Xavier Soria, & Angel Sappa. (2018). Improving Edge Detection in RGB Images by Adding NIR Channel. In 14th IEEE International Conference on Signal Image Technology & Internet Based System.
Abstract: The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum.
Keywords: Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images
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Alejandro Cartas, Juan Marin, Petia Radeva, & Mariella Dimiccoli. (2018). Batch-based activity recognition from egocentric photo-streams revisited. PAA - Pattern Analysis and Applications, 21(4), 953–965.
Abstract: Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.
Keywords: Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks
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Marc Bolaños, Alvaro Peris, Francisco Casacuberta, Sergi Solera, & Petia Radeva. (2018). Egocentric video description based on temporally-linked sequences. JVCIR - Journal of Visual Communication and Image Representation, 50, 205–216.
Abstract: Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.
Keywords: egocentric vision; video description; deep learning; multi-modal learning
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Marc Bolaños, R. Mestre, Estefania Talavera, Xavier Giro, & Petia Radeva. (2015). Visual Summary of Egocentric Photostreams by Representative Keyframes. In IEEE International Conference on Multimedia and Expo ICMEW2015 (pp. 1–6).
Abstract: Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries.
Keywords: egocentric; lifelogging; summarization; keyframes
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T. Mouats, N. Aouf, Angel Sappa, Cristhian A. Aguilera-Carrasco, & Ricardo Toledo. (2015). Multi-Spectral Stereo Odometry. TITS - IEEE Transactions on Intelligent Transportation Systems, 16(3), 1210–1224.
Abstract: In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
Keywords: Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery
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Debora Gil, Jose Maria-Carazo, & Roberto Marabini. (2006). On the nature of 2D crystal unbending. Journal of Structural Biology, 156(3), 546–555.
Abstract: Crystal unbending, the process that aims to recover a perfect crystal from experimental data, is one of the more important steps in electron crystallography image processing. The unbending process involves three steps: estimation of the unit cell displacements from their ideal positions, extension of the deformation field to the whole image and transformation of the image in order to recover an ideal crystal. In this work, we present a systematic analysis of the second step oriented to address two issues. First, whether the unit cells remain undistorted and only the distance between them should be changed (rigid case) or should be modified with the same deformation suffered by the whole crystal (elastic case). Second, the performance of different extension algorithms (interpolation versus approximation) is explored. Our experiments show that there is no difference between elastic and rigid cases or among the extension algorithms. This implies that the deformation fields are constant over large areas. Furthermore, our results indicate that the main source of error is the transformation of the crystal image.
Keywords: Electron microscopy
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Debora Gil, Aura Hernandez-Sabate, Antoni Carol, Oriol Rodriguez, & Petia Radeva. (2005). A Deterministic-Statistic Adventitia Detection in IVUS Images. In ESC Congress. ,Sweden (EU).
Abstract: Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles.
Keywords: Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation
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Debora Gil, Aura Hernandez-Sabate, Antoni Carol, Oriol Rodriguez, & Petia Radeva. (2005). A Deterministic-Statistic Adventitia Detection in IVUS Images. In 3rd International workshop on International Workshop on Functional Imaging and Modeling of the Heart (pp. 65–74).
Abstract: Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles.
Keywords: Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation
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Laura Lopez-Fuentes, Joost Van de Weijer, Manuel Gonzalez-Hidalgo, Harald Skinnemoen, & Andrew Bagdanov. (2018). Review on computer vision techniques in emergency situations. MTAP - Multimedia Tools and Applications, 77(13), 17069–17107.
Abstract: In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.
Keywords: Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation
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Rain Eric Haamer, Eka Rusadze, Iiris Lusi, Tauseef Ahmed, Sergio Escalera, & Gholamreza Anbarjafari. (2018). Review on Emotion Recognition Databases. In Human-Robot Interaction: Theory and Application.
Abstract: Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.
Keywords: emotion; computer vision; databases
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Jorge Bernal, Nima Tajkbaksh, F. Javier Sanchez, Bogdan J. Matuszewski, Hao Chen, Lequan Yu, et al. (2017). Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge. TMI - IEEE Transactions on Medical Imaging, 36(6), 1231–1249.
Abstract: Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack
of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted
Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Keywords: Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework
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Farhan Riaz, Fernando Vilariño, Mario Dinis-Ribeiro, & Miguel Coimbraln. (2011). Identifying Potentially Cancerous Tissues in Chromoendoscopy Images. In and M. Hernandez J. M. S. J. Vitria (Ed.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 709–716). LNCS. Berlin: Springer.
Abstract: The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered.
Keywords: Endoscopy, Computer Assisted Diagnosis, Gradient.
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Miguel Angel Bautista, Xavier Baro, Oriol Pujol, Petia Radeva, Jordi Vitria, & Sergio Escalera. (2010). Compact Evolutive Design of Error-Correcting Output Codes. In Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 119–128).
Abstract: The classication of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classiers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classiers. Evolutionary computation is used for tuning the parameters of the classiers and looking for the best Minimal ECOC code conguration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
Keywords: Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization
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Jose Elias Yauri, M. Lagos, H. Vega-Huerta, P. de-la-Cruz, G.L.E Maquen-Niño, & E. Condor-Tinoco. (2023). Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings. IJACSA - International Journal of Advanced Computer Science and Applications, 14(5), 1067–1074.
Abstract: According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches.
Keywords: Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2013). A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding. PR - Pattern Recognition, 46(10), 2830–2839.
Abstract: Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.
Keywords: Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification
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