Florin Popescu, Stephane Ayache, Sergio Escalera, Xavier Baro, Cecile Capponi, Patrick Panciatici, et al. (2016). From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning. In European Geosciences Union General Assembly (Vol. 18).
Abstract: The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image.
We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks.
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2016). Support Vector Machines with Time Series Distance Kernels for Action Classification. In IEEE Winter Conference on Applications of Computer Vision (pp. 1–7).
Abstract: Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.
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Gloria Fernandez Esparrach, Jorge Bernal, Maria Lopez Ceron, Henry Cordova, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, et al. (2016). Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. END - Endoscopy, 48(9), 837–842.
Abstract: Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
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Gloria Fernandez Esparrach, Jorge Bernal, Cristina Rodriguez de Miguel, Debora Gil, Fernando Vilariño, Henry Cordova, et al. (2016). Utilidad de la visión por computador para la localización de pólipos pequeños y planos. In XIX Reunión Nacional de la Asociación Española de Gastroenterología, Gastroenterology Hepatology (Vol. 39, 94).
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Francesco Ciompi, Simone Balocco, Juan Rigla, Xavier Carrillo, J. Mauri, & Petia Radeva. (2016). Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences. MP - Medical Physics, 43(10).
Abstract: Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identi cation of struts location and the de nition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape.
Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classi cation. The output of the classi cation 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.
Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts.
Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions.
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Maria Oliver, Gloria Haro, Mariella Dimiccoli, Baptiste Mazin, & Coloma Ballester. (2016). A computational model of amodal completion. In SIAM Conference on Imaging Science.
Abstract: This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
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G. de Oliveira, A. Cartas, Marc Bolaños, Mariella Dimiccoli, Xavier Giro, & Petia Radeva. (2016). LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task. In 12th NTCIR Conference on Evaluation of Information Access Technologies.
Abstract: Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
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G. de Oliveira, Mariella Dimiccoli, & Petia Radeva. (2016). Egocentric Image Retrieval With Deep Convolutional Neural Networks. In 19th International Conference of the Catalan Association for Artificial Intelligence (pp. 71–76).
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2016). With whom do I interact with? Social interaction detection in egocentric photo-streams. In 23rd International Conference on Pattern Recognition.
Abstract: Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
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Fernando Vilariño, Dan Norton, & Onur Ferhat. (2016). The Eye Doesn't Click – Eyetracking and Digital Content Interaction. In 4S/EASST Conference.
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Fernando Vilariño. (2016). Giving Value to digital collections in the Public Library. In Librarian 2020.
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Fernando Vilariño, & Dimosthenis Karatzas. (2016). A Living Lab approach for Citizen Science in Libraries. In 1st International ECSA Conference.
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Fernando Vilariño. (2016). Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation. In International Workshop on Poetry: Archives, Poetries and Receptions.
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Cristhian A. Aguilera-Carrasco, F. Aguilera, Angel Sappa, C. Aguilera, & Ricardo Toledo. (2016). Learning cross-spectral similarity measures with deep convolutional neural networks. In 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops.
Abstract: The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.
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Jose A. Garcia, David Masip, Valerio Sbragaglia, & Jacopo Aguzzi. (2016). Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.). In 3rd International Conference on Maritime Technology and Engineering.
Abstract: Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.
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