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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 (up) 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.
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Notes MILAB Approved no
Call Number Admin @ si @ NBA2023 Serial 3844
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Author Jordi Vitria; X. Binefa; Juan J. Villanueva
Title Morphological Algorithms for Visual Analysis of Integrated Circuits. Type Miscellaneous
Year 1992 Publication (up) Journal of Visual Communications and image Representation, Vol.3, No.2, pp.194–202. Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ VBV1992 Serial 248
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Author X. Binefa; J.M. Sanchez; Petia Radeva; Jordi Vitria
Title Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains. Type Miscellaneous
Year 2000 Publication (up) Journal of Visual Languages and Computing, 11(3):253–271. Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ BRS2000 Serial 337
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Author Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title Detailed 3D face reconstruction from a single RGB image Type Journal
Year 2019 Publication (up) Journal of WSCG Abbreviated Journal JWSCG
Volume 27 Issue 2 Pages 103-112
Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.
Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
Address 2019/11
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes ADAS; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3708
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Author Hamdi Dibeklioglu; M.O. Hortas; I. Kosunen; P. Zuzánek; Albert Ali Salah; Theo Gevers
Title Design and implementation of an affect-responsive interactive photo frame Type Journal
Year 2011 Publication (up) Journal on Multimodal User Interfaces Abbreviated Journal JMUI
Volume 4 Issue 2 Pages 81-95
Keywords
Abstract This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user’s affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem.
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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 1783-7677 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ DHK2011 Serial 1842
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Author F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra
Title Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. Type Journal Article
Year 1994 Publication (up) Kidney International Abbreviated Journal
Volume 46 Issue 6 Pages 1721-1727
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MSV1994 Serial 113
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Author Jaume Amores
Title MILDE: multiple instance learning by discriminative embedding Type Journal Article
Year 2015 Publication (up) Knowledge and Information Systems Abbreviated Journal KAIS
Volume 42 Issue 2 Pages 381-407
Keywords Multi-instance learning; Codebook; Bag of words
Abstract While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
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Publisher Springer London Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0219-1377 ISBN Medium
Area Expedition Conference
Notes ADAS; 601.042; 600.057; 600.076 Approved no
Call Number Admin @ si @ Amo2015 Serial 2383
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate
Title Decremental generalized discriminative common vectors applied to images classification Type Journal Article
Year 2017 Publication (up) Knowledge-Based Systems Abbreviated Journal KBS
Volume 131 Issue Pages 46-57
Keywords Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification
Abstract In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.
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Notes ADAS; 600.118; 600.121 Approved no
Call Number Admin @ si @ DMH2017a Serial 3003
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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 (up) 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
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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 Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez
Title Image rain removal and illumination enhancement done in one go Type Journal Article
Year 2022 Publication (up) Knowledge-Based Systems Abbreviated Journal KBS
Volume 252 Issue Pages 109244
Keywords
Abstract Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement.
Address Sept 2022
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
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Notes ISE; 600.157; 600.168 Approved no
Call Number Admin @ si @ WCS2022 Serial 3744
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Author Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang
Title Diffusion-based network for unsupervised landmark detection Type Journal Article
Year 2024 Publication (up) Knowledge-Based Systems Abbreviated Journal
Volume 292 Issue Pages 111627
Keywords
Abstract Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases.
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Notes LAMP Approved no
Call Number Admin @ si @ WWT2024 Serial 4024
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes Type Journal
Year 2013 Publication (up) Komputer Sapiens Abbreviated Journal KS
Volume 1 Issue Pages 20-25
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Notes OR;MV Approved no
Call Number Admin @ si @ TSR2013 Serial 2231
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Author Fernando Vilariño
Title Computer Vision and Performing Arts Type Conference Article
Year 2015 Publication (up) Korean Scholars of Marketing Science Abbreviated Journal
Volume Issue Pages
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Address Seoul; Korea; October 2015
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Area Expedition Conference KAMS
Notes MV;SIAI Approved no
Call Number Admin @ si @Vil2015 Serial 2799
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Author J. Elder; Fadi Dornaika; Y. Hou; R. Goldstein
Title Attentive wide-field sensing for visual telepresence and surveillance Type Book Chapter
Year 2005 Publication (up) L. Itti, G. Rees and J. Tsotsos (editors), Neurobiology of Attention, Academic Press / Elsevier Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ EDH2005 Serial 604
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Author Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer
Title AutoCV Challenge Design and Baseline Results Type Conference Article
Year 2019 Publication (up) La Conference sur l’Apprentissage Automatique Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019.
Address Toulouse; Francia; July 2019
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LGJ2019 Serial 3323
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