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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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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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J.Kuhn, A.Nussbaumer, J.Pirker, Dimosthenis Karatzas, A. Pagani, O.Conlan, et al. (2015). Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space. In Workshop Proceedings on the 11th International Conference on Intelligent Environments (Vol. 19, pp. 373–380).
Abstract: Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.
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Monica Piñol, Angel Sappa, & Ricardo Toledo. (2015). Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy. NEUCOM - Neurocomputing, 150(A), 106–115.
Abstract: This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
Keywords: Reinforcement learning; Q-learning; Bag of features; Descriptors
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Mikhail Mozerov, & Joost Van de Weijer. (2015). Accurate stereo matching by two step global optimization. TIP - IEEE Transactions on Image Processing, 24(3), 1153–1163.
Abstract: In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results.
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Andres Traumann, Gholamreza Anbarjafari, & Sergio Escalera. (2015). Accurate 3D Measurement Using Optical Depth Information. EL - Electronic Letters, 51(18), 1420–1422.
Abstract: A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II.
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G.Thorvaldsen, Joana Maria Pujadas-Mora, T.Andersen, L.Eikvil, Josep Llados, Alicia Fornes, et al. (2015). A Tale of two Transcriptions. Historical Life Course Studies, 1–19.
Abstract: non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.
Keywords: Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting
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David Aldavert, Marçal Rusiñol, Ricardo Toledo, & Josep Llados. (2015). A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting. IJDAR - International Journal on Document Analysis and Recognition, 18(3), 223–234.
Abstract: The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.
Keywords: Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation
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David Roche. (2015). A Statistical Framework for Terminating Evolutionary Algorithms at their Steady State (Debora Gil, & Jesus Giraldo, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: As any iterative technique, it is a necessary condition a stop criterion for terminating Evolutionary Algorithms (EA). In the case of optimization methods, the algorithm should stop at the time it has reached a steady state so it can not improve results anymore. Assessing the reliability of termination conditions for EAs is of prime importance. A wrong or weak stop criterion can negatively a ect both the computational e ort and the nal result.
In this Thesis, we introduce a statistical framework for assessing whether a termination condition is able to stop EA at its steady state. In one hand a numeric approximation to steady states to detect the point in which EA population has lost its diversity has been presented for EA termination. This approximation has been applied to di erent EA paradigms based on diversity and a selection of functions covering the properties most relevant for EA convergence. Experiments show that our condition works regardless of the search space dimension and function landscape and Di erential Evolution (DE) arises as the best paradigm. On the other hand, 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 xspace.
Our theoretical framework is analyzed across several benchmark test functions
and two standard termination criteria based on function improvement in f-space and EA population x-space distribution for the DE paradigm. Results validate our statistical framework as a powerful tool for determining the capability of a measure for terminating EA and select the x-space distribution as the best-suited for accurately stopping DE in real-world applications.
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Suman Ghosh, & Ernest Valveny. (2015). A Sliding Window Framework for Word Spotting Based on Word Attributes. In Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 (Vol. 9117, pp. 652–661). LNCS. Springer International Publishing.
Abstract: In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.
Keywords: Word spotting; Sliding window; Word attributes
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J. Chazalon, Marçal Rusiñol, Jean-Marc Ogier, & Josep Llados. (2015). A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 621–625).
Abstract: This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015
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Miguel Oliveira, Angel Sappa, & Victor Santos. (2015). A probabilistic approach for color correction in image mosaicking applications. TIP - IEEE Transactions on Image Processing, 14(2), 508–523.
Abstract: Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
Keywords: Color correction; image mosaicking; color transfer; color palette mapping functions
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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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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2015). A Novel Cochlear Reference Frame Based On The Laplace Equation. In 29th international Congress and Exhibition on Computer Assisted Radiology and Surgery (Vol. 10, pp. 1–312).
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Andres Traumann, Sergio Escalera, & Gholamreza Anbarjafari. (2015). A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 75–79).
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