• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 32
  • 8
  • 7
  • 1
  • 1
  • Tagged with
  • 55
  • 55
  • 17
  • 15
  • 14
  • 11
  • 11
  • 9
  • 9
  • 9
  • 8
  • 8
  • 7
  • 7
  • 7
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Mining for Frequent Community Structures using Approximate Graph Matching

Kolli, Lakshmi Priya 15 July 2021 (has links)
No description available.
32

Performance Enhancement of Data Retrieval from Episodic Memory in Soar Architecture

BHUJEL, MAN BAHADUR 14 December 2018 (has links)
No description available.
33

Extraction et reconnaissance de primitives dans les façades de Paris à l'aide d'appariement de graphes / Extraction and recognition of object in the facades of Paris using graph matching

Haugeard, Jean-emmanuel 17 December 2010 (has links)
Cette dernière décennie, la modélisation des villes 3D est devenue l'un des enjeux de la recherche multimédia et un axe important en reconnaissance d'objets. Dans cette thèse nous nous sommes intéressés à localiser différentes primitives, plus particulièrement les fenêtres, dans les façades de Paris. Dans un premier temps, nous présentons une analyse des façades et des différentes propriétés des fenêtres. Nous en déduisons et proposons ensuite un algorithme capable d'extraire automatiquement des hypothèses de fenêtres. Dans une deuxième partie, nous abordons l'extraction et la reconnaissance des primitives à l'aide d'appariement de graphes de contours. En effet une image de contours est lisible par l'oeil humain qui effectue un groupement perceptuel et distingue les entités présentes dans la scène. C'est ce mécanisme que nous avons cherché à reproduire. L'image est représentée sous la forme d'un graphe d'adjacence de segments de contours, valué par des informations d'orientation et de proximité des segments de contours. Pour la mise en correspondance inexacte des graphes, nous proposons plusieurs variantes d'une nouvelle similarité basée sur des ensembles de chemins tracés sur les graphes, capables d'effectuer les groupements des contours et robustes aux changements d'échelle. La similarité entre chemins prend en compte la similarité des ensembles de segments de contours et la similarité des régions définies par ces chemins. La sélection des images d'une base contenant un objet particulier s'effectue à l'aide d'un classifieur SVM ou kppv. La localisation des objets dans l'image utilise un système de vote à partir des chemins sélectionnés par l'algorithme d'appariement. / This last decade, modeling of 3D city became one of the challenges of multimedia search and an important focus in object recognition. In this thesis we are interested to locate various primitive, especially the windows, in the facades of Paris. At first, we present an analysis of the facades and windows properties. Then we propose an algorithm able to extract automatically window candidates. In a second part, we discuss about extraction and recognition primitives using graph matching of contours. Indeed an image of contours is readable by the human eye, which uses perceptual grouping and makes distinction between entities present in the scene. It is this mechanism that we have tried to replicate. The image is represented as a graph of adjacency of segments of contours, valued by information orientation and proximity to edge segments. For the inexact matching of graphs, we propose several variants of a new similarity based on sets of paths, able to group several contours and robust to scale changes. The similarity between paths takes into account the similarity of sets of segments of contours and the similarity of the regions defined by these paths. The selection of images from a database containing a particular object is done using a KNN or SVM classifier.
34

Contributions to a fast and robust object recognition in images / Contributions à une reconnaissance d'objet rapide et robuste en images

Revaud, Jérôme 27 May 2011 (has links)
Dans cette thèse, nous présentons tout d'abord une contribution visant à pallier ce problème de robustesse pour la reconnaissance d'instances, puis une extension directe de cette contribution à la reconnaissance et la localisation de classes d'objets. Dans un premier temps, nous avons développé une méthode inspiré de l'appariement de graphe (i.e. graph matching) afin de traiter le problème de la reconnaissance rapide d'instances d'objets spécifiques dans des conditions bruitées. Cette méthode permet de rajouter facilement un nombre quelconque d’autres types de caractéristiques locales (e.g. contours, textures…) moins affectées par le bruit tout en contournant le problème de la normalisation et sans pénaliser la vitesse de détection. Nos expériences sur plusieurs bases de test ont montré la pertinence de notre approche. Notre approche est globalement légèrement moins robuste à l'occultation que les approches existantes, mais elle produit des performances supérieures aux approches standard en conditions bruitées. Dans un second temps, nous avons développé une approche pour la détection de classes d'objets dans le même esprit que celui du sac de mots visuels. Pour cela, nous utilisons nos cascades de micro-classifieurs pour reconnaître des mots visuels plus distinctifs que les mots basés simplement sur des points d'intérêts. L'apprentissage se divise en deux parties: dans un premier temps, nous générons des cascades de micro-classifieurs servant à reconnaître des parties locales des images modèles ; puis dans un second temps, nous utilisons un classifieur afin de modéliser la frontière de décision entre les images de classe et celles de non-classe. Nous montrons que l'association de mots classiques (à partir de points d'intérêts) et de nos mots plus distincts produit une amélioration significative des performances pour un temps de calcul assez faible. / In this thesis, we first present a contribution to overcome this problem of robustness for the recognition of object instances, then we straightly extend this contribution to the detection and localization of classes of objects. In a first step, we have developed a method inspired by graph matching to address the problem of fast recognition of instances of specific objects in noisy conditions. This method allows to easily combine any types of local features (eg contours, textures ...) less affected by noise than keypoints, while bypassing the normalization problem and without penalizing too much the detection speed. Unlike other methods based on a global rigid transformation, our approach is robust to complex deformations such as those due to perspective or those non-rigid inherent to the model itself (e.g. a face, a flexible magazine). Our experiments on several datasets have showed the relevance of our approach. It is overall slightly less robust to occlusion than existing approaches, but it produces better performances in noisy conditions. In a second step, we have developed an approach for detecting classes of objects in the same spirit as the bag-of-visual-words model. For this we use our cascaded micro-classifiers to recognize visual words more distinctive than the classical words simply based on visual dictionaries. Training is divided into two parts: First, we generate cascades of micro-classifiers for recognizing local parts of the model pictures and then in a second step, we use a classifier to model the decision boundary between images of class and those of non-class. We show that the association of classical visual words (from keypoints patches) and our disctinctive words results in a significant improvement. The computation time is generally quite low, given the structure of the cascades that minimizes the detection time and the form of the classifier is extremely fast to evaluate.
35

Rotulação de símbolos matemáticos manuscritos via casamento de expressões / Labeling of Handwritten Mathematical Symbols via Expression Matching

Honda, Willian Yukio 23 January 2013 (has links)
O problema de reconhecimento de expressões matemáticas manuscritas envolve três subproblemas importantes: segmentação de símbolos, reconhecimento de símbolos e análise estrutural de expressões. Para avaliar métodos e técnicas de reconhecimento, eles precisam ser testados sobre conjuntos de amostras representativos do domínio de aplicação. Uma das preocupações que tem sido apontada ultimamente é a quase inexistência de base de dados pública de expressões matemáticas, o que dificulta o desenvolvimento e comparação de diferentes abordagens. Em geral, os resultados de reconhecimento apresentados na literatura restringem-se a conjuntos de dados pequenos, não disponíveis publicamente, e muitas vezes formados por dados que visam avaliar apenas alguns aspectos específicos do reconhecimento. No caso de expressões online, para treinar e testar reconhecedores de símbolos, as amostras são em geral obtidas solicitando-se que as pessoas escrevam uma série de símbolos individualmente e repetidas vezes. Tal tarefa é monótona e cansativa. Uma abordagem alternativa para obter amostras de símbolos seria solicitar aos usuários a transcrição de expressões modelo previamente definidas. Dessa forma, a escrita dos símbolos seria realizada de forma natural, menos monótona, e várias amostras de símbolos poderiam ser obtidas de uma única expressão. Para evitar o trabalho de anotar manualmente cada símbolo das expressões transcritas, este trabalho propõe um método para casamento de expressões matemáticas manuscritas, no qual símbolos de uma expressão transcrita por um usuário são associados aos correspondentes símbolos (previamente identificados) da expressão modelo. O método proposto é baseado em uma formulação que reduz o problema a um problema de associação simples, no qual os custos são definidos em termos de características dos símbolos e estrutura da expressão. Resultados experimentais utilizando o método proposto mostram taxas médias de associação correta superiores a 99%. / The problem of recognizing handwritten mathematical expressions includes three important subproblems: symbol segmentation, symbol recognition, and structural analysis of expressions. In order to evaluate recognition methods and techniques, they should be tested on representative sample sets of the application domain. One of the concerns that are being repeatedly pointed recently is the almost non-existence of public representative datasets of mathematical expressions, which makes difficult the development and comparison of distinct approaches. In general, recognition results reported in the literature are restricted to small datasets, not publicly available, and often consisting of data aiming only evaluation of some specific aspects of the recognition. In the case of online expressions, to train and test symbol recognizers, samples are in general obtained asking users to write a series of symbols individually and repeatedly. Such task is boring and tiring. An alternative approach for obtaining samples of symbols would be to ask users to transcribe previously defined model expressions. By doing so, writing would be more natural and less boring, and several symbol samples could be obtained from one transcription. To avoid the task of manually labeling the symbols of the transcribed expressions, in this work a method for handwritten expression matching, in which symbols of a transcribed expression are assigned to the corresponding ones in the model expression, is proposed. The proposed method is based on a formulation that reduces the matching problem to a linear assignment problem, where costs are defined based on symbol features and expression structure. Experimental results using the proposed method show that mean correct assignment rate superior to 99% is achieved.
36

Investigating and developing a model for iris changes under varied lighting conditions

Phang, Shiau Shing January 2007 (has links)
Biometric identification systems have several distinct advantages over other authentication technologies, such as passwords, in reliably recognising individuals. Iris based recognition is one such biometric recognition system. Unlike other biometrics such as fingerprints or face images, the distinct aspect of the iris comes from its randomly distributed features. The patterns of these randomly distributed features on the iris have been proved to be fixed in a person's lifetime, and are stable over time for healthy eyes except for the distortions caused by the constriction and dilation of the pupil. The distortion of the iris pattern caused by pupillary activity, which is mainly due changes in ambient lighting conditions, can be significant. One important question that arises from this is: How closely do two different iris images of the same person, taken at different times using different cameras, in different environments, and under different lighting conditions, agree with each other? It is also problematic for iris recognition systems to correctly identify a person when his/her pupil size is very different from the person's iris images, used at the time of constructing the system's data-base. To date, researchers in the field of iris recognition have made attempts to address this problem, with varying degrees of success. However, there is still a need to conduct in-depth investigations into this matter in order to arrive at more reliable solutions. It is therefore necessary to study the behaviour of iris surface deformation caused by the change of lighting conditions. In this thesis, a study of the physiological behaviour of pupil size variation under different normal indoor lighting conditions (100 lux ~ 1,200 lux) and brightness levels is presented. The thesis also presents the results of applying Elastic Graph Matching (EGM) tracking techniques to study the mechanisms of iris surface deformation. A study of the pupil size variation under different normal indoor lighting conditions was conducted. The study showed that the behaviour of the pupil size can be significantly different from one person to another under the same lighting conditions. There was no evidence from this study to show that the exact pupil sizes of an individual can be determined at a given illumination level. However, the range of pupil sizes can be estimated for a range of specific lighting conditions. The range of average pupil sizes under normal indoor lighting found was between 3 mm and 4 mm. One of the advantages of using EGM for iris surface deformation tracking is that it incorporates the benefit of the use of Gabor wavelets to encode the iris features for tracking. The tracking results showed that the radial stretch of the iris surface is nonlinear. However, the amount of extension of iris surface at any point on the iris during the stretch is approximately linear. The analyses of the tracking results also showed that the behaviour of iris surface deformation is different from one person to another. This implies that a generalised iris surface deformation model cannot be established for personal identification. However, a deformation model can be established for every individual based on their analysis result, which can be useful for personal verification using the iris. Therefore, analysis of the tracking results of each individual was used to model iris surface deformations for that individual. The model was able to estimate the movement of a point on the iris surface at a particular pupil size. This makes it possible to estimate and construct the 2D deformed iris image of a desired pupil size from a given iris image of another different pupil size. The estimated deformed iris images were compared with their actual images for similarity, using an intensitybased (zero mean normalised cross-correlation). The result shows that 86% of the comparisons have over 65% similarity between the estimated and actual iris image. Preliminary tests of the estimated deformed iris images using an open-source iris recognition algorithm have showed an improved personal verification performance. The studies presented in this thesis were conducted using a very small sample of iris images and therefore should not be generalised, before further investigations are conducted.
37

Super-resolution image processing with application to face recognition

Lin, Frank Chi-Hao January 2008 (has links)
Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.
38

Inspection automatisée d’assemblages mécaniques aéronautiques par vision artificielle : une approche exploitant le modèle CAO / Automated inspection of mechanical parts by computer vision : an approach based on CAD model

Viana do Espírito Santo, Ilísio 12 December 2016 (has links)
Les travaux présentés dans ce manuscrit s’inscrivent dans le contexte de l’inspection automatisée d’assemblages mécaniques aéronautiques par vision artificielle. Il s’agit de décider si l’assemblage mécanique a été correctement réalisé (assemblage conforme). Les travaux ont été menés dans le cadre de deux projets industriels. Le projet CAAMVis d’une part, dans lequel le capteur d’inspection est constitué d’une double tête stéréoscopique portée par un robot, le projet Lynx© d’autre part, dans lequel le capteur d’inspection est une caméra Pan/Tilt/Zoom (vision monoculaire). Ces deux projets ont pour point commun la volonté d’exploiter au mieux le modèle CAO de l’assemblage (qui fournit l’état de référence souhaité) dans la tâche d’inspection qui est basée sur l’analyse de l’image ou des images 2D fournies par le capteur. La méthode développée consiste à comparer une image 2D acquise par le capteur (désignée par « image réelle ») avec une image 2D synthétique, générée à partir du modèle CAO. Les images réelles et synthétiques sont segmentées puis décomposées en un ensemble de primitives 2D. Ces primitives sont ensuite appariées, en exploitant des concepts de la théorie de graphes, notamment l’utilisation d’un graphe biparti pour s’assurer du respect de la contrainte d’unicité dans le processus d’appariement. Le résultat de l’appariement permet de statuer sur la conformité ou la non-conformité de l’assemblage. L’approche proposée a été validée à la fois sur des données de simulation et sur des données réelles acquises dans le cadre des projets sus-cités. / The work presented in this manuscript deals with automated inspection of aeronautical mechanical parts using computer vision. The goal is to decide whether a mechanical assembly has been assembled correctly i.e. if it is compliant with the specifications. This work was conducted within two industrial projects. On one hand the CAAMVis project, in which the inspection sensor consists of a dual stereoscopic head (stereovision) carried by a robot, on the other hand the Lynx© project, in which the inspection sensor is a single Pan/Tilt/Zoom camera (monocular vision). These two projects share the common objective of exploiting as much as possible the CAD model of the assembly (which provides the desired reference state) in the inspection task which is based on the analysis of the 2D images provided by the sensor. The proposed method consists in comparing a 2D image acquired by the sensor (referred to as "real image") with a synthetic 2D image generated from the CAD model. The real and synthetic images are segmented and then decomposed into a set of 2D primitives. These primitives are then matched by exploiting concepts from the graph theory, namely the use of a bipartite graph to guarantee the respect of the uniqueness constraint required in such a matching process. The matching result allows to decide whether the assembly has been assembled correctly or not. The proposed approach was validated on both simulation data and real data acquired within the above-mentioned projects.
39

Rotulação de símbolos matemáticos manuscritos via casamento de expressões / Labeling of Handwritten Mathematical Symbols via Expression Matching

Willian Yukio Honda 23 January 2013 (has links)
O problema de reconhecimento de expressões matemáticas manuscritas envolve três subproblemas importantes: segmentação de símbolos, reconhecimento de símbolos e análise estrutural de expressões. Para avaliar métodos e técnicas de reconhecimento, eles precisam ser testados sobre conjuntos de amostras representativos do domínio de aplicação. Uma das preocupações que tem sido apontada ultimamente é a quase inexistência de base de dados pública de expressões matemáticas, o que dificulta o desenvolvimento e comparação de diferentes abordagens. Em geral, os resultados de reconhecimento apresentados na literatura restringem-se a conjuntos de dados pequenos, não disponíveis publicamente, e muitas vezes formados por dados que visam avaliar apenas alguns aspectos específicos do reconhecimento. No caso de expressões online, para treinar e testar reconhecedores de símbolos, as amostras são em geral obtidas solicitando-se que as pessoas escrevam uma série de símbolos individualmente e repetidas vezes. Tal tarefa é monótona e cansativa. Uma abordagem alternativa para obter amostras de símbolos seria solicitar aos usuários a transcrição de expressões modelo previamente definidas. Dessa forma, a escrita dos símbolos seria realizada de forma natural, menos monótona, e várias amostras de símbolos poderiam ser obtidas de uma única expressão. Para evitar o trabalho de anotar manualmente cada símbolo das expressões transcritas, este trabalho propõe um método para casamento de expressões matemáticas manuscritas, no qual símbolos de uma expressão transcrita por um usuário são associados aos correspondentes símbolos (previamente identificados) da expressão modelo. O método proposto é baseado em uma formulação que reduz o problema a um problema de associação simples, no qual os custos são definidos em termos de características dos símbolos e estrutura da expressão. Resultados experimentais utilizando o método proposto mostram taxas médias de associação correta superiores a 99%. / The problem of recognizing handwritten mathematical expressions includes three important subproblems: symbol segmentation, symbol recognition, and structural analysis of expressions. In order to evaluate recognition methods and techniques, they should be tested on representative sample sets of the application domain. One of the concerns that are being repeatedly pointed recently is the almost non-existence of public representative datasets of mathematical expressions, which makes difficult the development and comparison of distinct approaches. In general, recognition results reported in the literature are restricted to small datasets, not publicly available, and often consisting of data aiming only evaluation of some specific aspects of the recognition. In the case of online expressions, to train and test symbol recognizers, samples are in general obtained asking users to write a series of symbols individually and repeatedly. Such task is boring and tiring. An alternative approach for obtaining samples of symbols would be to ask users to transcribe previously defined model expressions. By doing so, writing would be more natural and less boring, and several symbol samples could be obtained from one transcription. To avoid the task of manually labeling the symbols of the transcribed expressions, in this work a method for handwritten expression matching, in which symbols of a transcribed expression are assigned to the corresponding ones in the model expression, is proposed. The proposed method is based on a formulation that reduces the matching problem to a linear assignment problem, where costs are defined based on symbol features and expression structure. Experimental results using the proposed method show that mean correct assignment rate superior to 99% is achieved.
40

Registrace fotografií do 3D modelu terénu / Registration of Photos to 3D Model

Deák, Jaromír January 2017 (has links)
This work refers existing solutions and options for the task registration of photos to 3D model based on the previous knowledge of the geographic position of the camera. The contribution of the work are new ways and possibilities of the solution with the usage of graph algorithms. In this area, the work interests are useful points of interest detection in input data, a construction of graphs and graph matching possibilities.

Page generated in 0.488 seconds