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Author | Antonio Hernandez; Stan Sclaroff; Sergio Escalera | ||||
Title | Contextual rescoring for Human Pose Estimation | Type | Conference Article | ||
Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | A contextual rescoring method is proposed for improving the detection of body joints of a pictorial structure model for human pose estimation. A set of mid-level parts is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body joint hypotheses. A technique is proposed for the automatic discovery of a compact subset of poselets that covers a set of validation images
while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for body joint detections, given its relationship to detections of other body joints and mid-level parts in the image. This new score complements the unary potential of a discriminatively trained pictorial structure model. Experiments on two benchmarks show performance improvements when considering the proposed mid-level image representation and rescoring approach in comparison with other pictorial structure-based approaches. |
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Address | Nottingham; UK; September 2013 | ||||
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 | BMVC | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | HSE2014 | Serial | 2525 | ||
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Author | David Fernandez | ||||
Title | Contextual Word Spotting in Historical Handwritten Documents | Type | Book Whole | ||
Year | 2014 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | There are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners.
There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely dicult due the inherent deciencies: poor physical preservation, dierent writing styles, obsolete languages, etc. Word spotting has become a popular an ecient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach. The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an ecient word segmentation is needed. Historical handwritten documents present some common diculties that can increase the diculties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as nding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path nding algorithm is used to nd the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art. Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an ecient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them. The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Alicia Fornes | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-940902-7-1 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG; 600.077 | Approved | no | ||
Call Number | Admin @ si @ Fer2014 | Serial | 2573 | ||
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Author | David Fernandez; Simone Marinai; Josep Llados; Alicia Fornes | ||||
Title | Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks | Type | Conference Article | ||
Year | 2013 | Publication | 2nd International Workshop on Historical Document Imaging and Processing | Abbreviated Journal | |
Volume | Issue | Pages | 36-43 | ||
Keywords | |||||
Abstract | Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information. | ||||
Address | washington; USA; August 2013 | ||||
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 | 978-1-4503-2115-0 | Medium | ||
Area | Expedition | Conference | HIP | ||
Notes | DAG; 600.056; 600.045; 600.061; 602.006 | Approved | no | ||
Call Number | Admin @ si @ FML2013 | Serial | 2308 | ||
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Author | Mehdi Mirza-Mohammadi; Sergio Escalera; Petia Radeva | ||||
Title | Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization | Type | Conference Article | ||
Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
Volume | 5702 | Issue | Pages | 748–756 | |
Keywords | |||||
Abstract | Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | 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 | 978-3-642-03766-5 | Medium | |
Area | Expedition | Conference | CAIP | ||
Notes | HuPBA; MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ MEP2009 | Serial | 1185 | ||
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Author | Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer | ||||
Title | Continual Evidential Deep Learning for Out-of-Distribution Detection | Type | Conference Article | ||
Year | 2023 | Publication | IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3444-3454 | ||
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Abstract | Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. | ||||
Address | Paris; France; October 2023 | ||||
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 | ICCVW | ||
Notes | LAMP; MILAB | Approved | no | ||
Call Number | Admin @ si @ ARR2023 | Serial | 3841 | ||
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Author | Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer | ||||
Title | Continual Evidential Deep Learning for Out-of-Distribution Detection | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3444-3454 | ||
Keywords | |||||
Abstract | Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. | ||||
Address | Paris; France; October 2023 | ||||
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 | ICCVW | ||
Notes | LAMP; MILAB | Approved | no | ||
Call Number | Admin @ si @ ARR2023 | Serial | 3974 | ||
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Author | Kai Wang | ||||
Title | Continual learning for hierarchical classification, few-shot recognition, and multi-modal learning | Type | Book Whole | ||
Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Deep learning has drastically changed computer vision in the past decades and achieved great success in many applications, such as image classification, retrieval, detection, and segmentation thanks to the emergence of neural networks. Typically, for most applications, these networks are presented with examples from all tasks they are expected to perform. However, for many applications, this is not a realistic
scenario, and an algorithm is required to learn tasks sequentially. Continual learning proposes theory and methods for this scenario. The main challenge for continual learning systems is called catastrophic forgetting and refers to a significant drop in performance on previous tasks. To tackle this problem, three main branches of methods have been explored to alleviate the forgetting in continual learning. They are regularization-based methods, rehearsalbased methods, and parameter isolation-based methods. However, most of them are focused on image classification tasks. Continual learning of many computer vision fields has still not been well-explored. Thus, in this thesis, we extend the continual learning knowledge to meta learning, we propose a method for the incremental learning of hierarchical relations for image classification, we explore image recognition in online continual learning, and study continual learning for cross-modal learning. In this thesis, we explore the usage of image rehearsal when addressing the incremental meta learning problem. Observing that existingmethods fail to improve performance with saved exemplars, we propose to mix exemplars with current task data and episode-level distillation to overcome forgetting in incremental meta learning. Next, we study a more realistic image classification scenario where each class has multiple granularity levels. Only one label is present at any time, which requires the model to infer if the provided label has a hierarchical relation with any already known label. In experiments, we show that the estimated hierarchy information can be beneficial in both the training and inference stage. For the online continual learning setting, we investigate the usage of intermediate feature replay. In this case, the training samples are only observed by the model only one time. Here we fix thememory buffer for feature replay and compare the effectiveness of saving features from different layers. Finally, we investigate multi-modal continual learning, where an image encoder is cooperating with a semantic branch. We consider the continual learning of both zero-shot learning and cross-modal retrieval problems. |
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Address | July, 2022 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Luis Herranz;Joost Van de Weijer | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-124793-2-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ Wan2022 | Serial | 3714 | ||
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Author | Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Continual learning in cross-modal retrieval | Type | Conference Article | ||
Year | 2021 | Publication | 2nd CLVISION workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3628-3638 | ||
Keywords | |||||
Abstract | Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. | ||||
Address | Virtual; June 2021 | ||||
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 | CVPRW | ||
Notes | LAMP; 600.120; 600.141; 600.147; 601.379 | Approved | no | ||
Call Number | Admin @ si @ WHW2021 | Serial | 3566 | ||
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Author | Alex Gomez-Villa; Bartlomiej Twardowski; Lu Yu; Andrew Bagdanov; Joost Van de Weijer | ||||
Title | Continually Learning Self-Supervised Representations With Projected Functional Regularization | Type | Conference Article | ||
Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 3866-3876 | ||
Keywords | Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition | ||||
Abstract | Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in different scenarios and on multiple datasets. |
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Address | New Orleans, USA; 20 June 2022 | ||||
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 | CVPRW | ||
Notes | LAMP: 600.147; 600.120 | Approved | no | ||
Call Number | Admin @ si @ GTY2022 | Serial | 3704 | ||
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Author | Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre | ||||
Title | Continuous Generalized Procrustes Analysis | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 2 | Pages | 659–671 |
Keywords | Procrustes analysis; 2D shape model; Continuous approach | ||||
Abstract | PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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Notes | OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil | ||||
Title | Continuous head pose estimation using manifold subspace embedding and multivariate regression | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 6 | Issue | Pages | 18325 - 18334 | |
Keywords | Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression | ||||
Abstract | In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 2169-3536 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ DMH2018b | Serial | 3091 | ||
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Author | Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera | ||||
Title | Continuous Supervised Descent Method for Facial Landmark Localisation | Type | Conference Article | ||
Year | 2016 | Publication | 13th Asian Conference on Computer Vision | Abbreviated Journal | |
Volume | 10112 | Issue | Pages | 121-135 | |
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Abstract | Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size. | ||||
Address | Taipei; Taiwan; November 2016 | ||||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ACCV | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ OCJ2016 | Serial | 2838 | ||
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Author | X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia | ||||
Title | Contour detection and color influence in defocused environtments. | Type | Miscellaneous | ||
Year | 1998 | Publication | Abbreviated Journal | ||
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Notes | MILAB | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ BRC1998 | Serial | 28 | ||
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Author | Jaume Garcia; David Rotger; Francesc Carreras; R.Leta; Petia Radeva | ||||
Title | Contrast echography segmentation and tracking by trained deformable models | Type | Conference Article | ||
Year | 2003 | Publication | Proc. Computers in Cardiology | Abbreviated Journal | |
Volume | 30 | Issue | Pages | 173-176 | |
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Abstract | The objective of this work is to segment the human left ventricle myocardium (LVM) in contrast echocardiography imaging and thus track it along a cardiac cycle in order to extract quantitative data about heart function. Ultrasound images are hard to work with due to their speckle appearance. To overcome this we report the combination of active contour models (ACM) or snakes and active shape models (ASM). The ability of ACM in giving closed and smooth curves in addition to the power of the ASM in producing shapes similar to the ones learned, evoke to a robust algorithm. Meanwhile the snake is attracted towards image main features, ASM acts as a correction factor. The algorithm was tested independently on 180 frames and satisfying results were obtained: in 95% the maximum difference between automatic and experts segmentation was less than 12 pixels. | ||||
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Publisher | Place of Publication | Centre de Visió per Computador – Dept. Informàtica, UAB Edifici O – Campus UAB, 08193 Bellater | Editor | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 0276-6547 | ISBN | 0-7803-8170-X | Medium | |
Area | Expedition | Conference | |||
Notes | IAM;MILAB | Approved | no | ||
Call Number | IAM @ iam @ GRC2003 | Serial | 1512 | ||
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Author | Ajian Liu; Chenxu Zhao; Zitong Yu; Jun Wan; Anyang Su; Xing Liu; Zichang Tan; Sergio Escalera; Junliang Xing; Yanyan Liang; Guodong Guo; Zhen Lei; Stan Z. Li; Shenshen Du | ||||
Title | Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Information Forensics and Security | Abbreviated Journal | TIForensicSEC |
Volume | 17 | Issue | Pages | 2497 - 2507 | |
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Abstract | Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a large-scale Hi gh- Fi delity Mask dataset, namely HiFiMask . Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel C ontrastive C ontext-aware L earning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon. | ||||
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Publisher | IEEE | Place of Publication | Editor | ||
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Area | Expedition | Conference | |||
Notes | HuPBA | Approved | no | ||
Call Number | Admin @ si @ LZY2022 | Serial | 3778 | ||
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