Victor Ponce, Mario Gorga, Xavier Baro, Petia Radeva, & Sergio Escalera. (2011). Analisis de la Expresion Oral y Gestual en Proyectos Fin de Carrera Via un Sistema de Vision Artificial (Vol. 4).
Abstract: La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
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Victor Ponce, Mario Gorga, Xavier Baro, Petia Radeva, & Sergio Escalera. (2011). Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial. ReVisión, 4(1).
Abstract: La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
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C. Alejandro Parraga, Olivier Penacchio, & Maria Vanrell. (2011). Retinal Filtering Matches Natural Image Statistics at Low Luminance Levels. PER - Perception, 40, 96.
Abstract: The assumption that the retina’s main objective is to provide a minimum entropy representation to higher visual areas (ie efficient coding principle) allows to predict retinal filtering in space–time and colour (Atick, 1992 Network 3 213–251). This is achieved by considering the power spectra of natural images (which is proportional to 1/f2) and the suppression of retinal and image noise. However, most studies consider images within a limited range of lighting conditions (eg near noon) whereas the visual system’s spatial filtering depends on light intensity and the spatiochromatic properties of natural scenes depend of the time of the day. Here, we explore whether the dependence of visual spatial filtering on luminance match the changes in power spectrum of natural scenes at different times of the day. Using human cone-activation based naturalistic stimuli (from the Barcelona Calibrated Images Database), we show that for a range of luminance levels, the shape of the retinal CSF reflects the slope of the power spectrum at low spatial frequencies. Accordingly, the retina implements the filtering which best decorrelates the input signal at every luminance level. This result is in line with the body of work that places efficient coding as a guiding neural principle.
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C. Alejandro Parraga, Jordi Roca, & Maria Vanrell. (2011). Do Basic Colors Influence Chromatic Adaptation? VSS - Journal of Vision, 11(11), 85.
Abstract: Color constancy (the ability to perceive colors relatively stable under different illuminants) is the result of several mechanisms spread across different neural levels and responding to several visual scene cues. It is usually measured by estimating the perceived color of a grey patch under an illuminant change. In this work, we hypothesize whether chromatic adaptation (without a reference white or grey) could be driven by certain colors, specifically those corresponding to the universal color terms proposed by Berlin and Kay (1969). To this end we have developed a new psychophysical paradigm in which subjects adjust the color of a test patch (in CIELab space) to match their memory of the best example of a given color chosen from the universal terms list (grey, red, green, blue, yellow, purple, pink, orange and brown). The test patch is embedded inside a Mondrian image and presented on a calibrated CRT screen inside a dark cabin. All subjects were trained to “recall” their most exemplary colors reliably from memory and asked to always produce the same basic colors when required under several adaptation conditions. These include achromatic and colored Mondrian backgrounds, under a simulated D65 illuminant and several colored illuminants. A set of basic colors were measured for each subject under neutral conditions (achromatic background and D65 illuminant) and used as “reference” for the rest of the experiment. The colors adjusted by the subjects in each adaptation condition were compared to the reference colors under the corresponding illuminant and a “constancy index” was obtained for each of them. Our results show that for some colors the constancy index was better than for grey. The set of best adapted colors in each condition were common to a majority of subjects and were dependent on the chromaticity of the illuminant and the chromatic background considered.
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Marco Pedersoli, Andrea Vedaldi, & Jordi Gonzalez. (2011). A Coarse-to-fine Approach for fast Deformable Object Detection. In IEEE conference on Computer Vision and Pattern Recognition (pp. 1353–1360).
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Bogdan Raducanu, & Fadi Dornaika. (2011). A Discriminative Non-Linear Manifold Learning Technique for Face Recognition. In Informatics Engineering and Information Science (Vol. 254, pp. 339–353). Springer Berlin Heidelberg.
Abstract: In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance.
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Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo, & Josep Llados. (2011). Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval. In P. Clough, C. Foley, C. Gurrin, G.J.F. Jones, W. Kraaij, H. Lee, et al. (Eds.), 33rd European Conference on Information Retrieval (Vol. 6611, pp. 314–325). LNCS. Berlin: Springer.
Abstract: In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
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Marçal Rusiñol, David Aldavert, Ricardo Toledo, & Josep Llados. (2011). Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. In 11th International Conference on Document Analysis and Recognition (pp. 63–67).
Abstract: In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts.
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Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo, & Ramon Lopez de Mantaras. (2011). The IIIA30 MObile Robot Object Recognition Datset. In 11th Portuguese Robotics Open.
Abstract: Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones.
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Gemma Roig, Xavier Boix, F. de la Torre, Joan Serrat, & C. Vilella. (2011). Hierarchical CRF with product label spaces for parts-based Models. In IEEE Conference on Automatic Face and Gesture Recognition (pp. 657–664).
Abstract: Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset.
Keywords: Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features
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Miguel Reyes, Gabriel Dominguez, & Sergio Escalera. (2011). Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data. In 1st IEEE Workshop on Consumer Depth Cameras for Computer Vision (pp. 1182–1188).
Abstract: We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.
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Arnau Ramisa, Alex Goldhoorn, David Aldavert, Ricardo Toledo, & Ramon Lopez de Mantaras. (2011). Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas. JIRC - Journal of Intelligent and Robotic Systems, 64(3-4), 625–649.
Abstract: Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.
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Mario Rojas, David Masip, A. Todorov, & Jordi Vitria. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. Plos - PloS one, 6(8), e23323.
Abstract: JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions
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Mario Rojas, David Masip, & Jordi Vitria. (2011). Automatic Detection of Facial Feature Points via HOGs and Geometric Prior Models. In 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 371–378). Springer Berlin Heidelberg.
Abstract: Most applications dealing with problems involving the face require a robust estimation of the facial salient points. Nevertheless, this estimation is not usually an automated preprocessing step in applications dealing with facial expression recognition. In this paper we present a simple method to detect facial salient points in the face. It is based on a prior Point Distribution Model and a robust object descriptor. The model learns the distribution of the points from the training data, as well as the amount of variation in location each point exhibits. Using this model, we reduce the search areas to look for each point. In addition, we also exploit the global consistency of the points constellation, increasing the detection accuracy. The method was tested on two separate data sets and the results, in some cases, outperform the state of the art.
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Mario Rojas, David Masip, & Jordi Vitria. (2011). Predicting Dominance Judgements Automatically: A Machine Learning Approach. In IEEE International Workshop on Social Behavior Analysis (pp. 939–944).
Abstract: The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
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