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E. Tavalera, Mariella Dimiccoli, Marc Bolaños, Maedeh Aghaei, & Petia Radeva. (2015). Regularized Clustering for Egocentric Video Segmentation. In Pattern Recognition and Image Analysis (pp. 327–336). LNCS. Springer International Publishing.
Abstract: In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energyminimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.
Keywords: Temporal video segmentation ; Egocentric videos ; Clustering
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Mariella Dimiccoli, & Petia Radeva. (2015). Lifelogging in the era of outstanding digitization. In International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage.
Abstract: In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior.
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Aniol Lidon, Xavier Giro, Marc Bolaños, Petia Radeva, Markus Seidl, & Matthias Zeppelzauer. (2015). UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images. In 2015 MediaEval Retrieving Diverse Images Task.
Abstract: This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.
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Adriana Romero, Petia Radeva, & Carlo Gatta. (2015). Meta-parameter free unsupervised sparse feature learning. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1716–1722.
Abstract: We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.
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Santiago Segui, Oriol Pujol, & Jordi Vitria. (2015). Learning to count with deep object features. In Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop (pp. 90–96).
Abstract: Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation.
To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training.
We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.
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Carolina Malagelada, Michal Drozdzal, Santiago Segui, Sara Mendez, Jordi Vitria, Petia Radeva, et al. (2015). Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis. AJPGI - American Journal of Physiology-Gastrointestinal and Liver Physiology, 309(6), G413–G419.
Abstract: We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function.
Keywords: capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning
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Michal Drozdzal, Santiago Segui, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, & Jordi Vitria. (2015). Motility bar: a new tool for motility analysis of endoluminal videos. CBM - Computers in Biology and Medicine, 65, 320–330.
Abstract: Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information.
Keywords: Small intestine; Motility; WCE; Computer vision; Image classification
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Jorge Bernal, F. Javier Sanchez, Cristina Rodriguez de Miguel, & Gloria Fernandez Esparrach. (2015). Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists. In Colonoscopy and Colorectal Cancer.
Abstract: Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly.
Keywords: Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description
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Joan M. Nuñez. (2015). Vascular Pattern Characterization in Colonoscopy Images (Fernando Vilariño, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Colorectal cancer is the third most common cancer worldwide and the second most common malignant tumor in Europe. Screening tests have shown to be very e ective in increasing the survival rates since they allow an early detection of polyps. Among the di erent screening techniques, colonoscopy is considered the gold standard although clinical studies mention several problems that have an impact in the quality of the procedure. The navigation through the rectum and colon track can be challenging for the physicians which can increase polyp miss rates. The thorough visualization of the colon track must be ensured so that
the chances of missing lesions are minimized. The visual analysis of colonoscopy images can provide important information to the physicians and support their navigation during the procedure.
Blood vessels and their branching patterns can provide descriptive power to potentially develop biometric markers. Anatomical markers based on blood vessel patterns could be used to identify a particular scene in colonoscopy videos and to support endoscope navigation by generating a sequence of ordered scenes through the di erent colon sections. By verifying the presence of vascular content in the endoluminal scene it is also possible to certify a proper
inspection of the colon mucosa and to improve polyp localization. Considering the potential uses of blood vessel description, this contribution studies the characterization of the vascular content and the analysis of the descriptive power of its branching patterns.
Blood vessel characterization in colonoscopy images is shown to be a challenging task. The endoluminal scene is conformed by several elements whose similar characteristics hinder the development of particular models for each of them. To overcome such diculties we propose the use of the blood vessel branching characteristics as key features for pattern description. We present a model to characterize junctions in binary patterns. The implementation
of the junction model allows us to develop a junction localization method. We
created two data sets including manually labeled vessel information as well as manual ground truths of two types of keypoint landmarks: junctions and endpoints. The proposed method outperforms the available algorithms in the literature in experiments in both, our newly created colon vessel data set, and in DRIVE retinal fundus image data set. In the latter case, we created a manual ground truth of junction coordinates. Since we want to explore the descriptive potential of junctions and vessels, we propose a graph-based approach to
create anatomical markers. In the context of polyp localization, we present a new method to inhibit the in uence of blood vessels in the extraction valley-pro le information. The results show that our methodology decreases vessel in
uence, increases polyp information and leads to an improvement in state-of-the-art polyp localization performance. We also propose a polyp-speci c segmentation method that outperforms other general and speci c approaches.
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Fernando Vilariño, & Dimosthenis Karatzas. (2015). The Library Living Lab. In Open Living Lab Days.
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Fernando Vilariño, Dimosthenis Karatzas, Marcos Catalan, & Alberto Valcarcel. (2015). An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres. In The White Book on Public Library Network from Diputació de Barcelona.
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Jorge Bernal, F. Javier Sanchez, Gloria Fernandez Esparrach, Debora Gil, Cristina Rodriguez de Miguel, & Fernando Vilariño. (2015). WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians. CMIG - Computerized Medical Imaging and Graphics, 43, 99–111.
Abstract: We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice.
Keywords: Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection
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Gloria Fernandez Esparrach, Jorge Bernal, Cristina Rodriguez de Miguel, Debora Gil, Fernando Vilariño, Henry Cordova, et al. (2015). Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps. In Proceedings of 23 United European- UEG Week 2015.
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Onur Ferhat, Arcadi Llanza, & Fernando Vilariño. (2015). A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios. In Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 (Vol. 9117, pp. 569–576). LNCS. Springer International Publishing.
Abstract: We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system.
Keywords: Eye tracking; Gaze estimation; Natural light; Webcam
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Onur Ferhat, Arcadi Llanza, & Fernando Vilariño. (2015). Gaze interaction for multi-display systems using natural light eye-tracker. In 2nd International Workshop on Solutions for Automatic Gaze Data Analysis.
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