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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Measuring Interest of Human Dyadic Interactions |
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Conference Article |
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2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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45-54 |
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In this paper, we argue that only using behavioural motion information, we are able to predict the interest of observers when looking at face-to-face interactions. We propose a set of movement-related features from body, face, and mouth activity in order to define a set of higher level interaction features, such as stress, activity, speaking engagement, and corporal engagement. Error-Correcting Output Codes framework with an Adaboost base classifier is used to learn to rank the perceived observer's interest in face-to-face interactions. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers. In particular, the learning system shows that stress features have a high predictive power for ranking interest of observers when looking at of face-to-face interactions. |
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Cardona (Spain) |
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978-1-60750-061-2 |
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CCIA |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ EPR2009b |
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1182 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
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Measuring External Face Appearance for Face Classification. |
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2007 |
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Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing |
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Vienna (Austria) |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2007b |
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940 |
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David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo |
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Measuring bottom-up visual attention in eye tracking experimentation with synthetic images |
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Conference Article |
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2019 |
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8th Iberian Conference on Perception |
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A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. |
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San Lorenzo El Escorial; July 2019 |
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CIP |
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NEUROBIT; 600.128 |
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Admin @ si @ BFO2019c |
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3375 |
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Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Mathematics learning opportunities when playing a Tower Defense Game |
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2015 |
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International Journal of Serious Games |
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IJSG |
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2 |
Issue |
4 |
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57-71 |
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Tower Defense game; learning opportunities; mathematics; problem solving; game design |
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A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. |
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ADAS; 600.076 |
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Admin @ si @ HJG2015 |
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2730 |
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Author |
Maria Vanrell; Jordi Vitria |
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Mathematical Morphology, Granulometries and Texture Perception. |
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Miscellaneous |
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1993 |
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SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). |
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San Diego; CA; USA |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1993 |
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178 |
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Author |
David Roche; Debora Gil; Jesus Giraldo |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? |
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2014 |
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G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology |
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796 |
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3 |
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159-181 |
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β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model |
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Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists. |
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Springer Netherlands |
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0065-2598 |
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978-94-007-7422-3 |
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IAM; 600.075 |
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IAM @ iam @ RGG2014 |
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2197 |
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Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Matching visual induction effects on screens of different size |
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Journal Article |
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2021 |
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Journal of Vision |
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JOV |
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21 |
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6(10) |
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1-22 |
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In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. |
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CIC |
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Admin @ si @ CVM2021 |
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3595 |
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Author |
Francisco Jose Perales; Juan J. Villanueva; Yuhua Luo |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Matching Criteria |
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Conference Article |
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1991 |
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Computer and Information Sciences VI, Proceedings of the 1991 International Symposium on Computer and Information Sciences |
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1 |
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1029-1038 |
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Antalya, Turkey, 30 October-2 November 1991 |
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Elsevier Science Pub. |
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0097-8493 |
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ISE @ ise @ PVL1991a |
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264 |
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Bonifaz Stuhr; Jurgen Brauer; Bernhard Schick; Jordi Gonzalez |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation |
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2023 |
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Arxiv |
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A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level. |
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Admin @ si @ SBS2023 |
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3863 |
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Author |
Sounak Dey |
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Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This thesis presents several contributions to the literature of sketch based image retrieval (SBIR). In SBIR the first challenge we face is how to map two different domains to common space for effective retrieval of images, while tackling the different levels of abstraction people use to express their notion of objects around while sketching. To this extent we first propose a cross-modal learning framework that maps both sketches and text into a joint embedding space invariant to depictive style, while preserving semantics. Then we have also investigated different query types possible to encompass people's dilema in sketching certain world objects. For this we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set.
Finally, we explore the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognises two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended. We also in this dissertation pave the path to the future direction of research in this domain. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Umapada Pal |
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978-84-121011-8-8 |
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DAG; 600.121 |
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Admin @ si @ Dey20 |
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3480 |
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Jose Carlos Rubio |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Many-to-Many High Order Matching. Applications to Tracking and Object Segmentation |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Feature matching is a fundamental problem in Computer Vision, having multiple applications such as tracking, image classification and retrieval, shape recognition and stereo fusion. In numerous domains, it is useful to represent the local structure of the matching features to increase the matching accuracy or to make the correspondence invariant to certain transformations (affine, homography, etc. . . ). However, encoding this knowledge requires complicating the model by establishing high-order relationships between the model elements, and therefore increasing the complexity of the optimization problem.
The importance of many-to-many matching is sometimes dismissed in the literature. Most methods are restricted to perform one-to-one matching, and are usually validated on synthetic, or non-realistic datasets. In a real challenging environment, with scale, pose and illumination variations of the object of interest, as well as the presence of occlusions, clutter, and noisy observations, many-to-many matching is necessary to achieve satisfactory results. As a consequence, finding the most likely many-to-many correspondence often involves a challenging combinatorial optimization process.
In this work, we design and demonstrate matching algorithms that compute many-to-many correspondences, applied to several challenging problems. Our goal is to make use of high-order representations to improve the expressive power of the matching, at the same time that we make feasible the process of inference or optimization of such models. We effectively use graphical models as our preferred representation because they provide an elegant probabilistic framework to tackle structured prediction problems.
We introduce a matching-based tracking algorithm which performs matching between frames of a video sequence in order to solve the difficult problem of headlight tracking at night-time. We also generalise this algorithm to solve the problem of data association applied to various tracking scenarios. We demonstrate the effectiveness of such approach in real video sequences and we show that our tracking algorithm can be used to improve the accuracy of a headlight classification system.
In the second part of this work, we move from single (point) matching to dense (region) matching and we introduce a new hierarchical image representation. We make use of such model to develop a high-order many-to-many matching between pairs of images. We show that the use of high-order models in comparison to simpler models improves not only the accuracy of the results, but also the convergence speed of the inference algorithm.
Finally, we keep exploiting the idea of region matching to design a fully unsupervised image co-segmentation algorithm that is able to perform competitively with state-of-the-art supervised methods. Our method also overcomes the typical drawbacks of some of the past works, such as avoiding the necessity of variate appearances on the image backgrounds. The region matching in this case is applied to effectively exploit inter-image information. We also extend this work to perform co-segmentation of videos, being the first time that such problem is addressed, as a way to perform video object segmentation |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Joan Serrat |
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ADAS |
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Admin @ si @ Rub2012 |
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2206 |
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Author |
David Aldavert; Marçal Rusiñol |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Manuscript text line detection and segmentation using second-order derivatives analysis |
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Conference Article |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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293 - 298 |
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text line detection; text line segmentation; text region detection; second-order derivatives |
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In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.129; 302.065; 600.121 |
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Debora Gil; Jaume Garcia; Aura Hernandez-Sabate; Enric Marti |
![download PDF file pdf](img/file_PDF.gif)
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Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy |
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2010 |
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8th Medical Imaging |
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7623 |
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762304 |
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304 |
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Distortion of Left Ventricle (LV) external anatomy is related to some dysfunctions, such as hypertrophy. The architecture of myocardial fibers determines LV electromechanical activation patterns as well as mechanics. Thus, their joined modelling would allow the design of specific interventions (such as peacemaker implantation and LV remodelling) and therapies (such as resynchronization). On one hand, accurate modelling of external anatomy requires either a dense sampling or a continuous infinite dimensional approach, which requires non-Euclidean statistics. On the other hand, computation of fiber models requires statistics on Riemannian spaces. Most approaches compute separate statistical models for external anatomy and fibers architecture. In this work we propose a general mathematical framework based on differential geometry concepts for computing a statistical model including, both, external and fiber anatomy. Our framework provides a continuous approach to external anatomy supporting standard statistics. We also provide a straightforward formula for the computation of the Riemannian fiber statistics. We have applied our methodology to the computation of complete anatomical atlas of canine hearts from diffusion tensor studies. The orientation of fibers over the average external geometry agrees with the segmental description of orientations reported in the literature. |
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Vacit Oguz Yazici; Longlong Yu; Arnau Ramisa; Luis Herranz; Joost Van de Weijer |
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Main product detection with graph networks for fashion |
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2024 |
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Multimedia Tools and Applications |
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MTAP |
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83 |
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3215–3231 |
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Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin. |
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LAMP; MACO; 600.147; 600.167; 600.164; 600.161; 600.141; 601.309 |
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Admin @ si @ YYR2024 |
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4017 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
![download PDF file pdf](img/file_PDF.gif)
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MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
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2018 |
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European Conference on Computer Vision workshops |
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423-433 |
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First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
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ECCVW |
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MILAB; no menciona |
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Admin @ si @ SRR2018b |
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3185 |
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