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| Author | Albert Clapes; Ozan Bilici; Dariia Temirova; Egils Avots; Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation | Type | Conference Article | ||
| Year | 2018 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2373-2382 | ||
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| Address | Salt Lake City; USA; June 2018 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | HUPBA | Approved | no | ||
Call Number  |
Admin @ si @ | Serial | 3116 | ||
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| Author | Bojana Gajic; Ramon Baldrich | ||||
| Title | Cross-domain fashion image retrieval | Type | Conference Article | ||
| Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 19500-19502 | ||
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| Abstract | Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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| Address | Salt Lake City, USA; 22 June 2018 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | CIC; 600.087 | Approved | no | ||
| Call Number |
Admin @ si @ | Serial | 3709 | ||
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| Author | Jon Almazan; Bojana Gajic; Naila Murray; Diane Larlus | ||||
| Title | Re-ID done right: towards good practices for person re-identification | Type | Miscellaneous | ||
| Year | 2018 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism. | ||||
| Address | January 2018 | ||||
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number |
Admin @ si @ | Serial | 3711 | ||
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| Author | Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa | ||||
| Title | Melamine Faced Panels Defect Classification beyond the Visible Spectrum | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 11 | Pages | 1-10 |
| Keywords | industrial application; infrared; machine learning | ||||
| Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. | ||||
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| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number |
Admin @ si @ AAS2018 | Serial | 3191 | ||
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| Author | Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal | ||||
| Title | Results of the use of Kahoot! gamification tool in a course of Chemistry | Type | Conference Article | ||
| Year | 2018 | Publication | 4th International Conference on Higher Education Advances | Abbreviated Journal | |
| Volume | Issue | Pages | 1215-1222 | ||
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| Abstract | The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance. | ||||
| Address | Valencia; June 2018 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | HEAD | ||
| Notes | MV; no proj | Approved | no | ||
| Call Number |
Admin @ si @ ABN2018 | Serial | 3246 | ||
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| Author | Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras | ||||
| Title | Segmentation of aerial images for plausible detail synthesis | Type | Journal Article | ||
| Year | 2018 | Publication | Computers & Graphics | Abbreviated Journal | CG |
| Volume | 71 | Issue | Pages | 23-34 | |
| Keywords | Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation | ||||
| Abstract | The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0097-8493 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MSIAU; 600.086; 600.118 | Approved | no | ||
| Call Number |
Admin @ si @ ACC2018 | Serial | 3147 | ||
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| Author | Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva | ||||
| Title | Towards social pattern characterization from egocentric photo-streams | Type | Journal Article | ||
| Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 171 | Issue | Pages | 104-117 | |
| Keywords | Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks | ||||
| Abstract | Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ ADC2018 | Serial | 3022 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Colour Constancy Beyond the Classical Receptive Field | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 40 | Issue | 9 | Pages | 2081 - 2094 |
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| Abstract | The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. | ||||
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| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number |
Admin @ si @ AkP2018a | Serial | 2990 | ||
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| Author | Arash Akbarinia; C. Alejandro Parraga | ||||
| Title | Feedback and Surround Modulated Boundary Detection | Type | Journal Article | ||
| Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 126 | Issue | 12 | Pages | 1367–1380 |
| Keywords | Boundary detection; Surround modulation; Biologically-inspired vision | ||||
| Abstract | Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. | ||||
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| Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
| Call Number |
Admin @ si @ AkP2018b | Serial | 2991 | ||
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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Manuscript text line detection and segmentation using second-order derivatives analysis | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 293 - 298 | ||
| Keywords | text line detection; text line segmentation; text region detection; second-order derivatives | ||||
| Abstract | 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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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 302.065; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ AlR2018a | Serial | 3104 | ||
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| Author | David Aldavert; Marçal Rusiñol | ||||
| Title | Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 223 - 228 | ||
| Keywords | Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information | ||||
| Abstract | Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation. |
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| Address | Viena; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.084; 600.129; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ AlR2018b | Serial | 3105 | ||
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| Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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| ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ AnE2018 | Serial | 3216 | ||
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| Author | Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva | ||||
| Title | Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | |
| Volume | 20 | Issue | 12 | Pages | 3266 - 3275 |
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| Abstract | The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ ARB2018 | Serial | 3236 | ||
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| Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Marc Masana | ||||
| Title | Context Proposals for Saliency Detection | Type | Journal Article | ||
| Year | 2018 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 174 | Issue | Pages | 1-11 | |
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| Abstract | One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist which potentially can all be salient. One way to prevent an exhaustive search over all object regions is by using object proposal algorithms. These return a limited set of regions which are most likely to contain an object. Several saliency estimation methods have used object proposals. However, they focus on the saliency of the proposal only, and the importance of its immediate context has not been evaluated. In this paper, we aim to improve salient object detection. Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation. We propose several saliency features which are computed from the context proposals. In the experiments, we evaluate five object proposal methods for the task of saliency segmentation, and find that Multiscale Combinatorial Grouping outperforms the others. Furthermore, experiments show that the proposed context features improve performance, and that our method matches results on the FT datasets and obtains competitive results on three other datasets (PASCAL-S, MSRA-B and ECSSD). |
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| Notes | LAMP; 600.109; 600.109; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ AWD2018 | Serial | 3241 | ||
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| Author | Aymen Azaza | ||||
| Title | Context, Motion and Semantic Information for Computational Saliency | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of explicit context modelling for saliency estimation. Several important works in saliency are based on the usage of object proposals. However, these methods focus on the saliency of the object proposal itself and ignore the context. To introduce context in such saliency approaches, we couple every object proposal with its direct context. This allows us to evaluate the importance of the immediate surround (context) for its saliency. We propose several saliency features which are computed from the context proposals including features based on omni-directional and horizontal context continuity. Secondly, we investigate the usage of top-downmethods (high-level semantic information) for the task of saliency prediction since most computational methods are bottom-up or only include few semantic classes. We propose to consider a wider group of object classes. These objects represent important semantic information which we will exploit in our saliency prediction approach. Thirdly, we develop a method to detect video saliency by computing saliency from supervoxels and optical flow. In addition, we apply the context features developed in this thesis for video saliency detection. The method combines shape and motion features with our proposed context features. To summarize, we prove that extending object proposals with their direct context improves the task of saliency detection in both image and video data. Also the importance of the semantic information in saliency estimation is evaluated. Finally, we propose a newmotion feature to detect saliency in video data. The three proposed novelties are evaluated on standard saliency benchmark datasets and are shown to improve with respect to state-of-the-art. |
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| Address | October 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Ali Douik | |
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| ISSN | ISBN | 978-84-945373-9-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number |
Admin @ si @ Aza2018 | Serial | 3218 | ||
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