.
    Landré, Jérôme
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Aries, Myriam
    Jönköping University, School of Engineering, JTH, Construction Engineering and Lighting Science.
    Hurtig-Wennlöf, Anita
    Jönköping University, School of Health and Welfare, HHJ, Department of Clinical Diagnostics.
    Potentials of radar sensor detecting the presence of an imitated user for optimising short-range presence-sensing lighting in homes2023In: Journal of Physics: Conference Series, Volume 2600, Daylighting & electric lighting, Institute of Physics (IOP), 2023, Vol. 2600, no 11, article id 132010Conference paper (Refereed)
    Abstract [en]

    Current presence-sensing technologies for energy-efficient lighting control and building optimisation are (i) catered to commercial and institutional environments, and (ii) focused on lamp technology and occupancy detection. They often ignore user behaviour characteristics, which significantly influence energy consumption. Therefore, this study aims to identify alternative sensing techniques as part of a lighting control system that can energy-efficiently support user's behavioural needs in mixed-function residential spaces. An exploratory study investigated the optimal placement of a non-wearable radar sensor to detect an imitated user's breathing frequency at varying pre-set horizontal distance positions, and the sensor's performance was validated with a spirometer. The procedure measured a balloon's radar-detected distance, radar-detected breathing frequency, and spirometer-registered breathing frequency at each pre-set position. The radar sensor detected all simulated breathing frequencies with almost 100% data accuracy but was not comparable in detecting all distances. The radar offers a less intrusive short-range presence-sensing for homes, accurately detecting breathing frequencies in a contactless way between 0.2m to 0.8m. Further investigations are recommended to develop radar sensing that could predict lighting options based on user's objective feedback.

1 - 23 of 23
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