Fadi Dornaika, & Bogdan Raducanu. (2009). Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach. In IEEE Workshop on Applications of Computer Vision.
Abstract: This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.
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Jorge Bernal, Fernando Vilariño, F. Javier Sanchez, M. Arnold, Anarta Ghosh, & Gerard Lacey. (2014). Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search. In 2014 Symposium on Eye Tracking Research and Applications (pp. 223–226).
Abstract: We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2001). Human Activity Learning and Recognition from Appearance..
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Josep Llados, Felipe Lumbreras, V. Chapaprieta, & J. Queralt. (2001). ICAR: Identity Card Automatic Reader..
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Gemma Sanchez, & Josep Llados. (2001). A Graph Grammar to Recognize Textured Symbols..
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Lubomir Latchev, Maya Dimitrova, & David Rotger. (2006). A Classifier of Technical Diagnostic States of Electrocardiograph.
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Jordi Vitria, & J. Llacer. (1993). Recovering Depth from Focus Using Iterative image Estimation Techniques..
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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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Pierdomenico Fiadino, Victor Ponce, Juan Antonio Torrero-Gonzalez, & Marc Torrent-Moreno. (2017). Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era". In Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (pp. 43–48).
Abstract: The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users.
By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “
always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations.
Keywords: mobile networks; call detail records; human mobility
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Antonio Esteban Lansaque. (2014). 3D reconstruction and recognition using structured ligth (Vol. 179). Master's thesis, , .
Abstract: This work covers the problem of 3D reconstruction, recognition and 6DOF pose estimation. The goal of this project is to reconstruct a 3D scene and to align an object model of the industrial pieces onto the reconstructed scene. The reconstruction algorithm is based on stereo techniques and the recognition algorithm is based on SHOT descriptors computed on a set of uniform keypoints. Correspondences are used to estimate a first 6DOF transformation that maps the model onto the scene and then ICP algorithm is used to refine the transformation. In order to check the effectiveness of the proposed algorithm, several experiments were performed. These experiments were conducted on a lab environment in order to get results under the same conditions in all of them. Although obtained results are not real time results, the proposed algorithm ends up with high rates of object recognition.
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Ricard Balague. (2014). Exploring the combination of color cues for intrinsic image decomposition (Vol. 178). Master's thesis, , .
Abstract: Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth.
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Sebastian Ramos. (2014). Vision-based Detection of Road Hazards for Autonomous Driving. Master's thesis, , .
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Mireia Sole, Joan Blanco, Debora Gil, G. Fonseka, Richard Frodsham, Oliver Valero, et al. (2017). Unraveling the enigmas of chromosome territoriality during spermatogenesis. In IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia.
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Enric Marti, J. Rocarias, A. Sanchez, Petia Radeva, Ricardo Toledo, & Jordi Vitria. (2006). Caronte: un gestor documental para asignaturas del EEES.
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Cristina Palmero, Oleg V Komogortsev, Sergio Escalera, & Sachin S Talathi. (2023). Multi-Rate Sensor Fusion for Unconstrained Near-Eye Gaze Estimation. In Proceedings of the 2023 Symposium on Eye Tracking Research and Applications (pp. 1–8).
Abstract: The power requirements of video-oculography systems can be prohibitive for high-speed operation on portable devices. Recently, low-power alternatives such as photosensors have been evaluated, providing gaze estimates at high frequency with a trade-off in accuracy and robustness. Potentially, an approach combining slow/high-fidelity and fast/low-fidelity sensors should be able to exploit their complementarity to track fast eye motion accurately and robustly. To foster research on this topic, we introduce OpenSFEDS, a near-eye gaze estimation dataset containing approximately 2M synthetic camera-photosensor image pairs sampled at 500 Hz under varied appearance and camera position. We also formulate the task of sensor fusion for gaze estimation, proposing a deep learning framework consisting in appearance-based encoding and temporal eye-state dynamics. We evaluate several single- and multi-rate fusion baselines on OpenSFEDS, achieving 8.7% error decrease when tracking fast eye movements with a multi-rate approach vs. a gaze forecasting approach operating with a low-speed sensor alone.
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