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	Author	Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
	Title	Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain			Type	Journal Article
	Year	2021	Publication	Biomedical Signal Processing and Control	Abbreviated Journal	BSPC
	Volume	68	Issue		Pages	102535
	Keywords
	Abstract	Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ DGR2021b			Serial	3636
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	Author	Reuben Dorent; Aaron Kujawa; Marina Ivory; Spyridon Bakas; Nikola Rieke; Samuel Joutard; Ben Glocker; Jorge Cardoso; Marc Modat; Kayhan Batmanghelich; Arseniy Belkov; Maria Baldeon Calisto; Jae Won Choi; Benoit M. Dawant; Hexin Dong; Sergio Escalera; Yubo Fan; Lasse Hansen; Mattias P. Heinrich; Smriti Joshi; Victoriya Kashtanova; Hyeon Gyu Kim; Satoshi Kondo; Christian N. Kruse; Susana K. Lai-Yuen; Hao Li; Han Liu; Buntheng Ly; Ipek Oguz; Hyungseob Shin; Boris Shirokikh; Zixian Su; Guotai Wang; Jianghao Wu; Yanwu Xu; Kai Yao; Li Zhang; Sebastien Ourselin,
	Title	CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation			Type	Journal Article
	Year	2023	Publication	Medical Image Analysis	Abbreviated Journal	MIA
	Volume	83	Issue		Pages	102628
	Keywords	Domain Adaptation; Segmen tation; Vestibular Schwnannoma
	Abstract	Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice – VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice – VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.
	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	HUPBA			Approved	no
	Call Number	Admin @ si @ DKI2023			Serial	3706
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	Author	Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera
	Title	RGB-D-based Human Motion Recognition with Deep Learning: A Survey			Type	Journal Article
	Year	2018	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	171	Issue		Pages	118-139
	Keywords	Human motion recognition; RGB-D data; Deep learning; Survey
	Abstract	Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.
	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	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ WLO2018			Serial	3123
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	Author	Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar; Petia Radeva
	Title	Deep ensemble-based hard sample mining for food recognition			Type	Journal Article
	Year	2023	Publication	Journal of Visual Communication and Image Representation	Abbreviated Journal	JVCIR
	Volume	95	Issue		Pages	103905
	Keywords
	Abstract	Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance 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	MILAB			Approved	no
	Call Number	Admin @ si @ NBA2023			Serial	3844
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	Author	Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva
	Title	Introduction to the special issue: Egocentric Vision and Lifelogging			Type	Journal Article
	Year	2018	Publication	Journal of Visual Communication and Image Representation	Abbreviated Journal	JVCIR
	Volume	55	Issue		Pages	352-353
	Keywords
	Abstract
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ DGC2018			Serial	3187
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	Author	Md Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Vivek Kumar Singh; Syeda Furruka Banu; Forhad U H Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig; Mohamed Abdel-Nasser
	Title	SLSNet: Skin lesion segmentation using a lightweight generative adversarial network			Type	Journal Article
	Year	2021	Publication	Expert Systems With Applications	Abbreviated Journal	ESWA
	Volume	183	Issue		Pages	115433
	Keywords
	Abstract	The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SRA2021			Serial	3633
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	Author	Andreea Glavan; Alina Matei; Petia Radeva; Estefania Talavera
	Title	Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams			Type	Journal Article
	Year	2021	Publication	Expert Systems with Applications	Abbreviated Journal	ESWA
	Volume	171	Issue		Pages	114506
	Keywords
	Abstract	Nutrition and social interactions are both key aspects of the daily lives of humans. In this work, we propose a system to evaluate the influence of social interaction in the nutritional habits of a person from a first-person perspective. In order to detect the routine of an individual, we construct a nutritional behaviour pattern discovery model, which outputs routines over a number of days. Our method evaluates similarity of routines with respect to visited food-related scenes over the collected days, making use of Dynamic Time Warping, as well as considering social engagement and its correlation with food-related activities. The nutritional and social descriptors of the collected days are evaluated and encoded using an LSTM Autoencoder. Later, the obtained latent space is clustered to find similar days unaffected by outliers using the Isolation Forest method. Moreover, we introduce a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100 k egocentric images gathered by 7 users. Several different visualizations are evaluated for the understanding of the findings. Our results demonstrate good performance and applicability of our proposed model for social-related nutritional behaviour understanding. At the end, relevant applications of the model are discussed by analysing the discovered routine of particular individuals.
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ GMR2021			Serial	3634
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	Author	Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
	Title	Hand sign language recognition using multi-view hand skeleton			Type	Journal Article
	Year	2020	Publication	Expert Systems With Applications	Abbreviated Journal	ESWA
	Volume	150	Issue		Pages	113336
	Keywords	Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition
	Abstract	Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition.
	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	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ RKE2020a			Serial	3411
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	Author	F.Negin; Pau Rodriguez; M.Koperski; A.Kerboua; Jordi Gonzalez; J.Bourgeois; E.Chapoulie; P.Robert; F.Bremond
	Title	PRAXIS: Towards automatic cognitive assessment using gesture recognition			Type	Journal Article
	Year	2018	Publication	Expert Systems with Applications	Abbreviated Journal	ESWA
	Volume	106	Issue		Pages	21-35
	Keywords
	Abstract	Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults. In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames. We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis.
	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	ISE			Approved	no
	Call Number	Admin @ si @ NRK2018			Serial	3669
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	Author	Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate
	Title	An overview of incremental feature extraction methods based on linear subspaces			Type	Journal Article
	Year	2018	Publication	Knowledge-Based Systems	Abbreviated Journal	KBS
	Volume	145	Issue		Pages	219-235
	Keywords
	Abstract	With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.
	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	0950-7051	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ DFH2018			Serial	3090
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	Author	P. Canals; Simone Balocco; O. Diaz; J. Li; A. Garcia Tornel; M. Olive Gadea; M. Ribo
	Title	A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning			Type	Journal Article
	Year	2023	Publication	Computerized Medical Imaging and Graphics	Abbreviated Journal	CMIG
	Volume	104	Issue	102170	Pages
	Keywords	Artificial intelligence; Deep learning; Stroke; Thrombectomy; Vascular feature extraction; Vascular tortuosity
	Abstract	Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.
	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	MILAB			Approved	no
	Call Number	Admin @ si @ CBD2023			Serial	4005
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	Author	Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer
	Title	Self-supervised blur detection from synthetically blurred scenes			Type	Journal Article
	Year	2019	Publication	Image and Vision Computing	Abbreviated Journal	IMAVIS
	Volume	92	Issue		Pages	103804
	Keywords
	Abstract	Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.
	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; 600.109; 600.120			Approved	no
	Call Number	Admin @ si @ AGG2019			Serial	3301
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	Author	Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
	Title	Multimodal grid features and cell pointers for scene text visual question answering			Type	Journal Article
	Year	2021	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	150	Issue		Pages	242-249
	Keywords
	Abstract	This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link.
	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	DAG; 600.084; 600.121			Approved	no
	Call Number	Admin @ si @ GBT2021			Serial	3620
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	Author	B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols
	Title	Knowledge graph based methods for record linkage			Type	Journal Article
	Year	2020	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	136	Issue		Pages	127-133
	Keywords
	Abstract	Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner. In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
	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	DAG; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ GRP2020			Serial	3453
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	Author	Ivet Rafegas; Maria Vanrell
	Title	Color encoding in biologically-inspired convolutional neural networks			Type	Journal Article
	Year	2018	Publication	Vision Research	Abbreviated Journal	VR
	Volume	151	Issue		Pages	7-17
	Keywords	Color coding; Computer vision; Deep learning; Convolutional neural networks
	Abstract	Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations.
	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	CIC; 600.051; 600.087			Approved	no
	Call Number	Admin @ si @RaV2018			Serial	3114
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