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Titre : Generic programming in modern C++ for image processing Type de document : Thèse/HDR Auteurs : Michaël Roynard, Auteur ; Thierry Géraud, Directeur de thèse ; Edwin Carlinet, Directeur de thèse Editeur : Paris : Sorbonne Université Année de publication : 2022 Importance : 237 p. Format : 21 x 30 cm Note générale : bibliographie
Doctoral thesis submitted to fufill the requirements for the degree of Doctor of Sorbonne Université with the doctoral speciality of "Software Engineering and Image Processing"Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] C++
[Termes IGN] langage de programmation
[Termes IGN] morphologie mathématique
[Termes IGN] programmation informatique
[Termes IGN] taxinomie
[Termes IGN] traitement d'imageIndex. décimale : THESE Thèses et HDR Résumé : (auteur) C++ is a multi-paradigm language that enables the initiated programmer to set up efficient image processing algorithms. This language strength comes from several aspects. C++ is high-level, which enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One of the most fundamental aspects where C++ really shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless,conciliating the aspects of genericity, efficiency, and simplicity is not trivial. Modern C++ (post-2011) has brought new features that made the language simpler and more powerful. In this thesis, we first explore one particular C++20aspect: the concepts, in order to build a concrete taxonomy of image related types and algorithms. Second, we explore another addition to C++20, ranges (and views), and we apply this design to image processing algorithms and image types in order to solve issues such as how hard it is to customize/tweak image processing algorithms. Finally, we explore possibilities regarding how we can offer a bridge between static (compile-time) generic C++ code and dynamic (runtime) Python code. We offer our own hybrid solution and benchmark its performance as well as discuss what can be done in the future with JIT technologies. Considering those three axes, we will address the issue regarding the way to conciliate generic programming, efficiency and ease of use. Note de contenu : I Context and History of Generic programming
1- Introduction
2- Generic programming (genericity)
II Applying Generic programming for Image processing in the static world
3- Taxonomy for Image Processing: Image types and algorithms
4- Image views
III Bringing Generic programming to the dynamic world
5- A bridge between the static world and the dynamic world
6- Conclusion and continuationNuméro de notice : 24083 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : PhD thesis : Software Engineering and Image Processing : Sorbonne Université : 2022 Organisme de stage : EPITA DOI : sans En ligne : https://theses.hal.science/tel-03922670 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102391
Titre : Developing graphics frameworks with Python and OpenGL Type de document : Guide/Manuel Auteurs : Lee Stemkoski, Auteur ; Michael Pascale, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2021 Importance : 345 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-00-318137-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] image 3D
[Termes IGN] interface de programmation
[Termes IGN] OpenGL
[Termes IGN] processeur graphique
[Termes IGN] programmation informatique
[Termes IGN] Python (langage de programmation)
[Termes IGN] scène 3D
[Termes IGN] texture d'image
[Termes IGN] transformation géométriqueRésumé : (éditeur) Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds. You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results. Note de contenu : 1- Introduction to computer graphics
2- Introduction to Pygame and OpenGL
3- Matrix algebra and transformations
4- A scene graph framework
5- Textures
6- Light and shadowNuméro de notice : 28306 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel DOI : 10.1201/9781003181378 En ligne : https://doi.org/10.1201/9781003181378 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98077
Titre : Introduction to scientific programming with Python Type de document : Guide/Manuel Auteurs : Joakim Sundnes, Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 157 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-50356-7 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] équation polynomiale
[Termes IGN] interface graphique
[Termes IGN] programmation adaptée à l'objet
[Termes IGN] Python (langage de programmation)Résumé : (éditeur) This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies. Note de contenu : 1- Getting started with Python
2- Computing with formulas
3- Loops and lists
4- Functions and branching
5- User input and error handling
6- Arrays and plotting
7- Dictionaries and strings
8- Classes
9- Object-oriented programmingNuméro de notice : 28555 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-50356-7 En ligne : https://doi.org/10.1007/978-3-030-50356-7 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97444
Titre : Programming for Computations - Python Type de document : Guide/Manuel Auteurs : Svein Linge, Éditeur scientifique ; Hans Petter Langtangen, Éditeur scientifique Editeur : Springer Nature Année de publication : 2020 Importance : 332 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-16877-3 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] codage
[Termes IGN] équation différentielle
[Termes IGN] programmation informatique
[Termes IGN] Python (langage de programmation)Index. décimale : 26.04 Langages informatiques Résumé : (éditeur) This second edition of the book presents computer programming as a key method for solving mathematical problems and represents a major revision: all code is now written in Python version 3.6 (the first edition was based on Python version 2.7). The first two chapters of the previous edition have been extended and split up into
five new chapters, thus expanding the introduction to programming from 50 to 150 pages. Throughout, explanations are now more complete, previous examples have been modified, and new sections, examples, and exercises have been added. Also, errors and typos have been corrected. The book was inspired by the Springer book TCSE 6, A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.Note de contenu : 1- The First Few Steps
2- A Few More Steps
3- Loops and Branching
4- Functions and the Writing of Code
5- Some More Python Essentials
6- Computing Integrals and Testing Code
7- Solving Nonlinear Algebraic Equations
8- Solving Ordinary Differential Equations
9- Solving Partial Differential EquationsNuméro de notice : 28461 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-3-030-16877-3 En ligne : https://doi.org/10.1007/978-3-030-16877-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99072 Geographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation / Shu Wang in ISPRS International journal of geo-information, vol 8 n° 4 (April 2019)
Titre : Geographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation Type de document : Article/Communication Auteurs : Shu Wang, Auteur ; Xueying Zhang, Auteur ; Peng Ye, Auteur ; Mi Du, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : n° 184 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Langages informatiques
[Termes IGN] formalisation
[Termes IGN] langage de programmation
[Termes IGN] Nankin (Kiangsou)
[Termes IGN] représentation des connaissances
[Termes IGN] réseau sémantiqueRésumé : (auteur) Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation. Numéro de notice : A2019-671 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.3390/ijgi8040184 Date de publication en ligne : 08/04/2019 En ligne : https://doi.org/10.3390/ijgi8040184 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100286
in ISPRS International journal of geo-information > vol 8 n° 4 (April 2019) . - n° 184[article]
