Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Error Correcting Output Codes for multiclass classification: Application to two image vision problems. In 16th symposium on Artificial Intelligence & Signal Processing (pp. 508–513). IEEE Xplore.
Abstract: Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches.
|
Rada Deeb, Damien Muselet, Mathieu Hebert, Alain Tremeau, & Joost Van de Weijer. (2017). 3D color charts for camera spectral sensitivity estimation. In 28th British Machine Vision Conference.
Abstract: Estimating spectral data such as camera sensor responses or illuminant spectral power distribution from raw RGB camera outputs is crucial in many computer vision applications.
Usually, 2D color charts with various patches of known spectral reflectance are
used as reference for such purpose. Deducing n-D spectral data (n»3) from 3D RGB inputs is an ill-posed problem that requires a high number of inputs. Unfortunately, most of the natural color surfaces have spectral reflectances that are well described by low-dimensional linear models, i.e. each spectral reflectance can be approximated by a weighted sum of the others. It has been shown that adding patches to color charts does not help in practice, because the information they add is redundant with the information provided by the first set of patches. In this paper, we propose to use spectral data of
higher dimensionality by using 3D color charts that create inter-reflections between the surfaces. These inter-reflections produce multiplications between natural spectral curves and so provide non-linear spectral curves. We show that such data provide enough information for accurate spectral data estimation.
|
Hassan Ahmed Sial, Ramon Baldrich, & Maria Vanrell. (2020). Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects. JOSA A - Journal of the Optical Society of America A, 37(1), 1–15.
Abstract: Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
|
Aura Hernandez-Sabate, Debora Gil, & Albert Teis. (2007). How Do Conservation Laws Define a Motion Suppression Score in In-Vivo Ivus Sequences? In Proc. IEEE Ultrasonics Symp (pp. 2231–2234).
Abstract: Evaluation of arterial tissue biomechanics for diagnosis and treatment of cardiovascular diseases is an active research field in the biomedical imaging processing area. IntraVascular UltraSound (IVUS) is a unique tool for such assessment since it reflects tissue morphology and deformation. A proper quantification and visualization of both properties is hindered by vessel structures misalignments introduced by cardiac dynamics. This has encouraged development of IVUS motion compensation techniques. However, there is a lack of an objective evaluation of motion reduction ensuring a reliable clinical application This work reports a novel score, the Conservation of Density Rate (CDR), for validation of motion compensation in in-vivo pullbacks. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; while results in in vivo pullbacks show its reliability in clinical cases.
Keywords: validation standards; IVUS motion compensation; conservation laws.
|
Katerine Diaz, Aura Hernandez-Sabate, & Antonio Lopez. (2016). A reduced feature set for driver head pose estimation. ASOC - Applied Soft Computing, 45, 98–107.
Abstract: Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.
Keywords: Head pose estimation; driving performance evaluation; subspace based methods; linear regression
|
Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto, & Sergio S. Furuie. (2011). Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano, & Walter Kropatsch (Eds.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6854, pp. 520–528). LNCS. Berlin: Springer-Verlag Berlin Heidelberg.
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
|
Joan Arnedo-Moreno, D. Bañeres, Xavier Baro, S. Caballe, S. Guerrero, L. Porta, et al. (2014). Va-ID: A trust-based virtual assessment system. In 6th International Conference on Intelligent Networking and Collaborative Systems (pp. 328–335).
Abstract: Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model.
|
Ferran Poveda, Debora Gil, & Enric Marti. (2012). Multi-resolution DT-MRI cardiac tractography. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 270–277). LNCS. Springer Berlin Heidelberg.
Abstract: Even using objective measures from DT-MRI no consensus about myocardial architecture has been achieved so far. Streamlining provides good reconstructions at low level of detail, but falls short to give global abstract interpretations. In this paper, we present a multi-resolution methodology that is able to produce simplified representations of cardiac architecture. Our approach produces a reduced set of tracts that are representative of the main geometric features of myocardial anatomical structure. Experiments show that fiber geometry is preserved along reductions, which validates the simplified model for interpretation of cardiac architecture.
|
Alejandro Cartas, Estefania Talavera, Petia Radeva, & Mariella Dimiccoli. (2018). On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams.
Abstract: Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users.
|
Xavier Baro. (2009). Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection (Jordi Vitria, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.
This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.
In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.
After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach.
|
Marc Bolaños, Maite Garolera, & Petia Radeva. (2013). Active labeling application applied to food-related object recognition. In 5th International Workshop on Multimedia for Cooking & Eating Activities (pp. 45–50).
Abstract: Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.
Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015].
|
Arjan Gijsenij, & Theo Gevers. (2011). Color Constancy Using Natural Image Statistics and Scene Semantics. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 687–698.
Abstract: Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.
|
Hannes Mueller, Andre Groger, Jonathan Hersh, Andrea Matranga, & Joan Serrat. (2020). Monitoring War Destruction from Space: A Machine Learning Approach.
Abstract: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively.
|
Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, & Joan Serrat. (2021). Monitoring war destruction from space using machine learning. PNAS - Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2025400118.
Abstract: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
|
Naveen Onkarappa, & Angel Sappa. (2013). Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 483–490). LNCS. Springer Berlin Heidelberg.
Abstract: Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
Keywords: Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
|
