2014 |
Epameinondas Antonakos, Vassilis Pitsikalis, Petros Maragos Classification of extreme facial events in sign language videos Journal Article Eurasip Journal on Image and Video Processing, 2014 , 2014, ISSN: 16875281. @article{143, title = {Classification of extreme facial events in sign language videos}, author = {Epameinondas Antonakos and Vassilis Pitsikalis and Petros Maragos}, doi = {10.1186/1687-5281-2014-14}, issn = {16875281}, year = {2014}, date = {2014-01-01}, journal = {Eurasip Journal on Image and Video Processing}, volume = {2014}, abstract = {We propose a new approach for Extreme States Classification (ESC) on feature spaces of facial cues in sign language (SL) videos. The method is built upon Active Appearance Model (AAM) face tracking and feature extraction of global and local AAMs. ESC is applied on various facial cues-as, for instance, pose rotations, head movements and eye blinking-leading to the detection of extreme states such as left/right, up/down and open/closed. Given the importance of such facial events in SL analysis, we apply ESC to detect visual events on SL videos, including both American (ASL) and Greek (GSL) corpora, yielding promising qualitative and quantitative results. Further, we show the potential of ESC for assistive annotation tools and demonstrate a link of the detections with indicative higher-level linguistic events. Given the lack of facial annotated data and the fact that manual annotations are highly time-consuming, ESC results indicate that the framework can have significant impact on SL processing and analysis. textcopyright 2014 Antonakos et al.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We propose a new approach for Extreme States Classification (ESC) on feature spaces of facial cues in sign language (SL) videos. The method is built upon Active Appearance Model (AAM) face tracking and feature extraction of global and local AAMs. ESC is applied on various facial cues-as, for instance, pose rotations, head movements and eye blinking-leading to the detection of extreme states such as left/right, up/down and open/closed. Given the importance of such facial events in SL analysis, we apply ESC to detect visual events on SL videos, including both American (ASL) and Greek (GSL) corpora, yielding promising qualitative and quantitative results. Further, we show the potential of ESC for assistive annotation tools and demonstrate a link of the detections with indicative higher-level linguistic events. Given the lack of facial annotated data and the fact that manual annotations are highly time-consuming, ESC results indicate that the framework can have significant impact on SL processing and analysis. textcopyright 2014 Antonakos et al. |
2012 |
Epameinondas Antonakos, Vassilis Pitsikalis, Isidoros Rodomagoulakis, Petros Maragos Unsupervised classification of extreme facial events using active appearance models tracking for sign language videos Conference Proceedings - International Conference on Image Processing, ICIP, 2012, ISSN: 15224880. Abstract | BibTeX | Links: [PDF] @conference{178, title = {Unsupervised classification of extreme facial events using active appearance models tracking for sign language videos}, author = { Epameinondas Antonakos and Vassilis Pitsikalis and Isidoros Rodomagoulakis and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/APRM_UnsupervisClassifExtremeFacialEventsAAM-SignLangVideos_ICIP2012.pdf}, doi = {10.1109/ICIP.2012.6467133}, issn = {15224880}, year = {2012}, date = {2012-01-01}, booktitle = {Proceedings - International Conference on Image Processing, ICIP}, pages = {1409--1412}, abstract = {We propose an Unsupervised method for Extreme States Classification (UnESC) on feature spaces of facial cues of interest. The method is built upon Active Appearance Models (AAM) face tracking and on feature extraction of Global and Local AAMs. UnESC is applied primarily on facial pose, but is shown to be extendable for the case of local models on the eyes and mouth. Given the importance of facial events in Sign Languages we apply the UnESC on videos from two sign language corpora, both American (ASL) and Greek (GSL) yielding promising qualitative and quantitative results. Apart from the detection of extreme facial states, the proposed Un-ESC also has impact for SL corpora lacking any facial annotations.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We propose an Unsupervised method for Extreme States Classification (UnESC) on feature spaces of facial cues of interest. The method is built upon Active Appearance Models (AAM) face tracking and on feature extraction of Global and Local AAMs. UnESC is applied primarily on facial pose, but is shown to be extendable for the case of local models on the eyes and mouth. Given the importance of facial events in Sign Languages we apply the UnESC on videos from two sign language corpora, both American (ASL) and Greek (GSL) yielding promising qualitative and quantitative results. Apart from the detection of extreme facial states, the proposed Un-ESC also has impact for SL corpora lacking any facial annotations. |
2009 |
Iasonas Kokkinos, Petros Maragos Synergy between object recognition and image segmentation using the expectation-maximization algorithm Journal Article IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (8), pp. 1486–1501, 2009, ISSN: 01628828. Abstract | BibTeX | Links: [PDF] @article{135, title = {Synergy between object recognition and image segmentation using the expectation-maximization algorithm}, author = {Iasonas Kokkinos and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/KokkinosMaragos_SynergyBetweenObjectRecognitionAndImageSegmentation_ieeetPAMI09.pdf}, doi = {10.1109/TPAMI.2008.158}, issn = {01628828}, year = {2009}, date = {2009-01-01}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, number = {8}, pages = {1486--1501}, abstract = {In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations. These two tasks are performed iteratively, thereby simultaneously segmenting an image and reconstructing it in terms of objects. We model objects using Active Appearance Models (AAMs) as they capture both shape and appearance variation. During the E-step, the fidelity of the AAM predictions to the image is used to decide about assigning observations to the object. For this, we propose two top-down segmentation algorithms. The first starts with an oversegmentation of the image and then softly assigns image segments to objects, as in the common setting of EM. The second uses curve evolution to minimize a criterion derived from the variational interpretation of EM and introduces AAMs as shape priors. For the M-step, we derive AAM fitting equations that accommodate segmentation information, thereby allowing for the automated treatment of occlusions. Apart from top-down segmentation results, we provide systematic experiments on object detection that validate the merits of our joint segmentation and recognition approach.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations. These two tasks are performed iteratively, thereby simultaneously segmenting an image and reconstructing it in terms of objects. We model objects using Active Appearance Models (AAMs) as they capture both shape and appearance variation. During the E-step, the fidelity of the AAM predictions to the image is used to decide about assigning observations to the object. For this, we propose two top-down segmentation algorithms. The first starts with an oversegmentation of the image and then softly assigns image segments to objects, as in the common setting of EM. The second uses curve evolution to minimize a criterion derived from the variational interpretation of EM and introduces AAMs as shape priors. For the M-step, we derive AAM fitting equations that accommodate segmentation information, thereby allowing for the automated treatment of occlusions. Apart from top-down segmentation results, we provide systematic experiments on object detection that validate the merits of our joint segmentation and recognition approach. |
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