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	Author	Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund
	Title	A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points			Type	Journal Article
	Year	2012	Publication	IEEE Journal of Selected Topics in Signal Processing	Abbreviated Journal	J-STSP
	Volume	6	Issue	5	Pages	553-565
	Keywords
	Abstract	In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1932-4553	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ HCG2012			Serial	1994
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	Author	Diego Cheda; Daniel Ponsa; Antonio Lopez
	Title	Monocular Depth-based Background Estimation			Type	Conference Article
	Year	2012	Publication	7th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	323-328
	Keywords
	Abstract	In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences.
	Address	Roma
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	VISAPP
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e			Serial	2012
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	Author	Marçal Rusiñol; Josep Llados; Gemma Sanchez
	Title	Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings			Type	Journal Article
	Year	2010	Publication	Pattern Analysis and Applications	Abbreviated Journal	PAA
	Volume	13	Issue	3	Pages	321-331
	Keywords
	Abstract	In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1433-7541	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RLS2010			Serial	1165
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	Author	Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva
	Title	VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering			Type	Conference Article
	Year	2017	Publication	8th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
	Abstract	In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
	Address	Faro; Portugal; June 2017
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ BPC2017			Serial	2939
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	Author	Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal
	Title	Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing			Type	Journal Article
	Year	2014	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	72	Issue	1	Pages	515-539
	Keywords
	Abstract	In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.
	Address
	Corporate Author				Thesis
	Publisher	Springer US	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1380-7501	ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ SDT2014			Serial	2357
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	Author	Ignasi Rius; Jordi Gonzalez; Javier Varona; Xavier Roca
	Title	Action-specific motion prior for efficient bayesian 3D human body tracking			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	11	Pages	2907–2921
	Keywords
	Abstract	In this paper, we aim to reconstruct the 3D motion parameters of a human body model from the known 2D positions of a reduced set of joints in the image plane. Towards this end, an action-speciﬁc motion model is trained from a database of real motion-captured performances. The learnt motion model is used within a particle ﬁltering framework as a priori knowledge on human motion. First, our dynamic model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D conﬁguration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ RGV2009			Serial	1159
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	Author	Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria
	Title	Measuring Interest of Human Dyadic Interactions			Type	Conference Article
	Year	2009	Publication	12th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	202	Issue		Pages	45-54
	Keywords
	Abstract	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.
	Address	Cardona (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-60750-061-2	Medium
	Area		Expedition		Conference	CCIA
	Notes	OR;MILAB;HuPBA;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EPR2009b			Serial	1182
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	Author	Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
	Title	Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12822	Issue		Pages	381–395
	Keywords
	Abstract	In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
	Address	Lausanne; Suissa; September 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ RMG2021			Serial	3572
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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.
	Address	Viena; Austria; April 2018
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	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	Mikhail Mozerov; Joost Van de Weijer
	Title	One-view occlusion detection for stereo matching with a fully connected CRF model			Type	Journal Article
	Year	2019	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	28	Issue	6	Pages	2936-2947
	Keywords	Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter
	Abstract	In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.098; 600.109; 602.133; 600.120			Approved	no
	Call Number	Admin @ si @ MoW2019			Serial	3221
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	Author	Mariella Dimiccoli; Petia Radeva
	Title	Lifelogging in the era of outstanding digitization			Type	Conference Article
	Year	2015	Publication	International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior.
	Address	Verliko Tarmovo; Bulgaria; September 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	DiPP
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @DiR2016			Serial	2792
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	Author	Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes
	Title	ASTRA: An Action Spotting TRAnsformer for Soccer Videos			Type	Conference Article
	Year	2023	Publication	Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports	Abbreviated Journal
	Volume		Issue		Pages	93–102
	Keywords
	Abstract	In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set.
	Address	Otawa; Canada; October 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	MMSports
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ XEM2023			Serial	3970
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	Author	Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
	Title	One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift			Type	Miscellaneous
	Year	2022	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve (SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a ( +1)-way classifier to predict the source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show: After source training, the resulting source model can get excellent performance for ; After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; no proj			Approved	no
	Call Number	Admin @ si @ YWW2022c			Serial	3818
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	Author	Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer
	Title	Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization			Type	Conference Article
	Year	2022	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis
	Abstract	In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1
	Address	New Orleans; USA; June 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	LAMP; 600.147			Approved	no
	Call Number	Admin @ si @ PJT2022			Serial	3784
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	Author	T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo
	Title	Multi-Spectral Stereo Odometry			Type	Journal Article
	Year	2015	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	16	Issue	3	Pages	1210-1224
	Keywords	Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery
	Abstract	In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.055; 600.076			Approved	no
	Call Number	Admin @ si @ MAS2015a			Serial	2533
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