Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, & Petia Radeva. (2018). Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Transactions on Multimedia, 20(12), 3266–3275.
Abstract: The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.
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David Aldavert, Arnau Ramisa, Ramon Lopez de Mantaras, & Ricardo Toledo. (2010). Fast and Robust Object Segmentation with the Integral Linear Classifier. In 23rd IEEE Conference on Computer Vision and Pattern Recognition (1046–1053).
Abstract: We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.
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David Aldavert, Arnau Ramisa, Ramon Lopez de Mantaras, & Ricardo Toledo. (2010). Real-time Object Segmentation using a Bag of Features Approach. In J.Aguilar. A. M. In R.Alquezar (Ed.), 13th International Conference of the Catalan Association for Artificial Intelligence (Vol. 220, 321–329). IOS Press Amsterdam,.
Abstract: In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.
Keywords: Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors
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Joan Arnedo-Moreno, & Agata Lapedriza. (2010). Visualizing key authenticity: turning your face into your public key. In 6th China International Conference on Information Security and Cryptology (pp. 605–618). LNCS.
Abstract: Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.
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Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, & Joost Van de Weijer. (2023). Continual Evidential Deep Learning for Out-of-Distribution Detection. In IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop (pp. 3444–3454).
Abstract: Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.
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Eduardo Aguilar, Bogdan Raducanu, Petia Radeva, & Joost Van de Weijer. (2023). Continual Evidential Deep Learning for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3444–3454).
Abstract: Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.
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Cristhian Aguilera, M.Ramos, & Angel Sappa. (2012). Simulated Annealing: A Novel Application of Image Processing in the Wood Area. In Marcos de Sales Guerra Tsuzuki (Ed.), Simulated Annealing – Advances, Applications and Hybridizations (pp. 91–104).
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Ekain Artola. (2010). Human Attention Map Prediction Combining Visual Features (Vol. 160). Bachelor's thesis, , .
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David Aldavert, Marçal Rusiñol, Ricardo Toledo, & Josep Llados. (2013). Integrating Visual and Textual Cues for Query-by-String Word Spotting. In 12th International Conference on Document Analysis and Recognition (pp. 511–515).
Abstract: In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances.
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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 Aldavert, Marçal Rusiñol, & Ricardo Toledo. (2017). Automatic Static/Variable Content Separation in Administrative Document Images. In 14th International Conference on Document Analysis and Recognition.
Abstract: In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
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Cristhian A. Aguilera-Carrasco, Angel Sappa, Cristhian Aguilera, & Ricardo Toledo. (2017). Cross-Spectral Local Descriptors via Quadruplet Network. SENS - Sensors, 17(4), 873.
Abstract: This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.
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Cristhian Aguilera, Xavier Soria, Angel Sappa, & Ricardo Toledo. (2017). RGBN Multispectral Images: a Novel Color Restoration Approach. In 15th International Conference on Practical Applications of Agents and Multi-Agent System.
Abstract: This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided.
Keywords: Multispectral Imaging; Free Sensor Model; Neural Network
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Cristhian A. Aguilera-Carrasco, Angel Sappa, & Ricardo Toledo. (2015). LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations. In 22th IEEE International Conference on Image Processing (pp. 178–181).
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Susana Alvarez, Anna Salvatella, Maria Vanrell, & Xavier Otazu. (2012). Low-dimensional and Comprehensive Color Texture Description. CVIU - Computer Vision and Image Understanding, 116(I), 54–67.
Abstract: Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap
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