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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Three-Dimensional Design of Error Correcting Output Codes | Type | Conference Article | ||
Year | 2012 | Publication | 8th International Conference on Machine Learning and Data Mining | Abbreviated Journal | |
Volume | Issue | Pages | 29- | ||
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Address | Berlin, Germany | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | MLDM | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2012a | Serial | 2041 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Error Correcting Output Codes for multiclass classification: Application to two image vision problems | Type | Conference Article | ||
Year | 2012 | Publication | 16th symposium on Artificial Intelligence & Signal Processing | Abbreviated Journal | |
Volume | Issue | Pages | 508-513 | ||
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Abstract | Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches. | ||||
Address | Shiraz, Iran | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE Xplore | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-4673-1478-7 | Medium | ||
Area | Expedition | Conference | AISP | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2012b | Serial | 2042 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Efficient pairwise classification using Local Cross Off strategy | Type | Conference Article | ||
Year | 2012 | Publication | 25th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | 7310 | Issue | Pages | 25-36 | |
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Abstract | The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. | ||||
Address | Toronto, Ontario | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-30352-4 | Medium | |
Area | Expedition | Conference | AI | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2012c | Serial | 2044 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers | Type | Conference Article | ||
Year | 2013 | Publication | 26th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | 7884 | Issue | Pages | 1-12 | |
Keywords | Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature | ||||
Abstract | Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology. |
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Address | Canada; May 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-642-38456-1 | Medium | |
Area | Expedition | Conference | AI | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2013b | Serial | 2249 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding | Type | Journal Article | ||
Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 46 | Issue | 10 | Pages | 2830-2839 |
Keywords | Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification | ||||
Abstract | Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0031-3203 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2013a | Serial | 2247 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Combining Local and Global Learners in the Pairwise Multiclass Classification | Type | Journal Article | ||
Year | 2015 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 18 | Issue | 4 | Pages | 845-860 |
Keywords | Multiclass classification; Pairwise approach; One-versus-one | ||||
Abstract | Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes. | ||||
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Publisher | Springer London | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | 1433-7541 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2014 | Serial | 2441 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Generic Subclass Ensemble: A Novel Approach to Ensemble Classification | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1254 - 1259 | ||
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Abstract | Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches. | ||||
Address | Stockholm; August 2014 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1051-4651 | ISBN | Medium | ||
Area | Expedition | Conference | ICPR | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2014b | Serial | 2445 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels | Type | Conference Article | ||
Year | 2016 | Publication | 29th Canadian Conference on Artificial Intelligence | Abbreviated Journal | |
Volume | 9673 | Issue | Pages | 3-14 | |
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Abstract | In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect | ||||
Address | Victoria; Canada; May 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | AI | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ BGE2016b | Serial | 2770 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
Title | Support Vector Machines with Time Series Distance Kernels for Action Classification | Type | Conference Article | ||
Year | 2016 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1-7 | ||
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Abstract | Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets. |
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Address | Lake Placid; NY (USA); March 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ BGE2016a | Serial | 2773 | ||
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Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund | ||||
Title | Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning | Type | Conference Article | ||
Year | 2015 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 22-29 | ||
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Abstract | The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology. |
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Address | Boston; EEUU; June 2015 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ BGE2015 | Serial | 2655 | ||
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Author | Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva | ||||
Title | Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN | Type | Journal Article | ||
Year | 2023 | Publication | Food Control | Abbreviated Journal | FC |
Volume | 147 | Issue | Pages | 109554 | |
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Abstract | Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU. | ||||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ MNJ2023 | Serial | 3882 | ||
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Author | Mohammad N. S. Jahromi; Morten Bojesen Bonderup; Maryam Asadi-Aghbolaghi; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Shohreh Kasaei; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | Automatic Access Control Based on Face and Hand Biometrics in a Non-cooperative Context | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Winter Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 28-36 | ||
Keywords | IEEE Winter Applications of Computer Vision Workshops | ||||
Abstract | Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users. | ||||
Address | Lake Tahoe; USA; March 2018 | ||||
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Area | Expedition | Conference | WACVW | ||
Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ JBA2018 | Serial | 3121 | ||
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Author | Mohammad N. S. Jahromi; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
Title | Privacy-Constrained Biometric System for Non-cooperative Users | Type | Journal Article | ||
Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
Volume | 21 | Issue | 11 | Pages | 1033 |
Keywords | biometric recognition; multimodal-based human identification; privacy; deep learning | ||||
Abstract | With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ NBA2019 | Serial | 3313 | ||
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Author | Mohammad Rouhani | ||||
Title | 3D Data Fitting and Tracking for Real Time Applications | Type | Report | ||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 138 | Issue | 138 | Pages | |
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Address | Barcelona, Spain | ||||
Corporate Author | Thesis | Master's thesis | |||
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Notes | invisible;ADAS | Approved | no | ||
Call Number | Admin @ si @ Rou2009 | Serial | 1150 | ||
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Author | Mohammad Rouhani | ||||
Title | Shape Representation and Registration using Implicit Functions | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Shape representation and registration are two important problems in computer vision and graphics. Representing the given cloud of points through an implicit function provides a higher level information describing the data. This representation can be more compact more robust to noise and outliers, hence it can be exploited in different computer vision application. In the first part of this thesis implicit shape representations, including both implicit B-spline and polynomial, are tackled. First, an approximation of a geometric distance is proposed to measure the closeness of the given cloud of points and the implicit surface. The analysis of the proposed distance shows an accurate estimation with smooth behavior. The distance by itself is used in a RANSAC based quadratic fitting method. Moreover, since the gradient information of the distance with respect to the surface parameters can be analytically computed, it is used in Levenberg-Marquadt algorithm to refine the surface parameters. In a different approach, an algebraic fitting method is used to represent an object through implicit B-splines. The outcome is a smooth flexible surface and can be represented in different levels from coarse to fine. This property has been exploited to solve the registration problem in the second part of the thesis. In the proposed registration technique the model set is replaced with an implicit representation provided in the first part; then, the point-to-point registration is converted to a point-to-model one in a higher level. This registration error can benefit from different distance estimations to speed up the registration process even without need of correspondence search. Finally, the non-rigid registration problem is tackled through a quadratic distance approximation that is based on the curvature information of the model set. This approximation is used in a free form deformation model to update its control lattice. Then it is shown how an accurate distance approximation can benefit non-rigid registration problems. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Rou2012 | Serial | 2205 | ||
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