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Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, & Xavier Roca. (2019). Towards Visual Personality Questionnaires based on Deep Learning and Social Media. In 21st International Conference on Social Influence and Social Psychology.
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Asma Bensalah, Antonio Parziale, Giuseppe De Gregorio, Angelo Marcelli, Alicia Fornes, & Josep Llados. (2023). I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation. In 21st International Graphonomics Conference (136–148).
Abstract: During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
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Fadi Dornaika, & Angel Sappa. (2006). 3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm. In 21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541.
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Esmitt Ramirez, Carles Sanchez, & Debora Gil. (2019). Localizing Pulmonary Lesions Using Fuzzy Deep Learning. In 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 290–294).
Abstract: The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.
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S.Grau, Anna Puig, Sergio Escalera, Maria Salamo, & Oscar Amoros. (2013). Efficient complementary viewpoint selection in volume rendering. In 21st WSCG Conference on Computer Graphics,.
Abstract: A major goal of visualization is to appropriately express knowledge of scientific data. Generally, gathering visual information contained in the volume data often requires a lot of expertise from the final user to setup the parameters of the visualization. One way of alleviating this problem is to provide the position of inner structures with different viewpoint locations to enhance the perception and construction of the mental image. To this end, traditional illustrations use two or three different views of the regions of interest. Similarly, with the aim of assisting the users to easily place a good viewpoint location, this paper proposes an automatic and interactive method that locates different complementary viewpoints from a reference camera in volume datasets. Specifically, the proposed method combines the quantity of information each camera provides for each structure and the shape similarity of the projections of the remaining viewpoints based on Dynamic Time Warping. The selected complementary viewpoints allow a better understanding of the focused structure in several applications. Thus, the user interactively receives feedback based on several viewpoints that helps him to understand the visual information. A live-user evaluation on different data sets show a good convergence to useful complementary viewpoints.
Keywords: Dual camera; Visualization; Interactive Interfaces; Dynamic Time Warping.
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Neus Salvatella, E Fernandez-Nofrerias, Francesco Ciompi, O. Rodriguez-Leor, Xavier Carrillo, R. Hemetsberger, et al. (2010). Canvis de volum a la arteria radial despres de la administracio de dos tractaments vasodilatadors. Avaluacio mitjançant ecografia intravascular. In 22nd Congres Societat Catalana de Cardiologia, (179).
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O. Rodriguez-Leor, R. Hemetsberger, Francesco Ciompi, E Fernandez-Nofrerias, Angel Serrano, M. Bernet, et al. (2010). Caracteritzacio automatica de la placa mitjançant analisis del espectre de radiofreqüencia en estudi de ecografia intracoronaria: resultat de la fusio de dades invivo i exvivo. In 22nd Congres Societat Catalana de Cardiologia, (131).
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Jose Manuel Alvarez, Theo Gevers, & Antonio Lopez. (2009). Learning Photometric Invariance from Diversified Color Model Ensembles. In 22nd IEEE Conference on Computer Vision and Pattern Recognition (565–572).
Abstract: Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.
Keywords: road detection
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Arjan Gijsenij, Theo Gevers, & Joost Van de Weijer. (2009). Physics-based Edge Evaluation for Improved Color Constancy. In 22nd IEEE Conference on Computer Vision and Pattern Recognition (581 – 588).
Abstract: Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation.
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Md.Mostafa Kamal Sarker, Syeda Furruka Banu, Hatem A. Rashwan, Mohamed Abdel-Nasser, Vivek Kumar Singh, Sylvie Chambon, et al. (2019). Food Places Classification in Egocentric Images Using Siamese Neural Networks. In 22nd International Conference of the Catalan Association of Artificial Intelligence (pp. 145–151).
Abstract: Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
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Patricia Suarez, Dario Carpio, & Angel Sappa. (2023). A Deep Learning Based Approach for Synthesizing Realistic Depth Maps. In 22nd International Conference on Image Analysis and Processing (Vol. 14234, 369–380). LNCS.
Abstract: This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.
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Jiaolong Xu, Sebastian Ramos, David Vazquez, & Antonio Lopez. (2014). Cost-sensitive Structured SVM for Multi-category Domain Adaptation. In 22nd International Conference on Pattern Recognition (pp. 3886–3891). IEEE.
Abstract: Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition.
Keywords: Domain Adaptation; Pedestrian Detection
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2014). Generic Subclass Ensemble: A Novel Approach to Ensemble Classification. In 22nd International Conference on Pattern Recognition (pp. 1254–1259).
Abstract: Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches.
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Mohammad Ali Bagheri, Gang Hu, Qigang Gao, & Sergio Escalera. (2014). A Framework of Multi-Classifier Fusion for Human Action Recognition. In 22nd International Conference on Pattern Recognition (pp. 1260–1265).
Abstract: The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.
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Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, & Michael Felsberg. (2014). Scale Coding Bag-of-Words for Action Recognition. In 22nd International Conference on Pattern Recognition (pp. 1514–1519).
Abstract: Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant
strategy is sub-optimal since it ignores the multi-scale information
available with each bounding box of a person.
This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music,
riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
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