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R. Clariso, David Masip, & A. Rius. (2014). Student projects empowering mobile learning in higher education. RUSC - Revista de Universidad y Sociedad del Conocimiento, 192–207.
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Jorge Bernal, Aymeric Histace, Marc Masana, Quentin Angermann, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, et al. (2019). GTCreator: a flexible annotation tool for image-based datasets. IJCAR - International Journal of Computer Assisted Radiology and Surgery, 14(2), 191–201.
Abstract: Abstract Purpose: Methodology evaluation for decision support systems for health is a time consuming-task. To assess performance of polyp detection
methods in colonoscopy videos, clinicians have to deal with the annotation
of thousands of images. Current existing tools could be improved in terms of
exibility and ease of use. Methods:We introduce GTCreator, a exible annotation tool for providing image and text annotations to image-based datasets.
It keeps the main basic functionalities of other similar tools while extending
other capabilities such as allowing multiple annotators to work simultaneously
on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. Results: The
comparison with other similar tools shows that GTCreator allows to obtain
fast and precise annotation of image datasets, being the only one which offers
full annotation editing and browsing capabilites. Conclusions: Our proposed
annotation tool has been proven to be efficient for large image dataset annota-
tion, as well as showing potential of use in other stages of method evaluation
such as experimental setup or results analysis.
Keywords: Annotation tool; Validation Framework; Benchmark; Colonoscopy; Evaluation
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Santiago Segui, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2016). Generic Feature Learning for Wireless Capsule Endoscopy Analysis. CBM - Computers in Biology and Medicine, 79, 163–172.
Abstract: The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).
Keywords: Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis
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Xavier Baro, Sergio Escalera, Jordi Vitria, Oriol Pujol, & Petia Radeva. (2009). Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification. TITS - IEEE Transactions on Intelligent Transportation Systems, 10(1), 113–126.
Abstract: The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.
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Antonio Hernandez, Miguel Angel Bautista, Xavier Perez Sala, Victor Ponce, Sergio Escalera, Xavier Baro, et al. (2014). Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D. PRL - Pattern Recognition Letters, 50(1), 112–121.
Abstract: PATREC5825
We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach.
Keywords: RGB-D; Bag-of-Words; Dynamic Time Warping; Human Gesture Recognition
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