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Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
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Title |
Context-aware features and robust image representations |
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2014 |
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Journal of Visual Communication and Image Representation |
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JVCIR |
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25 |
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2 |
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339-348 |
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Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. |
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LAMP; 600.079;MILAB |
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Admin @ si @ MCG2014 |
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2467 |
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Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta |
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Title |
Diaphragm border detection in coronary X-ray angiographies: New method and applications |
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Journal Article |
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2014 |
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Computerized Medical Imaging and Graphics |
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CMIG |
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38 |
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4 |
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296-305 |
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X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ PCR2014 |
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2468 |
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Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |
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Title |
Semantic Pyramids for Gender and Action Recognition |
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2014 |
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IEEE Transactions on Image Processing |
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TIP |
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23 |
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8 |
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3633-3645 |
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Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
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1057-7149 |
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CIC; LAMP; 601.160; 600.074; 600.079;MILAB |
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Admin @ si @ KWR2014 |
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2507 |
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Author |
Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera |
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Title |
Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial |
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2011 |
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ReVisión |
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4 |
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1 |
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La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación. |
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1989-1199 |
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HuPBA; MILAB;MV |
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Admin @ si @ PGB2011d |
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2514 |
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Author |
Frederic Sampedro; Sergio Escalera |
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Title |
Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation |
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2015 |
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IET Computer Vision |
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IETCV |
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9 |
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3 |
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439 - 446 |
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In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches. |
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1751-9632 |
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HuPBA;MILAB |
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Admin @ si @ SaE2015 |
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2551 |
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