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| Author | Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell | ||||
| Title | Segmentation of Distal Airways using Structural Analysis | Type  |
Journal Article | ||
| Year | 2019 | Publication | PloS one | Abbreviated Journal | Plos |
| Volume | 14 | Issue | 12 | Pages | |
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| Abstract | Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. | ||||
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| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GSB2019 | Serial | 3357 | ||
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| Author | Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau | ||||
| Title | Deep spectral reflectance and illuminant estimation from self-interreflections | Type |
Journal Article | ||
| Year | 2019 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 31 | Issue | 1 | Pages | 105-114 |
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| Abstract | In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ DWM2019 | Serial | 3362 | ||
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| Author | Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit | ||||
| Title | Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding | Type |
Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 149 | Issue | Pages | 164-171 | |
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| Abstract | Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. | ||||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DGV2021 | Serial | 3364 | ||
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| Author | Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva | ||||
| Title | Hierarchical approach to classify food scenes in egocentric photo-streams | Type |
Journal Article | ||
| Year | 2020 | Publication | IEEE Journal of Biomedical and Health Informatics | Abbreviated Journal | J-BHI |
| Volume | 24 | Issue | 3 | Pages | 866 - 877 |
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| Abstract | Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ TLM2020 | Serial | 3380 | ||
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| Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
| Title | Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images | Type |
Journal Article | ||
| Year | 2020 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
| Volume | 10 | Issue | 22 | Pages | 8170 |
| Keywords | sentiment analysis, personality trait analysis; weakly-supervised learning; visual classification; OCEAN model; social networks | ||||
| Abstract | Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. | ||||
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| Notes | ISE; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RVC2020b | Serial | 3553 | ||
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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 | |
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| 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. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ DGR2021b | Serial | 3636 | ||
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| Author | Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores | ||||
| Title | Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation | Type |
Journal Article | ||
| Year | 2020 | Publication | Engineering Applications of Artificial Intelligence | Abbreviated Journal | EAAI |
| Volume | 87 | Issue | Pages | 103309 | |
| Keywords | Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance | ||||
| Abstract | Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance. | ||||
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| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ RRM2020 | Serial | 3401 | ||
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| Author | Ana Garcia Rodriguez; Jorge Bernal; F. Javier Sanchez; Henry Cordova; Rodrigo Garces Duran; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Polyp fingerprint: automatic recognition of colorectal polyps’ unique features | Type |
Journal Article | ||
| Year | 2020 | Publication | Surgical Endoscopy and other Interventional Techniques | Abbreviated Journal | SEND |
| Volume | 34 | Issue | 4 | Pages | 1887-1889 |
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| Abstract | BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint'). METHODS: A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset. RESULTS: The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%). CONCLUSIONS: A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition. KEYWORDS: Artificial intelligence; Colorectal polyps; Content-based image retrieval |
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| Notes | MV; no menciona | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3403 | ||
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| Author | Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach | ||||
| Title | Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia | Type |
Journal Article | ||
| Year | 2020 | Publication | Gastroenterología y Hepatología | Abbreviated Journal | GH |
| Volume | 43 | Issue | 4 | Pages | 222-232 |
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| Abstract | Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented. | ||||
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| Notes | MV; | Approved | no | ||
| Call Number | Admin @ si @ SBG2020 | Serial | 3404 | ||
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| Author | Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez | ||||
| Title | Recognizing new classes with synthetic data in the loop: application to traffic sign recognition | Type |
Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 3 | Pages | 583 |
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| Abstract | On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive. | ||||
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| Notes | LAMP; ADAS; 600.118; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ VWL2020 | Serial | 3405 | ||
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| Author | Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier | ||||
| Title | Modeling, Recognizing, and Explaining Apparent Personality from Videos | Type |
Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume | 13 | Issue | 2 | Pages | 894-911 |
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| Abstract | Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future. | ||||
| Address | 1 April-June 2022 | ||||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ EKS2022 | Serial | 3406 | ||
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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 | |
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| 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. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ GMR2021 | Serial | 3634 | ||
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| Author | Khalid El Asnaoui; Petia Radeva | ||||
| Title | Automatically Assess Day Similarity Using Visual Lifelogs | Type |
Journal Article | ||
| Year | 2020 | Publication | International Journal of Intelligent Systems | Abbreviated Journal | IJIS |
| Volume | 29 | Issue | Pages | 298–310 | |
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| Abstract | Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | AsR2020 | Serial | 3409 | ||
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| Author | Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez | ||||
| Title | DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation | Type |
Journal Article | ||
| Year | 2020 | Publication | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Abbreviated Journal | JSTAEOR |
| Volume | 13 | Issue | Pages | 726-737 | |
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| Abstract | One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ TRR2020 | Serial | 3410 | ||
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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. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKE2020a | Serial | 3411 | ||
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