Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
42 Research products, page 1 of 5

  • Research software
  • Other research products
  • Open Access
  • Other ORP type
  • ES
  • English
  • Diposit Digital de Documents de la UAB

10
arrow_drop_down
Relevance
arrow_drop_down
  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Mendívil-Giró, José-Luis;
    Country: Spain
  • Open Access English
    Authors: 
    Donatti, Guillermo Sebastián;
    Country: Spain

    Using distributed representations of objects enables artificial systems to be more versatile regarding inter- and intra-category variability, improving the appearance-based modeling of visual object understanding. They are built on the hypothesis that object models are structured dynamically using relatively invariant patches of information arranged in visual dictionaries, which can be shared across objects from the same category. However, implementing distributed representations efficiently to support the complexity of invariant object recognition and categorization remains a research problem of outstanding significance for the biological, the psychological, and the computational approach to understanding visual perception. The present work focuses on solutions driven by top-down object knowledge. It is motivated by the idea that, equipped with sensors and processing mechanisms from the neural pathways serving visual perception, biological systems are able to define efficient measures of similarities between properties observed in objects and use these relationships to form natural clusters of object parts that share equivalent ones. Based on the comparison of stimulus-response signatures from these object-to-memory mappings, biological systems are able to identify objects and their kinds. The present work combines biologically inspired mathematical models to develop memory frameworks for artificial systems, where these invariant patches are represented with regular-shaped graphs, whose nodes are labeled with elementary features that capture texture information from object images. It also applies unsupervised clustering techniques to these graph image features to corroborate the existence of natural clusters within their data distribution and determine their composition. The properties of such computational theory include self-organization and intelligent matching of these graph image features based on the similarity and co-occurrence of their captured texture information. The performance to model invariant object recognition and categorization of feature-based artificial systems equipped with each of the developed memory frameworks is validated applying standard methodologies to well-known image libraries found in literature. Additionally, these artificial systems are cross-compared with state-of-the-art alternative solutions. In conclusion, the findings of the present work convey implications for strategies and experimental paradigms to analyze human object memory as well as technical applications for robotics and computer vision.

  • Open Access English
    Authors: 
    Castrillo, C.; Baucells, M.; Vicente, F.; Muñoz, F.; Andueza, D.;
    Publisher: Wiley-Blackwell
    Country: Spain

    Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition, apparent digestibility and digestible nutrients and energy content of commercial extruded compound foods for dogs. Fifty-six foods of known chemical composition and in vivo apparent digestibility were analysed overall and 51 foods were used to predict gross energy digestibility and digestible energy content. Modified partial least square calibration models were developed for organic matter (OM), crude protein (CP), ether extract (EE), crude fibre (CF), nitrogen free extracts (NFE) and gross energy (GE) content, the apparent digestibility (OMD, CPD, EED, NFED and GED) and the digestible nutrient and energy content (DOM, DCP, DEE, DNFE and DE) of foods. The calibration equations obtained were evaluated by the standard error and the determination coefficient of cross-validation. The cross-validation coefficients of determination (R) were 0.61, 0.99, 0.91, 0.96, 0.94 and 0.92 for OM, CP, EE, CF, NFE and GE, the corresponding standard error of cross-validation (SECV) being 5.80, 3.51, 13.35, 3.64 and 16.95 g/kg dry matter (DM) and 0.29 MJ/kg DM respectively. The prediction of apparent digestibility was slightly less accurate, but NIRS prediction of digestible nutrient (g/kg DM) and DE (MJ/kg DM) gave satisfactory results, with high R (0.93, 0.97, 0.93, 0.83 and 0.93 for DOM, DCP, DEE, DNFE and DE respectively) and relatively low SECV (11.55, 6.85, 12.14 and 22.98 g/kg DM and 0.47 MJ/kg DM). It is concluded that the precision of NIRS in predicting the energy value of compound extruded foods for dogs is similar or better than by proximate analysis, as well as being faster and more accurate.

  • Open Access English
    Authors: 
    Boucher, Arnaud;
    Country: Spain

    Advisor: Nicole Vincent. Date and location of PhD thesis defense: 10 January 2013, University of Paris Descartes In this thesis, the problem addressed is the development of a computer-aided diagnosis system (CAD) based on conjoint analysis of several images, and therefore on the comparison of these medical images. The particularity of our approach is to look for evolutions or aberrant new tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of tissues. This problem allows to apprehend one aspect of the analysis of a medical file performed by experts which is the study of a case through comparison and evolution detection. The methodology proposed is carried out within the application context of the development of a CAD applied to mammograms. The first step when a couple of images are involved is to perform an adapted registration. Any automated comparison of signals requires an alignment of similar components present on the pictures, that is to say a registration phase, so that they occupy the same space on the two images. As the registration is never perfect, we must take into account the level of uncertainty and develop a comparison method able to distinguish registration error and real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used in the interpretation step that yields to prompt suspicious regions. In our case, registration is assumed to match the spatial coordinates of similar anatomical elements.

  • Open Access English
    Authors: 
    Lessmann, Markus;
    Country: Spain

    Advisor: Rolf P. Würtz, Institute for Neural Computation, Ruhr-University Bochum, Germany. Date and location of PhD thesis defense: 3 November 2014, Ruhr-University Bochum, Germany There has been a lot of progress in the field of invariant object recognition/categorization in the last decade with several methods trying to mimic functioning of the human visual system (e.g. Neocognitron, HMAX, VisNet). Examining those brain regions is a very difficult task with myriads of details to be considered. To simplify modeling approaches, Jeff Hawkins [1] proposed a framework of three basic principles that might underlie computations in regions of the neocortex. These also form the basis for a capable object recognition system named "Hierarchical Temporal Memory" (HTM). 1. Learning of temporal sequences for creating invariance to transformations contained in the training data. 2. Learning in a hierarchical structure, in which lower level knowledge can be reused in higher level context and thereby makes memory usage efficient. 3. Prediction of future signals for disambiguation of noisy input by feedback.

  • Open Access English
    Authors: 
    Gutierrez Gómez, Daniel;
    Country: Spain

    Under the rapid development of electronics and computer science in the last years, cameras have becomeomnipresent nowadays, to such extent that almost everybody is able to carry one at all times embedded intotheir cellular phone. What makes cameras specially appealing for us is their ability to quickly capture a lot ofinformation of the environment encoded in one image or video, allowing us to immortalize special moments inour life or share reliable visual information of the environment with other persons. However, while the task ofextracting the information from an image may by trivial for us, in the case of computers complex algorithmswith a high computational burden are required to transform a raw image into useful information. In this sense, the same rapid development in computer science that allowed the widespread of cameras has enabled also the possibility of real-time application of previously practically infeasible algorithms.Among the current fields of research in the computer vision community, this thesis is specially concerned inmetric localisation and mapping algorithms. These algorithms are a key component in many practical applications such as robot navigation, augmented reality or reconstructing 3D models of the environment.The goal of this thesis is to delve into visual localisation and mapping from vision, paying special attentionto conventional and unconventional cameras which can be easily worn or handled by a human. In this thesis Icontribute in the following aspects of the visual odometry and SLAM (Simultaneous Localisation and Mapping)pipeline:- Generalised Monocular SLAM for catadioptric central cameras- Resolution of the scale problem in monocular vision- Dense RGB-D odometry- Robust place recognition- Pose-graph optimisation

  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Othman, Alaa Tharwat;
    Country: Spain

    Advisor/s: Atalla I. Hashad, Gouda I. Salama. Date and location of PhD thesis defense: May 2014, AASTMT Biometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, \textit{Principal Component Analysis} (PCA), \textit{Linear Discriminant Analysis} (LDA), \textit{Independent Component Analysis} (ICA), and \textit{Discrete Cousin Transform} (DCT). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1]. The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12\% to 100\%. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12\% to 96.08\%. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded.

  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Haspelmath, Martin;
    Country: Spain
  • Open Access English
    Authors: 
    Sánchez Ramos, Carles;
    Country: Spain

    Advisor/s: F. Javier Sánchez, Debora Gil and Jorge Bernal. Date and location of PhD thesis defense: 16 December 2014, Autonomous University of Barcelona Recent advances in endoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures. Among all endoscopic modalities, bronchoscopy is one of the most frequent with around 261 millions of procedures per year. Although the use of bronchoscopy is spread among clinical facilities it presents some drawbacks, being the visual inspection for the assessment of anatomical measurements the most prevalent of them. In particular, inaccuracies in the estimation of the degree of stenosis (the percentage of obstructed airway) decreases its diagnostic yield and might lead to erroneous treatments. An objective computation of tracheal stenosis in bronchoscopy videos would constitute a breakthrough for this non-invasive technique and a reduction in treatment cost.This thesis settles the first steps towards on-line reliable extraction of anatomical information from videobronchoscopy for computation of objective measures. In particular, we focus on the computation of the degree of stenosis, which is obtained by comparing the area delimited by a healthy tracheal ring and the stenosed lumen. In this sense, we have to consider that reliable extraction of airway structures in interventional videobronchoscopy is a challenging task. This is mainly due to the large variety of acquisition conditions (positions and illumination), devices (different digitalizations) and in videosacquired at the operating room the unpredicted presence of surgical devices (such as probe ends). This thesis contributes to on-line stenosis assessment in several ways. We propose a parametric strategy for the extraction of lumen and tracheal rings regions based on the characterization of their geometry and appearance that guide a deformable model. The geometric and appearance characterization is based on a physical model describing the way bronchoscopy images are obtained and includes local and global descriptions. In order to ensure a systematic applicability we present a statistical framework to select the optimal parameters of our method. Experiments perform on the first public annotated database, show that the performance of our method is comparable to the one provided by clinicians and its computation time allows for a on-line implementation in the operating room.

  • Open Access English
    Authors: 
    Mylona, Eleftheria A.;
    Country: Spain

    Advisor: Dimitris Maroulis. PhD thesis defended 9th January 2014, National and Kapodistrian University of Athens, Ilissia, Athens,Greece This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Segmentation is a challenging task in computer vision with essential applications in biomedical engineering, remote sensing, robotics and automation. Typically, the target region is separated from the rest of image regions utilizing defining features including intensity, texture, color or motion cues. In this light, multiple segments are generated and the selection of the most significant segments becomes a controversial decision as it highly hinges on heuristic considerations. Moreover, the separation of the target regions is impeded by several daunting factors such as: background clutter, the presence of noise and artifacts as well as occlusions on multiple target regions. This thesis focuses on image segmentation using deformable models and specifically region-based Active Contours (ACs) because of their strong mathematical foundation and their appealing properties.

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
42 Research products, page 1 of 5
  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Mendívil-Giró, José-Luis;
    Country: Spain
  • Open Access English
    Authors: 
    Donatti, Guillermo Sebastián;
    Country: Spain

    Using distributed representations of objects enables artificial systems to be more versatile regarding inter- and intra-category variability, improving the appearance-based modeling of visual object understanding. They are built on the hypothesis that object models are structured dynamically using relatively invariant patches of information arranged in visual dictionaries, which can be shared across objects from the same category. However, implementing distributed representations efficiently to support the complexity of invariant object recognition and categorization remains a research problem of outstanding significance for the biological, the psychological, and the computational approach to understanding visual perception. The present work focuses on solutions driven by top-down object knowledge. It is motivated by the idea that, equipped with sensors and processing mechanisms from the neural pathways serving visual perception, biological systems are able to define efficient measures of similarities between properties observed in objects and use these relationships to form natural clusters of object parts that share equivalent ones. Based on the comparison of stimulus-response signatures from these object-to-memory mappings, biological systems are able to identify objects and their kinds. The present work combines biologically inspired mathematical models to develop memory frameworks for artificial systems, where these invariant patches are represented with regular-shaped graphs, whose nodes are labeled with elementary features that capture texture information from object images. It also applies unsupervised clustering techniques to these graph image features to corroborate the existence of natural clusters within their data distribution and determine their composition. The properties of such computational theory include self-organization and intelligent matching of these graph image features based on the similarity and co-occurrence of their captured texture information. The performance to model invariant object recognition and categorization of feature-based artificial systems equipped with each of the developed memory frameworks is validated applying standard methodologies to well-known image libraries found in literature. Additionally, these artificial systems are cross-compared with state-of-the-art alternative solutions. In conclusion, the findings of the present work convey implications for strategies and experimental paradigms to analyze human object memory as well as technical applications for robotics and computer vision.

  • Open Access English
    Authors: 
    Castrillo, C.; Baucells, M.; Vicente, F.; Muñoz, F.; Andueza, D.;
    Publisher: Wiley-Blackwell
    Country: Spain

    Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition, apparent digestibility and digestible nutrients and energy content of commercial extruded compound foods for dogs. Fifty-six foods of known chemical composition and in vivo apparent digestibility were analysed overall and 51 foods were used to predict gross energy digestibility and digestible energy content. Modified partial least square calibration models were developed for organic matter (OM), crude protein (CP), ether extract (EE), crude fibre (CF), nitrogen free extracts (NFE) and gross energy (GE) content, the apparent digestibility (OMD, CPD, EED, NFED and GED) and the digestible nutrient and energy content (DOM, DCP, DEE, DNFE and DE) of foods. The calibration equations obtained were evaluated by the standard error and the determination coefficient of cross-validation. The cross-validation coefficients of determination (R) were 0.61, 0.99, 0.91, 0.96, 0.94 and 0.92 for OM, CP, EE, CF, NFE and GE, the corresponding standard error of cross-validation (SECV) being 5.80, 3.51, 13.35, 3.64 and 16.95 g/kg dry matter (DM) and 0.29 MJ/kg DM respectively. The prediction of apparent digestibility was slightly less accurate, but NIRS prediction of digestible nutrient (g/kg DM) and DE (MJ/kg DM) gave satisfactory results, with high R (0.93, 0.97, 0.93, 0.83 and 0.93 for DOM, DCP, DEE, DNFE and DE respectively) and relatively low SECV (11.55, 6.85, 12.14 and 22.98 g/kg DM and 0.47 MJ/kg DM). It is concluded that the precision of NIRS in predicting the energy value of compound extruded foods for dogs is similar or better than by proximate analysis, as well as being faster and more accurate.

  • Open Access English
    Authors: 
    Boucher, Arnaud;
    Country: Spain

    Advisor: Nicole Vincent. Date and location of PhD thesis defense: 10 January 2013, University of Paris Descartes In this thesis, the problem addressed is the development of a computer-aided diagnosis system (CAD) based on conjoint analysis of several images, and therefore on the comparison of these medical images. The particularity of our approach is to look for evolutions or aberrant new tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of tissues. This problem allows to apprehend one aspect of the analysis of a medical file performed by experts which is the study of a case through comparison and evolution detection. The methodology proposed is carried out within the application context of the development of a CAD applied to mammograms. The first step when a couple of images are involved is to perform an adapted registration. Any automated comparison of signals requires an alignment of similar components present on the pictures, that is to say a registration phase, so that they occupy the same space on the two images. As the registration is never perfect, we must take into account the level of uncertainty and develop a comparison method able to distinguish registration error and real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used in the interpretation step that yields to prompt suspicious regions. In our case, registration is assumed to match the spatial coordinates of similar anatomical elements.

  • Open Access English
    Authors: 
    Lessmann, Markus;
    Country: Spain

    Advisor: Rolf P. Würtz, Institute for Neural Computation, Ruhr-University Bochum, Germany. Date and location of PhD thesis defense: 3 November 2014, Ruhr-University Bochum, Germany There has been a lot of progress in the field of invariant object recognition/categorization in the last decade with several methods trying to mimic functioning of the human visual system (e.g. Neocognitron, HMAX, VisNet). Examining those brain regions is a very difficult task with myriads of details to be considered. To simplify modeling approaches, Jeff Hawkins [1] proposed a framework of three basic principles that might underlie computations in regions of the neocortex. These also form the basis for a capable object recognition system named "Hierarchical Temporal Memory" (HTM). 1. Learning of temporal sequences for creating invariance to transformations contained in the training data. 2. Learning in a hierarchical structure, in which lower level knowledge can be reused in higher level context and thereby makes memory usage efficient. 3. Prediction of future signals for disambiguation of noisy input by feedback.

  • Open Access English
    Authors: 
    Gutierrez Gómez, Daniel;
    Country: Spain

    Under the rapid development of electronics and computer science in the last years, cameras have becomeomnipresent nowadays, to such extent that almost everybody is able to carry one at all times embedded intotheir cellular phone. What makes cameras specially appealing for us is their ability to quickly capture a lot ofinformation of the environment encoded in one image or video, allowing us to immortalize special moments inour life or share reliable visual information of the environment with other persons. However, while the task ofextracting the information from an image may by trivial for us, in the case of computers complex algorithmswith a high computational burden are required to transform a raw image into useful information. In this sense, the same rapid development in computer science that allowed the widespread of cameras has enabled also the possibility of real-time application of previously practically infeasible algorithms.Among the current fields of research in the computer vision community, this thesis is specially concerned inmetric localisation and mapping algorithms. These algorithms are a key component in many practical applications such as robot navigation, augmented reality or reconstructing 3D models of the environment.The goal of this thesis is to delve into visual localisation and mapping from vision, paying special attentionto conventional and unconventional cameras which can be easily worn or handled by a human. In this thesis Icontribute in the following aspects of the visual odometry and SLAM (Simultaneous Localisation and Mapping)pipeline:- Generalised Monocular SLAM for catadioptric central cameras- Resolution of the scale problem in monocular vision- Dense RGB-D odometry- Robust place recognition- Pose-graph optimisation

  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Othman, Alaa Tharwat;
    Country: Spain

    Advisor/s: Atalla I. Hashad, Gouda I. Salama. Date and location of PhD thesis defense: May 2014, AASTMT Biometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, \textit{Principal Component Analysis} (PCA), \textit{Linear Discriminant Analysis} (LDA), \textit{Independent Component Analysis} (ICA), and \textit{Discrete Cousin Transform} (DCT). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1]. The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12\% to 100\%. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12\% to 96.08\%. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded.

  • Other research product . Other ORP type . 2021
    Open Access English
    Authors: 
    Haspelmath, Martin;
    Country: Spain
  • Open Access English
    Authors: 
    Sánchez Ramos, Carles;
    Country: Spain

    Advisor/s: F. Javier Sánchez, Debora Gil and Jorge Bernal. Date and location of PhD thesis defense: 16 December 2014, Autonomous University of Barcelona Recent advances in endoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures. Among all endoscopic modalities, bronchoscopy is one of the most frequent with around 261 millions of procedures per year. Although the use of bronchoscopy is spread among clinical facilities it presents some drawbacks, being the visual inspection for the assessment of anatomical measurements the most prevalent of them. In particular, inaccuracies in the estimation of the degree of stenosis (the percentage of obstructed airway) decreases its diagnostic yield and might lead to erroneous treatments. An objective computation of tracheal stenosis in bronchoscopy videos would constitute a breakthrough for this non-invasive technique and a reduction in treatment cost.This thesis settles the first steps towards on-line reliable extraction of anatomical information from videobronchoscopy for computation of objective measures. In particular, we focus on the computation of the degree of stenosis, which is obtained by comparing the area delimited by a healthy tracheal ring and the stenosed lumen. In this sense, we have to consider that reliable extraction of airway structures in interventional videobronchoscopy is a challenging task. This is mainly due to the large variety of acquisition conditions (positions and illumination), devices (different digitalizations) and in videosacquired at the operating room the unpredicted presence of surgical devices (such as probe ends). This thesis contributes to on-line stenosis assessment in several ways. We propose a parametric strategy for the extraction of lumen and tracheal rings regions based on the characterization of their geometry and appearance that guide a deformable model. The geometric and appearance characterization is based on a physical model describing the way bronchoscopy images are obtained and includes local and global descriptions. In order to ensure a systematic applicability we present a statistical framework to select the optimal parameters of our method. Experiments perform on the first public annotated database, show that the performance of our method is comparable to the one provided by clinicians and its computation time allows for a on-line implementation in the operating room.

  • Open Access English
    Authors: 
    Mylona, Eleftheria A.;
    Country: Spain

    Advisor: Dimitris Maroulis. PhD thesis defended 9th January 2014, National and Kapodistrian University of Athens, Ilissia, Athens,Greece This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Segmentation is a challenging task in computer vision with essential applications in biomedical engineering, remote sensing, robotics and automation. Typically, the target region is separated from the rest of image regions utilizing defining features including intensity, texture, color or motion cues. In this light, multiple segments are generated and the selection of the most significant segments becomes a controversial decision as it highly hinges on heuristic considerations. Moreover, the separation of the target regions is impeded by several daunting factors such as: background clutter, the presence of noise and artifacts as well as occlusions on multiple target regions. This thesis focuses on image segmentation using deformable models and specifically region-based Active Contours (ACs) because of their strong mathematical foundation and their appealing properties.

Send a message
How can we help?
We usually respond in a few hours.