Eloi Puertas, Miguel Angel Bautista, Daniel Sanchez, Sergio Escalera, & Oriol Pujol. (2014). Learning to Segment Humans by Stacking their Body Parts,. In ECCV Workshop on ChaLearn Looking at People (Vol. 8925, pp. 685–697). LNCS.
Abstract: Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body
part likelihood maps. These likelihood maps are obtained in a first stage
by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline.
Keywords: Human body segmentation; Stacked Sequential Learning
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Ellen J.L. Brunenberg, Oriol Pujol, Bart M. Ter Haar Romeny, & Petia Radeva. (2006). Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake.
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Elena Valderrama, Joan Oliver, Josep Maria-Basart, Enric Marti, Petia Radeva, Ricardo Toledo, et al. (2005). Convergencia al EEES de la ingeniería informática. Título de Grado en tecnología (Informática).
Abstract: Elena Valderrama
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Ekta Vats, Anders Hast, & Alicia Fornes. (2019). Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion. In 15th International Conference on Document Analysis and Recognition (pp. 1294–1299).
Abstract: Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
Keywords: Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching
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Ekaterina Zaytseva, Santiago Segui, & Jordi Vitria. (2012). Sketchable Histograms of Oriented Gradients for Object Detection. In 17th Iberomerican Conference on Pattern Recognition (Vol. 7441, pp. 374–381). Springer Berlin Heidelberg.
Abstract: In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.
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Ekaterina Zaytseva, & Jordi Vitria. (2012). A search based approach to non maximum suppression in face detection. In 19th IEEE International Conference on Image Processing.
Abstract: Poster
paper TA.P5.12
Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results.
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Ekain Artola. (2010). Human Attention Map Prediction Combining Visual Features (Vol. 160). Bachelor's thesis, , .
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Eirikur Agustsson, Radu Timofte, Sergio Escalera, Xavier Baro, Isabelle Guyon, & Rasmus Rothe. (2017). Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database. In 12th IEEE International Conference on Automatic Face and Gesture Recognition.
Abstract: After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods.
The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.
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Egils Avots, Meysam Madadi, Sergio Escalera, Jordi Gonzalez, Xavier Baro, Paul Pallin, et al. (2019). From 2D to 3D geodesic-based garment matching. MTAP - Multimedia Tools and Applications, 78(18), 25829–25853.
Abstract: A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.
Keywords: Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model
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Egils Avots, M. Daneshmanda, Andres Traumann, Sergio Escalera, & G. Anbarjafaria. (2016). Automatic garment retexturing based on infrared information. CG - Computers & Graphics, 59, 28–38.
Abstract: This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms.
Keywords: Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading
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Eduardo Tusa, Arash Akbarinia, Raquel Gil Rodriguez, & Corina Barbalata. (2015). Real-Time Face Detection and Tracking Utilising OpenMP and ROS. In 3rd Asia-Pacific Conference on Computer Aided System Engineering (pp. 179–184).
Abstract: The first requisite of a robot to succeed in social interactions is accurate human localisation, i.e. subject detection and tracking. Later, it is estimated whether an interaction partner seeks attention, for example by interpreting the position and orientation of the body. In computer vision, these cues usually are obtained in colour images, whose qualities are degraded in ill illuminated social scenes. In these scenarios depth sensors offer a richer representation. Therefore, it is important to combine colour and depth information. The
second aspect that plays a fundamental role in the acceptance of social robots is their real-time-ability. Processing colour and depth images is computationally demanding. To overcome this we propose a parallelisation strategy of face detection and tracking based on two different architectures: message passing and shared memory. Our results demonstrate high accuracy in
low computational time, processing nine times more number of frames in a parallel implementation. This provides a real-time social robot interaction.
Keywords: RGB-D; Kinect; Human Detection and Tracking; ROS; OpenMP
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Eduardo Aguilar, & Petia Radeva. (2019). Class-Conditional Data Augmentation Applied to Image Classification. In 18th International Conference on Computer Analysis of Images and Patterns (Vol. 11679, pp. 182–192). LNCS.
Abstract: Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty.
Keywords: CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition
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Eduardo Aguilar, & Petia Radeva. (2019). Food Recognition by Integrating Local and Flat Classifiers. In 9th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 11867, pp. 65–74). LNCS.
Abstract: The recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier; otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier.
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Eduardo Aguilar, & Petia Radeva. (2020). Uncertainty-aware integration of local and flat classifiers for food recognition. PRL - Pattern Recognition Letters, 136, 237–243.
Abstract: Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
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Eduardo Aguilar, Marc Bolaños, & Petia Radeva. (2019). Regularized uncertainty-based multi-task learning model for food analysis. JVCIR - Journal of Visual Communication and Image Representation, 60, 360–370.
Abstract: Food plays an important role in several aspects of our daily life. Several computer vision approaches have been proposed for tackling food analysis problems, but very little effort has been done in developing methodologies that could take profit of the existent correlation between tasks. In this paper, we propose a new multi-task model that is able to simultaneously predict different food-related tasks, e.g. dish, cuisine and food categories. Here, we extend the homoscedastic uncertainty modeling to allow single-label and multi-label classification and propose a regularization term, which jointly weighs the tasks as well as their correlations. Furthermore, we propose a new Multi-Attribute Food dataset and a new metric, Multi-Task Accuracy. We prove that using both our uncertainty-based loss and the class regularization term, we are able to improve the coherence of outputs between different tasks. Moreover, we outperform the use of task-specific models on classical measures like accuracy or .
Keywords: Multi-task models; Uncertainty modeling; Convolutional neural networks; Food image analysis; Food recognition; Food group recognition; Ingredients recognition; Cuisine recognition
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