This study reports on the quantitative evaluation of a set of state-of-the-art feature detectors and descriptors in the context of repeat photography. Unlike most related work, the proposed study assesses the performance of feature detectors when intra-pair variations are uncontrolled and due to a variety of factors (landscape change, weather conditions, different acquisition sensors). There is no systematic way to model the factors inducing image change. The proposed evaluation is performed in the context of image matching, i.e. in conjunction with a descriptor and matching strategy. Thus, beyond just comparing the performance of these detectors and descriptors, we also examine the feasibility of feature-based matching on repeat photography. Our dataset consists of a set of repeat and historic images pairs that are representative for the database created by the Mountain Legacy Project www.mountainlegacy.ca. / Graduate

L’objectif du travail de thèse est de proposer un modèle de représentation des écritures dans les images de documents du patrimoine sans recourir à une transcription des textes. Ce modèle, issu d’une étude très complète des méthodes actuelles de caractérisation des écritures, est à la base d’une proposition de scénario de recherche par similarité de mots, indépendante du scripteur et ne nécessitant pas d’apprentissage. La recherche par similarité proposée repose sur une structure de graphes intégrant des informations sur la topologie, la morphologie locale des mots et sur le contexte extrait du voisinage de chaque point d’intérêt. Un graphe est construit à partir du squelette décrit en chaque point sommet par le contexte de formes, descripteur riche et compact. L’extraction de mots est assurée par une première étape de localisation grossière de régions candidates, décrites par une séquence déduite d’une représentation par graphes liée à des critères topologiques de voisinage. L’appariement entre mots repose ensuite sur une distance dynamique et un usage adapté du coût d’édition approximé entre graphes rendant compte de la nature bi-dimensionnelle de l’écriture. L’approche a été conçue pour être robuste aux distorsions de l’écriture et aux changements de scripteurs. Les expérimentations sont réalisées sur des bases de documents manuscrits patrimoniaux exploitées dans les compétitions de word-spotting. Les performances illustrent la pertinence de la proposition et ouvrent des voies nouvelles d’investigation dans des domaines d’applications autour de la reconnaissance de symboles et d’écritures iconographiques / As more and more documents, especially historical handwritten documents, are converted into digitized version for long-term preservation, the demands for efficient information retrieval techniques in such document images are increasing. The objective of this research is to establish an effective representation model for handwriting, especially historical manuscripts. The proposed model is supposed to help the navigation in historical document collections. Specifically speaking, we developed our handwriting representation model with regards to word spotting application. As a specific pattern recognition task, handwritten word spotting faces many challenges such as the high intra-writer and inter-writer variability. Nowadays, it has been admitted that OCR techniques are unsuccessful in handwritten offline documents, especially historical ones. Therefore, the particular characterization and comparison methods dedicated to handwritten word spotting are strongly required. In this work, we explore several techniques that allow the retrieval of singlestyle handwritten document images with query image. The proposed representation model contains two facets of handwriting, morphology and topology. Based on the skeleton of handwriting, graphs are constructed with the structural points as the vertexes and the strokes as the edges. By signing the Shape Context descriptor as the label of vertex, the contextual information of handwriting is also integrated. Moreover, we develop a coarse-to-fine system for the large-scale handwritten word spotting using our representation model. In the coarse selection, graph embedding is adapted with consideration of simple and fast computation. With selected regions of interest, in the fine selection, a specific similarity measure based on graph edit distance is designed. Regarding the importance of the order of handwriting, dynamic time warping assignment with block merging is added. The experimental results using benchmark handwriting datasets demonstrate the power of the proposed representation model and the efficiency of the developed word spotting approach. The main contribution of this work is the proposed graph-based representation model, which realizes a comprehensive description of handwriting, especially historical script. Our structure-based model captures the essential characteristics of handwriting without redundancy, and meanwhile is robust to the intra-variation of handwriting and specific noises. With additional experiments, we have also proved the potential of the proposed representation model in other symbol recognition applications, such as handwritten musical and architectural classification

In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were then repaired through a new network with shape context information. In this experiment, we created a new network structure by rebuilding the U-net into a 2-input structure and refined the processing pipeline step. In this proposed pipeline, the datasets and shape context were trained together through the new network model by iteration. The proposed method was evaluated on 247 posterior-anterior chest radiographs of public datasets and n-folds cross-validation was also used. The outcome shows that compared to origin U-net, the proposed pipeline reaches higher accuracy when trained with limited datasets. Here the "limited" datasets refer to 1-20 images in the medical image field. A better outcome with higher accuracy can be reached if the second structure is further refined and shape context generator's parameter is fine-tuned in the future. / Under de senaste åren har bildsegmentering med hjälp av djupa neurala nätverk gjort stora framsteg. Att nå ett bra resultat med träning med en liten mängd data kvarstår emellertid som en utmaning. För att hitta ett bra sätt att förbättra noggrannheten i segmenteringen med begränsade datamängder så implementerade vi en ny segmentering för automatiska röntgenbilder av bröstkorgsdiagram baserat på tidigare forskning av Chunliang. Detta tillvägagångssätt använder djupt lärande neurala nätverk kombinerat med "shape context" information. I detta experiment skapade vi en ny nätverkstruktur genom omkonfiguration av U-nätverket till en 2-inputstruktur och förfinade pipeline processeringssteget där bilden och "shape contexten" var tränade tillsammans genom den nya nätverksmodellen genom iteration.Den föreslagna metoden utvärderades på dataset med 247 bröströntgenfotografier, och n-faldig korsvalidering användes för utvärdering. Resultatet visar att den föreslagna pipelinen jämfört med ursprungs U-nätverket når högre noggrannhet när de tränas med begränsade datamängder. De "begränsade" dataseten här hänvisar till 1-20 bilder inom det medicinska fältet. Ett bättre resultat med högre noggrannhet kan nås om den andra strukturen förfinas ytterligare och "shape context-generatorns" parameter finjusteras.

Orientador: Aparecido Nilceu Marana / Banca: Adilson Gonzaga / Banca: Humberto Ferasoli Filho / Resumo: Diversos métodos biométricos baseados em características físicas do corpo humano como impressão digital, face, íris e retina têm sido propostos para identificação humana. No entanto, para a identificação post-mortem, tais características biométricas podem não estar disponíveis. Nestes casos, partes do esqueleto do corpo humano podem ser utilizadas para identificação, tais como dentes, tórax, vértebras, ombros e os sinus frontais. Investigações anteriores mostraram, por meio de técnicas manuais para extração de características, que os padrões dos sinus frontais são altamente variáveis entre indivíduos distintos e únicos para cada indivíduo. Esta dissertação de mestrado tem por objetivo propor um método computacional para o reconhecimento de sinus frontais para identificação humana post-mortem em aplicações forenses. Para tanto, foram avaliados métodos de segmentação de imagens de radiografias anteroposteriores de sinus frontais. O método baseado na Transformada Imagem-Floresta demonstrou ser eficiente para segmentação dos sinus frontais das imagens de radiografias, exigindo mínima intervenção humana. Foram também investigadas e implementadas técnicas para extração de descritores geométricos e descritores baseados nas formas dos sinus frontais. Experimentos realizados em um banco de imagens contendo 90 radiografias anteroposteriores de 29 indivíduos mostraram que a técnica de extração de características baseada nos descritores de contexto da forma foi a mais eficaz, propiciando taxas de erro igual (EER) e de recuperações corretas (CRR) de 3,73% e 95,5%, respectivamente. Os resultados obtidos nos experimentos corroboram os encontrados na literatura sobre a individualidade dos sinus frontais e sua viabilidade em termos de precisão e usabilidade para a identificação humana post-mortem. Palavras-chave: Biometria, identificação... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Several methods based on Biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for postmortem identification such biometric measurements may not be available. In such cases, parts of the human skeleton can be used for identification, such as teeth, thorax, vertebrae, shoulders, and frontal sinus. Previous investigations showed, by means of manual features extraction techniques, that frontal sinus patterns are highly variable for distinctive individuals and unique for each one. The objective of this master thesis is to propose a computational method for frontal sinus recognition for postmortem human identification in forensic applications. In order to achieve this, methods for frontal sinus segmentation from anteroposterior radiographs were evaluated. The method based on Image-Foresting Transform has shown itself efficient in frontal sinus segmentation from radiograph images, demanding minimal human intervention. After the segmentation, techniques for extracting frontal sinus geometrical and shape-based descriptors were investigated and implemented. Experiments over a database containing 90 anteroposterior radiograph images from 29 individuals have shown that the features extraction techniques based on shape context descriptors were the most efficient, providing equal error (EER) and correct retrievals (CRR) rates of 3.73% and 95,5%, respectively. The results obtained in our experiments confirm the outcomes described in literature about the individuality of the frontal sinus and its feasibility in terms of precision and usability for postmortem human identification. Keywords: Biometrics, forensics human... (Complete abstract click electronic access below) / Mestre

Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-06-23Bitstream added on 2014-06-13T19:18:04Z : No. of bitstreams: 1 falguera_jr_me_sjrp.pdf: 2234581 bytes, checksum: 19293ff7ecaf5caa8cf4417a59cb11fa (MD5) / Diversos métodos biométricos baseados em características físicas do corpo humano como impressão digital, face, íris e retina têm sido propostos para identificação humana. No entanto, para a identificação post-mortem, tais características biométricas podem não estar disponíveis. Nestes casos, partes do esqueleto do corpo humano podem ser utilizadas para identificação, tais como dentes, tórax, vértebras, ombros e os sinus frontais. Investigações anteriores mostraram, por meio de técnicas manuais para extração de características, que os padrões dos sinus frontais são altamente variáveis entre indivíduos distintos e únicos para cada indivíduo. Esta dissertação de mestrado tem por objetivo propor um método computacional para o reconhecimento de sinus frontais para identificação humana post-mortem em aplicações forenses. Para tanto, foram avaliados métodos de segmentação de imagens de radiografias anteroposteriores de sinus frontais. O método baseado na Transformada Imagem-Floresta demonstrou ser eficiente para segmentação dos sinus frontais das imagens de radiografias, exigindo mínima intervenção humana. Foram também investigadas e implementadas técnicas para extração de descritores geométricos e descritores baseados nas formas dos sinus frontais. Experimentos realizados em um banco de imagens contendo 90 radiografias anteroposteriores de 29 indivíduos mostraram que a técnica de extração de características baseada nos descritores de contexto da forma foi a mais eficaz, propiciando taxas de erro igual (EER) e de recuperações corretas (CRR) de 3,73% e 95,5%, respectivamente. Os resultados obtidos nos experimentos corroboram os encontrados na literatura sobre a individualidade dos sinus frontais e sua viabilidade em termos de precisão e usabilidade para a identificação humana post-mortem. Palavras-chave: Biometria, identificação... / Several methods based on Biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for postmortem identification such biometric measurements may not be available. In such cases, parts of the human skeleton can be used for identification, such as teeth, thorax, vertebrae, shoulders, and frontal sinus. Previous investigations showed, by means of manual features extraction techniques, that frontal sinus patterns are highly variable for distinctive individuals and unique for each one. The objective of this master thesis is to propose a computational method for frontal sinus recognition for postmortem human identification in forensic applications. In order to achieve this, methods for frontal sinus segmentation from anteroposterior radiographs were evaluated. The method based on Image-Foresting Transform has shown itself efficient in frontal sinus segmentation from radiograph images, demanding minimal human intervention. After the segmentation, techniques for extracting frontal sinus geometrical and shape-based descriptors were investigated and implemented. Experiments over a database containing 90 anteroposterior radiograph images from 29 individuals have shown that the features extraction techniques based on shape context descriptors were the most efficient, providing equal error (EER) and correct retrievals (CRR) rates of 3.73% and 95,5%, respectively. The results obtained in our experiments confirm the outcomes described in literature about the individuality of the frontal sinus and its feasibility in terms of precision and usability for postmortem human identification. Keywords: Biometrics, forensics human... (Complete abstract click electronic access below)

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.

Les techniques de description des éléments caractéristiques d’une image sont omniprésentes dans de nombreuses applications de vision par ordinateur. Nous proposons à travers ce manuscrit une extension, pour décrire (représenter) et apparier les éléments caractéristiques des images. L’extension proposée consiste en une approche originale pour apprendre, ou estimer, la présence sémantique des éléments caractéristiques locaux dans les images. L’information sémantique obtenue est ensuite exploitée, en conjonction avec le paradigme de sac-de-mots, pour construire un descripteur d’image performant. Le descripteur résultant, est la combinaison de deux types d’informations, locale et contextuelle-sémantique. L’approche proposée peut être généralisée et adaptée à n’importe quel descripteur local d’image, pour améliorer fortement ses performances spécialement quand l’image est soumise à des conditions d’imagerie contraintes. La performance de l’approche proposée est évaluée avec des images réelles aussi bien dans les deux domaines, 2D que 3D. Nous avons abordé dans le domaine 2D, un problème lié à l’appariement des éléments caractéristiques dans des images. Dans le domaine 3D, nous avons résolu les problèmes d’appariement et alignement des vues partielles tridimensionnelles. Les résultats obtenus ont montré qu’avec notre approche, les performances sont nettement meilleures par rapport aux autres méthodes existantes. / This manuscript presents an extension of feature description and matching strategies by proposing an original approach to learn the semantic information of local features. This semantic is then exploited, in conjunction with the bag-of-words paradigm, to build a powerful feature descriptor. The approach, ended up by combining local and context information into a single descriptor, is also a generalized method for improving the performance of the local features, in terms of distinctiveness and robustness under geometric image transformations and imaging conditions. The performance of the proposed approach is evaluated on real world data sets as well as in both the 2D and 3D domains. The 2D domain application addresses the problem of image feature matching while in 3D domain, we resolve the issue of matching and alignment of multiple range images. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.