Dennis G.Romero, Anselmo Frizera, Angel Sappa, Boris X. Vintimilla, & Teodiano F.Bastos. (2015). A predictive model for human activity recognition by observing actions and context. In Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 (Vol. 9386, pp. 323–333). LNCS. Springer International Publishing.
Abstract: This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
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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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Alvaro Cepero, Albert Clapes, & Sergio Escalera. (2015). Automatic non-verbal communication skills analysis: a quantitative evaluation. AIC - AI Communications, 28(1), 87–101.
Abstract: The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions.
Keywords: Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning
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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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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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Wenjuan Gong, Y.Huang, Jordi Gonzalez, & Liang Wang. (2015). An Effective Solution to Double Counting Problem in Human Pose Estimation.
Abstract: The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.
An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015].
Keywords: Pose estimation; double counting problem; mix-ture of parts Model
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2015). Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos.
Abstract: Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness.
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Xavier Otazu, Olivier Penacchio, & Xim Cerda-Company. (2015). An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort. In Barcelona Computational, Cognitive and Systems Neuroscience.
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C. Alejandro Parraga. (2015). Perceptual Psychophysics. In G.Cristobal, M.Keil, & L.Perrinet (Eds.), Biologically-Inspired Computer Vision: Fundamentals and Applications.
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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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Joost Van de Weijer, & Fahad Shahbaz Khan. (2015). An Overview of Color Name Applications in Computer Vision. In Computational Color Imaging Workshop.
Abstract: In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation.
Keywords: color features; color names; object recognition
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Debora Gil, David Roche, Agnes Borras, & Jesus Giraldo. (2015). Terminating Evolutionary Algorithms at their Steady State. COA - Computational Optimization and Applications, 61(2), 489–515.
Abstract: Assessing the reliability of termination conditions for evolutionary algorithms (EAs) is of prime importance. An erroneous or weak stop criterion can negatively affect both the computational effort and the final result. We introduce a statistical framework for assessing whether a termination condition is able to stop an EA at its steady state, so that its results can not be improved anymore. We use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in decision variable space. Our framework is analyzed across 24 benchmark test functions and two standard termination criteria based on function fitness value in objective function space and EA population decision variable space distribution for the differential evolution (DE) paradigm. Results validate our framework as a powerful tool for determining the capability of a measure for terminating EA and the results also identify the decision variable space distribution as the best-suited for accurately terminating DE in real-world applications.
Keywords: Evolutionary algorithms; Termination condition; Steady state; Differential evolution
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Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey, Sayan Chakraborty, Luca Saba, Dinesh Kumar, et al. (2015). A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. CMPB - Computer Methods and Programs in Biomedicine, 118(2), 158–172.
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Josep M. Gonfaus, Marco Pedersoli, Jordi Gonzalez, Andrea Vedaldi, & Xavier Roca. (2015). Factorized appearances for object detection. CVIU - Computer Vision and Image Understanding, 138, 92–101.
Abstract: Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
Keywords: Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
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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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