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| Author | Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin | ||||
| Title | Towards Automatic Concept Transfer | Type | Conference Article | ||
| Year | 2011 | Publication | Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering | Abbreviated Journal | |
| Volume | Issue | Pages | 167.176 | ||
Keywords  |
chromatic modeling, color concepts, color transfer, concept transfer | ||||
| Abstract | This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study. | ||||
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| Publisher | ACM Press | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-0907-3 | Medium | ||
| Area | Expedition | Conference | NPAR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MSM2011 | Serial | 1866 | ||
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| Author | Lu Yu; Xialei Liu; Joost Van de Weijer | ||||
| Title | Self-Training for Class-Incremental Semantic Segmentation | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | |||
| Keywords |
Class-incremental learning; Self-training; Semantic segmentation. | ||||
| Abstract | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. | ||||
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| Area | Expedition | Conference | |||
| Notes | LAMP; 600.147; 611.008; | Approved | no | ||
| Call Number | Admin @ si @ YLW2022 | Serial | 3745 | ||
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| Author | Oriol Ramos Terrades; Albert Berenguel; Debora Gil | ||||
| Title | A Flexible Outlier Detector Based on a Topology Given by Graph Communities | Type | Journal Article | ||
| Year | 2022 | Publication | Big Data Research | Abbreviated Journal | BDR |
| Volume | 29 | Issue | Pages | 100332 | |
| Keywords |
Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors | ||||
| Abstract | Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
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| Address | August 28, 2022 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | |||
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| Area | Expedition | Conference | |||
| Notes | DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 | Approved | no | ||
| Call Number | Admin @ si @ RBG2022a | Serial | 3718 | ||
| Permanent link to this record | |||||
| Author | Aura Hernandez-Sabate; Debora Gil; Petia Radeva | ||||
| Title | On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging | Type | Conference Article | ||
| Year | 2005 | Publication | Proceeding of the 2005 conference on Artificial Intelligence Research and Development | Abbreviated Journal | |
| Volume | Issue | Pages | 67-74 | ||
| Keywords |
classification; vessel border modelling; IVUS | ||||
| Abstract | IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability. | ||||
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| Publisher | IOS Press | Place of Publication | Amsterdam, The Netherlands | Editor | |
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | IAM;MILAB | Approved | no | ||
| Call Number | IAM @ iam @ HGR2005c | Serial | 1549 | ||
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| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| Title | Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy | Type | Conference Article | ||
| Year | 2006 | Publication | 18th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 719-722 | |
| Keywords |
Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization | ||||
| Abstract | Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found. | ||||
| Address | Hong Kong | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 0-7695-2521-0 | Medium | |
| Area | 800 | Expedition | Conference | ICPR | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g | Serial | 727 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
| Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords |
CNN in multispectral imaging; Image colorization | ||||
| Abstract | This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. | ||||
| Address | Porto; Portugal; June 2017 | ||||
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| Area | Expedition | Conference | PAAMS | ||
| Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2919 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th international conference on image analysis and processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords |
CNN in Multispectral Imaging; Image Colorization | ||||
| Abstract | This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. | ||||
| Address | Catania; Italy; September 2017 | ||||
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| Area | Expedition | Conference | ICIAP | ||
| Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017c | Serial | 3016 | ||
| Permanent link to this record | |||||
| Author | Eduardo Aguilar; Petia Radeva | ||||
| Title | Class-Conditional Data Augmentation Applied to Image Classification | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 11679 | Issue | Pages | 182-192 | |
| Keywords |
CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition | ||||
| Abstract | Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty. | ||||
| Address | Salermo; Italy; September 2019 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CAIP | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ AgR2019 | Serial | 3366 | ||
| Permanent link to this record | |||||
| Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
| Title | Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches | Type | Journal Article | ||
| Year | 2021 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 21 | Issue | 9 | Pages | 3185 |
| Keywords |
co-training; multi-modality; vision-based object detection; ADAS; self-driving | ||||
| Abstract | Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ GVL2021 | Serial | 3562 | ||
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| Author | Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil | ||||
| Title | Mental Workload Detection Based on EEG Analysis | Type | Conference Article | ||
| Year | 2021 | Publication | Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. | Abbreviated Journal | |
| Volume | 339 | Issue | Pages | 268-277 | |
| Keywords |
Cognitive states; Mental workload; EEG analysis; Neural Networks. | ||||
| Abstract | The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training. In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation. |
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| Address | Virtual; October 20-22 2021 | ||||
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| Area | Expedition | Conference | CCIA | ||
| Notes | IAM; 600.139; 600.118; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3723 | ||
| Permanent link to this record | |||||
| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil | ||||
| Title | Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals | Type | Journal Article | ||
| Year | 2022 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
| Volume | 12 | Issue | 5 | Pages | 2298 |
| Keywords |
Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion | ||||
| Abstract | The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. | ||||
| Address | February 2022 | ||||
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| Notes | IAM; ADAS; 600.139; 600.145; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ HYF2022 | Serial | 3720 | ||
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| Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title | Determining the Best Suited Semantic Events for Cognitive Surveillance | Type | Journal Article | ||
| Year | 2011 | Publication | Expert Systems with Applications | Abbreviated Journal | EXSY |
| Volume | 38 | Issue | 4 | Pages | 4068–4079 |
| Keywords |
Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces | ||||
| Abstract | State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ FBR2011a | Serial | 1722 | ||
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| Author | Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title | Interpretation of Complex Situations in a Semantic-based Surveillance Framework | Type | Journal | ||
| Year | 2008 | Publication | Signal Processing: Image Communication, Special Issue on Semantic Analysis for Interactive Multimedia Services | Abbreviated Journal | |
| Volume | 23 | Issue | 7 | Pages | 554-569 |
| Keywords |
Cognitive vision system; Situation analysis; Applied ontologies | ||||
| Abstract | The integration of cognitive capabilities in computer vision systems requires both to enable high semantic expressiveness and to deal with high computational costs as large amounts of data are involved in the analysis. This contribution describes a cognitive vision system conceived to automatically provide high-level interpretations of complex real-time situations in outdoor and indoor scenarios, and to eventually maintain communication with casual end users in multiple languages. The main contributions are: (i) the design of an integrative multilevel architecture for cognitive surveillance purposes; (ii) the proposal of a coherent taxonomy of knowledge to guide the process of interpretation, which leads to the conception of a situation-based ontology; (iii) the use of situational analysis for content detection and a progressive interpretation of semantically rich scenes, by managing incomplete or uncertain knowledge, and (iv) the use of such an ontological background to enable multilingual capabilities and advanced end-user interfaces. Experimental results are provided to show the feasibility of the proposed approach. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ FBR2008 | Serial | 954 | ||
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| Author | David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville | ||||
| Title | A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images | Type | Journal Article | ||
| Year | 2017 | Publication | Journal of Healthcare Engineering | Abbreviated Journal | JHCE |
| Volume | Issue | Pages | 2040-2295 | ||
| Keywords |
Colonoscopy images; Deep Learning; Semantic Segmentation | ||||
| Abstract | Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. | ||||
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| Notes | ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
| Call Number | VBS2017b | Serial | 2940 | ||
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| Author | Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal | ||||
| Title | 3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos | Type | Book Chapter | ||
| Year | 2015 | Publication | Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
| Volume | 9515 | Issue | Pages | 140-152 | |
| Keywords |
Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds | ||||
| Abstract | Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select
those image regions presenting the highest stable spatio-temporal response. Spatio-temporal stability is achieved by extracting 3D watershed regions on the temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection. |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CARE | ||
| Notes | IAM; MV; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ GSF2015 | Serial | 2733 | ||
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