ПОТЕНЦІАЛ ЗАСТОСУВАННЯ РІЗНИХ МЕТОДІВ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧІ РОЗПІЗНАВАННЯ КРЕСЛЕНЬ ТА ТРАНСФОРМАЦІЇ 2D→3D

The article analyzes the main methods of artificial intelligence in the task of recognizing drawings and transforming a 2D model into a 3D model. With the rapid development of information technologies, and especially in the pursuit of the most realistic reproduction of the project of the future product/house and other objects in digital form, the question of recognizing drawings and transforming a 2D model into a 3D model is very acute. As the number and complexity of tasks arising from the digitization of existing paper-based drawing and technical documentation grows, and the parallel need to transform two-dimensional models into three-dimensional models for visualization in three-dimensional space of complex objects, researchers have drawn attention to the possibilities of applying technologies and systems of artificial intelligence in the processes of drawing recognition and transformation of two-dimensional models into three-dimensional models. The first studies devoted to the application of artificial intelligence in the tasks of recognizing images on drawings began to appear in the early 90s of the 20th century. The analysis of approaches to the recognition of drawings allows us to consider the potential of using different methods of artificial intelligence in the task of recognizing drawings and transforming two-dimensional models into three-dimensional models. To analyze the potential of improving the work of CNN, as well as its architecture, without resorting to extensive expansion of the convolutional neural network (CNN) architecture, as well as taking into account the need to solve the task related to the logical vectorization of primitives and/or conditional graphics recognized by means of a convolutional neural network markings on drawings to perform 2D to 3D transformation. In the future, this stimulates researchers to look for alternative methods and models for image recognition systems on drawings.У статті проведено аналіз основних методів штучного інтелекту у задачі розпізнавання креслень та трансформації 2D моделі у 3D модель. Із стрімким розвитком інформаційних технологій, і, особливо, в прагненні максимально реалістичного відтворити проект майбутнього виробу/будинку та інших об’єктів в цифровому вигляді, дуже гостро постає питання розпізнавання креслень та трансформації 2D моделі у 3D модель.  В міру зростання кількості та складності завдань, що виникають при оцифруванні існуючої на паперових носіях креслярсько-технічної документації, та паралельної необхідності трансформації двовимірних моделей у тривимірні моделі для візуалізації у тривимірному просторі складних об’єктів, дослідники звернули увагу на можливості застосування технологій та систем штучного інтелекту у процесах розпізнавання креслень та трансформації двовимірних моделей у тривимірні моделі. Перші дослідження, присвячені застосуванню штучного інтелекту в задачах розпізнавання зображень на кресленнях, почали з’являтися ще на початку 90-х років 20-го століття. Аналіз підходів для розпізнавання креслень дозволяє розглянути потенціал застосування різних методів штучного інтелекту в задачі розпізнавання креслень та трансформації двовимірних моделей у тривимірні моделі. Проаналізувати потенціал покращення роботи CNN, а також її архітектури, не вдаючись до екстенсивного розширення архітектури згорткової нейронної мережі (CNN), а також враховуючи необхідність вирішення завдання, пов’язаного з логічною векторизацією розпізнаних за допомогою згорткової нейронної мережі примітивів та/або умовно-графічних позначень на кресленнях для виконання трансформації 2D в 3D. В подальшому це стимулює дослідників шукати альтернативні методи та моделі для систем розпізнавання зображень на кресленнях

A progressive learning method for symbols recognition

Conditional discriminant analysis, symbol recognitionInternational audienceThis paper deals with a progressive learning method for symbols recognition which improves its own recognition rate when new symbols are recognized in graphics documents. We propose a discriminant analysis method which provides allocation rules from learning samples with known classes. However a discriminant analysis method is efficient only if learning samples and data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each observation to take into account the parasitic effects between the data and the learning samples. We propose also an adaptation to consider the user feedback

Analysis of Engineering Drawings: State of the Art and Challenges

Contribution à un ouvrage.In this paper, we analyze the state of the art in interpretation of engineering drawings, both from a methodological point of view and from the perspective of the applications. We try to emphasize where techniques are mature, where they need further maturing, and where we still have open challenges. Special attention is given to the progress in the last two years

Apprentissage progressif pour la reconnaissance de symboles dans les documents graphiques

National audienceLes méthodes actuelles de reconnaissance de symboles donnent de bons résultats quand il s'agit de reconnaître peu de symboles différents qui sont peu bruités et souvent déconnectés du graphique. Cependant, dans le cas d'applications réelles, les méthodes sont encore mal maîtrisées quand il s'agit de discriminer dans de grandes bases entre plusieurs centaines de symboles différents, souvent complexes et bruités et encapsulés dans les couches graphiques. Dans ce contexte il est nécessaire de mettre en oeuvre des méthodes d'apprentissage. Nous présentons dans cet article une méthode d'apprentissage progressif pour la reconnaissance de symboles qui améliore son propre taux de reconnaissance au fur et à mesure que de nouveaux symboles sont reconnus dans les documents. Pour ce faire, nous proposons une nouvelle exploitation de l'analyse discriminante qui fournit des règles d'affectation à partir d'un échantillon d'apprentissage sur lequel les appartenances aux classes sont connues (apprentissage supervisé). Mais cette méthode ne se révèle efficace que si l'échantillon d'apprentissage et les données ultérieures sont observés dans les mêmes conditions. Or cette hypothèse est rarement vérifiée dans les conditions réelles. Pour pallier ce problème, nous avons adapté une approche récente d'analyse discriminante conditionnelle qui ajoute à chaque observation l'observation d'un vecteur aléatoire, représentatif des effets parasites observés dans l'analyse discriminante classique

Interactive interpretation of structured documents: Application to the recognition of handwritten architectural plans

International audienceThis paper addresses a whole architecture, including the IMISketch method. IMISketch method incorporates two aspects: document analysis and interactivity. This paper describes a global vision of all the parts of the project. IMISketch is a generic method for an interactive interpretation of handwritten sketches. The analysis of complex documents requires the management of uncertainty. While, in practice the similar methods often induce a large combinatorics, IMISketch method presents several optimization strategies to reduce the combinatorics. The goal of these optimizations is to have a time analysis compatible with user expectations. The decision process is able to solicit the user in the case of strong ambiguity: when it is not sure to make the right decision, the user explicitly validates the right decision to avoid a fastidious a posteriori verification phase due to propagation of errors.This interaction requires solving two major problems: how interpretation results will be presented to the user, and how the user will interact with analysis process. We propose to study the effects of those two aspects. The experiments demonstrate that (i) a progressive presentation of the analysis results, (ii) user interventions during it and (iii) the user solicitation by the analysis process are an efficient strategy for the recognition of complex off-line documents.To validate this interactive analysis method, several experiments are reported on off-line handwritten 2D architectural floor plans

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

Chart recognition and interpretation in document images

Ph.DDOCTOR OF PHILOSOPH

An online corpus of UML Design Models : construction and empirical studies

We address two problems in Software Engineering. The first problem is how to assess the severity of software defects? The second problem we address is that of studying software designs. Automated support for assessing the severity of software defects helps human developers to perform this task more efficiently and more accurately. We present (MAPDESO) for assessing the severity of software defects based on IEEE Standard Classification for Software Anomalies. The novelty of the approach lies in its use of uses ontologies and ontology-based reasoning which links defects to system level quality properties. One of the main reasons that makes studying of software designs challenging is the lack of their availability. We decided to collect software designs represented by UML models stored in image formats and use image processing techniques to convert them to models. We present the 'UML Repository' which contains UML diagrams (in image and XMI format) and design metrics. We conducted a series of empirical studies using the UML Repository. These empirical studies are a drop in the ocean empirical studies that can be conducted using the repository. Yet these studies show the versatility of useful studies that can be based on this novel repository of UML designs.Erasmus Mundus program (JOSYLEEN)Algorithms and the Foundations of Software technolog