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Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network / Feng-Ping An in The Visual Computer, vol 38 n° 2 (February 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 2 (February 2022) . - pp 541 - 553 Titre : Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network Type de document : Article/Communication Auteurs : Feng-Ping An, Auteur ; Jun-e Liu, Auteur ; Lei Bai, Auteur Année de publication : 2022 Article en page(s) : pp 541 - 553 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection d'objet [Termes IGN] programmation non linéaire [Termes IGN] réseau neuronal convolutif Résumé : (auteur) Traditional object recognition algorithms cannot meet the requirements of object recognition accuracy in the actual warehousing and logistics field. In recent years, the rapid development of the deep learning theory has provided a technical approach for solving the above problems, and a number of object recognition algorithms has been proposed based on deep learning, which have been promoted and applied. However, deep learning has the following problems in the application process of object recognition: First, the nonlinear modeling ability of the activation function in the deep learning model is poor; second, the deep learning model has a large number of repeated pooling operations during which information is lost. In view of these shortcomings, this paper proposes multiple-parameter exponential linear units with uniform and learnable parameter forms and introduces two learned parameters in the exponential linear unit (ELU), enabling it to represent piecewise linear and exponential nonlinear functions. Therefore, the ELU has good nonlinear modeling capabilities. At the same time, to improve the problem of losing information in the large number of repeated pooling operations, this paper proposes a new global convolutional neural network structure. This network structure makes full use of the local and global information of different layer feature maps in the network. It can reduce the problem of losing feature information in the large number of pooling operations. Based on the above ideas, this paper suggests an object recognition algorithm based on the optimized nonlinear activation function-global convolutional neural network. Experiments were carried out on the CIFAR100 dataset and the ImageNet dataset using the object recognition algorithm proposed in this paper. The results show that the object recognition method suggested in this paper not only has a better recognition accuracy than traditional machine learning and other deep learning models but also has a good stability and robustness. Numéro de notice : A2022-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02033-x Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02033-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100041 [article]

A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 32 - 40 Titre : A derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds Type de document : Article/Communication Auteurs : Fan Xue, Auteur ; Weisheng Lu, Auteur ; Christopher J. Webster, Auteur ; Ke Chen, Auteur Année de publication : 2019 Article en page(s) : pp 32 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] méthode robuste [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] octree [Termes IGN] programmation non linéaire [Termes IGN] semis de points Résumé : (Auteur) Symmetry is ubiquitous in architecture, across both time and place. Automated architectural symmetry detection (ASD) from a data source is not only an intriguing inquiry in its own right, but also a step towards creation of semantically rich building and city information models with applications in architectural design, construction management, heritage conservation, and smart city development. While recent advances in sensing technologies provide inexpensive yet high-quality architectural 3D point clouds, existing methods of ASD from these data sources suffer several weaknesses including noise sensitivity, inaccuracy, and high computational loads. This paper aims to develop a novel derivative-free optimization (DFO)-based approach for effective ASD. It does so by firstly transforming ASD into a nonlinear optimization problem involving architectural regularity and topology. An in-house ODAS (Optimization-based Detection of Architectural Symmetries) approach is then developed to solve the formulated problem using a set of state-of-the-art DFO algorithms. Efficiency, accuracy, and robustness of ODAS are gauged from the experimental results on nine sets of real-life architectural 3D point clouds, with the computational time for ASD from 1.4 million points only 3.7 s and increasing in a sheer logarithmic order against the number of points. The contributions of this paper are threefold. Firstly, formulating ASD as a nonlinear optimization problem constitutes a methodological innovation. Secondly, the provision of up-to-date, open source DFO algorithms allows benchmarking in the future development of free, fast, accurate, and robust approaches for ASD. Thirdly, the ODAS approach can be directly used to develop building and city information models for various value-added applications. Numéro de notice : A2019-070 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.12.005 Date de publication en ligne : 18/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.12.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92157 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019023	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019022	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 261 - 279 Titre : Automatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning Type de document : Article/Communication Auteurs : Qian Zhang, Auteur ; Yuan Tian, Auteur ; Yuan Yang, Auteur ; Chunhong Pan, Auteur Année de publication : 2015 Article en page(s) : pp 261 - 279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] apprentissage (cognition) [Termes IGN] apprentissage dirigé [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] méthode des moindres carrés [Termes IGN] programmation non linéaire Résumé : (Auteur) Spectral-spatial feature combination for hyperspectral image analysis has become an important research topic in hyperspectral remote sensing applications. A simple and straightforward way to integrate spectral-spatial features is to concatenate heterogeneous features into a long vector. Then, the dimensionality reduction techniques, i.e., feature selection, are applied before subsequent utilizations. However, such representation can introduce redundancy and noise. Moreover, traditional single-feature selection methods treat different features equally and ignore their complementary properties. As a result, the performance of subsequent tasks, i.e., classification, would drop down. In this paper, we propose a novel approach to integrate the spectral-spatial features based on the concatenating strategy, termed discriminative sparse multimodal learning for feature selection (DSML-FS). In the proposed method, joint structured sparsity regularizations are used to exploit the intrinsic data structure and relationships among different features. Discriminative least squares regression is applied to enlarge the distance between classes. Therefore, the weight matrix incorporating the information of feature wise and individual properties is automatically learned for spectral-spatial feature selection. We develop an alternative iterative algorithm to solve the nonlinear optimization problem in DSML-FS with global convergence. We systematically evaluate the proposed algorithm on three available hyperspectral data sets, and the encouraging experimental results demonstrate the effectiveness of DSML-FS. Numéro de notice : A2015-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321277 En ligne : https://doi.org/10.1109/TGRS.2014.2321277 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75114 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Image-based 3D surface reconstruction by combination of photometric, geometric, and real-aperture methods / Pablo d' Angelo in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 3 (May - June 2008)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 63 n° 3 (May - June 2008) . - pp 297 - 321 Titre : Image-based 3D surface reconstruction by combination of photometric, geometric, and real-aperture methods Type de document : Article/Communication Auteurs : Pablo d' Angelo, Auteur ; C. Wöhler, Auteur Année de publication : 2008 Article en page(s) : pp 297 - 321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] données de terrain [Termes IGN] gradient de pente [Termes IGN] pente [Termes IGN] photométrie [Termes IGN] polarisation [Termes IGN] profondeur [Termes IGN] programmation non linéaire [Termes IGN] reconstruction 3D [Termes IGN] réflectance Résumé : (Auteur) An image-based 3D surface reconstruction method based on simultaneous evaluation of intensity and polarisation features (shape from photopolarimetric reflectance) and its combination with absolute depth data is introduced in this article. The proposed technique is based on the analysis of single or multiple intensity and polarisation images. To compute the surface gradients, we present a global optimisation method based on a variational framework and a local optimisation method based on solving a set of non-linear equations individually for each image pixel. These approaches are suitable for strongly non-Lambertian surfaces and those of diffuse reflectance behaviour and can also be adapted to surfaces of non-uniform albedo. We describe how independently measured absolute depth data is integrated into the shape from photopolarimetric reflectance framework in order to increase the accuracy of the 3D reconstruction result. In this context we concentrate on dense but noisy depth data obtained by depth from defocus and on sparse but accurate depth data obtained by stereo or structure from motion analysis. We show that depth from defocus information should preferentially be used for initialising the optimisation schemes for the surface gradients. For integration of sparse depth information, we suggest an optimisation scheme that simultaneously adapts the surface gradients to the measured intensity and polarisation data and to the surface slopes implied by depth differences between pairs of depth points. In principle, arbitrary sources of depth information are possible in the presented framework. Experiments on synthetic and on real-world data reveal that while depth from defocus is especially helpful for providing an initial estimate of the surface gradients and the albedo in the absence of a-priori knowledge, integration of stereo or structure from motion information significantly increases the 3D reconstruction accuracy. In our real-world experiments, we regard the scenarios of 3D reconstruction of raw forged iron surfaces in the domain of industrial quality inspection and the generation of a digital elevation model of a section of the lunar surface in the context of space-based planetary exploration. Copyright ISPRS Numéro de notice : A2008-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.09.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.09.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29208 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-08031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Self-calibration of a stereo rig using monocular epipolar geometries / Fadi Dornaika in Pattern recognition, vol 40 n° 10 (October 2007)  

Public [article]  inPattern recognition > vol 40 n° 10 (October 2007) . - pp 2716 - 2729 Titre : Self-calibration of a stereo rig using monocular epipolar geometries Type de document : Article/Communication Auteurs : Fadi Dornaika , Auteur Année de publication : 2007 Article en page(s) : pp 2716 - 2729 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] auto-étalonnage [Termes IGN] géométrie épipolaire [Termes IGN] programmation non linéaire Résumé : (auteur) This paper addresses the problem of self-calibration from one unknown motion of an uncalibrated stereo rig. Unlike the existing methods for stereo rig self-calibration, which have been focused on applying the autocalibration paradigm using both motion and stereo correspondences, our method does not require the recovery of stereo correspondences. Our method combines purely algebraic constraints with implicit geometric constraints. Assuming that the rotational part of the stereo geometry has two unknown degrees of freedom (i.e., the third dof is roughly known), and that the principle point of each camera is known, we first show that the computation of the intrinsic and extrinsic parameters of the stereo rig can be recovered from the motion correspondences only, i.e., the monocular fundamental matrices. We then provide an initialization procedure for the proposed non-linear method. We provide an extensive performance study for the method in the presence of image noise. In addition, we study some of the aspects related to the 3D motion that govern the accuracy of the proposed self-calibration method. Experiments conducted on synthetic and real data/images demonstrate the effectiveness and efficiency of the proposed method. Numéro de notice : A2007-689 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.patcog.2007.01.008 Date de publication en ligne : 27/01/2007 En ligne : https://doi.org/10.1016/j.patcog.2007.01.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102802 [article]

Zur Lösung nichtlinearer Ausgleichungsprobleme bei der Bestimmung von Frequenzen in Zeitreihen / R. Mautz (2001)

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Ausgleichungsrechnung in nichtlinearen Modellen / P. Lohse (1994)

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Introduction à l'analyse numérique matricielle et à l'optimisation / Philippe Gaston Ciarlet (1993)

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Optimization of horizontal control networks by nonlinear programing / Dennis G. Milbert (1979)

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Zur analytischen Optimierung geodätischer Netze / R. Kelm (1976)

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