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Juan Ramon Terven Salinas, Bogdan Raducanu, Maria Elena Meza-de-Luna, & Joaquin Salas. (2015). Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses. In 16th IEEE International Conference on Computer Vision Workshops (pp. 452–460).
Abstract: Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis.
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M. Campos-Taberner, Adriana Romero, Carlo Gatta, & Gustavo Camps-Valls. (2015). Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination. In IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 (pp. 4169–4172).
Abstract: This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization.
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R. Bertrand, Oriol Ramos Terrades, P. Gomez-Kramer, P. Franco, & Jean-Marc Ogier. (2015). A Conditional Random Field model for font forgery detection. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 576–580).
Abstract: Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters.
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Lluis Pere de las Heras, Oriol Ramos Terrades, Josep Llados, David Fernandez, & Cristina Cañero. (2015). Use case visual Bag-of-Words techniques for camera based identity document classification. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 721–725).
Abstract: Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes.
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Lluis Pere de las Heras, Oriol Ramos Terrades, & Josep Llados. (2015). Attributed Graph Grammar for floor plan analysis. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 726–730).
Abstract: In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations.
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Aura Hernandez-Sabate, Meritxell Joanpere, Nuria Gorgorio, & Lluis Albarracin. (2015). Mathematics learning opportunities when playing a Tower Defense Game. IJSG - International Journal of Serious Games, 57–71.
Abstract: A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
Keywords: Tower Defense game; learning opportunities; mathematics; problem solving; game design
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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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Debora Gil, F. Javier Sanchez, Gloria Fernandez Esparrach, & Jorge Bernal. (2015). 3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos. In Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 (Vol. 9515, pp. 140–152). LNCS.
Abstract: Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response.
Spatio-temporal stability is achieved by extracting 3D watershed regions on the
temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection.
Keywords: Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds
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Hanne Kause, Aura Hernandez-Sabate, Patricia Marquez, Andrea Fuster, Luc Florack, Hans van Assen, et al. (2015). Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging. In Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 (Vol. 9534, pp. 69–79). LNCS. Springer International Publishing.
Abstract: Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm.
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Aleksandr Setkov, Fabio Martinez Carillo, Michele Gouiffes, Christian Jacquemin, Maria Vanrell, & Ramon Baldrich. (2015). DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition. In Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II (Vol. 9475, pp. 463–473). LNCS. Springer International Publishing.
Abstract: Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection.
Keywords: Projector-camera systems; Feature descriptors; Object recognition
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David Sanchez-Mendoza, David Masip, & Agata Lapedriza. (2015). Emotion recognition from mid-level features. PRL - Pattern Recognition Letters, 67(Part 1), 66–74.
Abstract: In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception.
Keywords: Facial expression; Emotion recognition; Action units; Computer vision
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Joan M. Nuñez, Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2015). Growing Algorithm for Intersection Detection (GRAID) in branching patterns. MVAP - Machine Vision and Applications, 26(2), 387–400.
Abstract: Analysis of branching structures represents a very important task in fields such as medical diagnosis, road detection or biometrics. Detecting intersection landmarks Becomes crucial when capturing the structure of a branching pattern. We present a very simple geometrical model to describe intersections in branching structures based on two conditions: Bounded Tangency condition (BT) and Shortest Branch (SB) condition. The proposed model precisely sets a geometrical characterization of intersections and allows us to introduce a new unsupervised operator for intersection extraction. We propose an implementation that handles the consequences of digital domain operation that,unlike existing approaches, is not restricted to a particular scale and does not require the computation of the thinned pattern. The new proposal, as well as other existing approaches in the bibliography, are evaluated in a common framework for the first time. The performance analysis is based on two manually segmented image data sets: DRIVE retinal image database and COLON-VESSEL data set, a newly created data set of vascular content in colonoscopy frames. We have created an intersection landmark ground truth for each data set besides comparing our method in the only existing ground truth. Quantitative results confirm that we are able to outperform state-of-the-art performancelevels with the advantage that neither training nor parameter tuning is needed.
Keywords: Bifurcation ; Crossroad; Intersection ;Retina ; Vessel
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Jordina Torrents-Barrena, Aida Valls, Petia Radeva, Meritxell Arenas, & Domenec Puig. (2015). Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis. In Artificial Intelligence Research and Development (Vol. 277, pp. 247–256). Frontiers in Artificial Intelligence and Applications. IOS Press.
Abstract: Breast cancer disease has recently been classified into four subtypes regarding the molecular properties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decide the most appropriate therapy in order to enhance life prospects. Nowadays, advanced therapeutic diagnosis research is focused on gene selection methods, which are not robust enough. Hence, we hypothesize that computer vision algorithms can offer benefits to address the problem of discriminating among them through X-Ray images. In this paper, we propose a novel approach driven by texture feature descriptors and machine learning techniques. First, we segment the tumour part through an active contour technique and then, we perform a complete fractal analysis to collect qualitative information of the region of interest in the feature extraction stage. Finally, several supervised and unsupervised classifiers are used to perform multiclass classification of the aforementioned data. The experimental results presented in this paper support that it is possible to establish a relation between each tumor subtype and the extracted features of the patterns revealed on mammograms.
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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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