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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 | 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 | 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 | 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 | Knowledge-Based Systems | Abbreviated Journal ![]() |
KBS |
Volume | 145 | Issue | Pages | 219-235 | |
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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. | ||||
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Series Volume | Series Issue | Edition | |||
ISSN | 0950-7051 | ISBN | Medium | ||
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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 | Knowledge-Based Systems | Abbreviated Journal ![]() |
KBS |
Volume | 252 | Issue | Pages | 109244 | |
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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 | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | ISE; 600.157; 600.168 | Approved | no | ||
Call Number | Admin @ si @ WCS2022 | Serial | 3744 | ||
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Author | Jaume Amores | ||||
Title | MILDE: multiple instance learning by discriminative embedding | Type | Journal Article | ||
Year | 2015 | Publication | 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 | ||
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Series Volume | Series Issue | Edition | |||
ISSN | 0219-1377 | ISBN | Medium | ||
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Notes | ADAS; 601.042; 600.057; 600.076 | Approved | no | ||
Call Number | Admin @ si @ Amo2015 | Serial | 2383 | ||
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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 | 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. |
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Address | 2019/11 | ||||
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Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3708 | ||
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Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
Title | Context-aware features and robust image representations | Type | Journal Article | ||
Year | 2014 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal ![]() |
JVCIR |
Volume | 25 | Issue | 2 | Pages | 339-348 |
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Abstract | Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. | ||||
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Notes | LAMP; 600.079;MILAB | Approved | no | ||
Call Number | Admin @ si @ MCG2014 | Serial | 2467 | ||
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Author | Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Robust non-blind color video watermarking using QR decomposition and entropy analysis | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal ![]() |
JVCIR |
Volume | 38 | Issue | Pages | 838-847 | |
Keywords | Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition | ||||
Abstract | Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. | ||||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @RSA2016 | Serial | 2766 | ||
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Author | Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva | ||||
Title | Egocentric video description based on temporally-linked sequences | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal ![]() |
JVCIR |
Volume | 50 | Issue | Pages | 205-216 | |
Keywords | egocentric vision; video description; deep learning; multi-modal learning | ||||
Abstract | Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description. |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ BPC2018 | Serial | 3109 | ||
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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 | |
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ DGC2018 | Serial | 3187 | ||
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Author | Eduardo Aguilar; Marc Bolaños; Petia Radeva | ||||
Title | Regularized uncertainty-based multi-task learning model for food analysis | Type | Journal Article | ||
Year | 2019 | Publication | Journal of Visual Communication and Image Representation | Abbreviated Journal ![]() |
JVCIR |
Volume | 60 | Issue | Pages | 360-370 | |
Keywords | Multi-task models; Uncertainty modeling; Convolutional neural networks; Food image analysis; Food recognition; Food group recognition; Ingredients recognition; Cuisine recognition | ||||
Abstract | Food plays an important role in several aspects of our daily life. Several computer vision approaches have been proposed for tackling food analysis problems, but very little effort has been done in developing methodologies that could take profit of the existent correlation between tasks. In this paper, we propose a new multi-task model that is able to simultaneously predict different food-related tasks, e.g. dish, cuisine and food categories. Here, we extend the homoscedastic uncertainty modeling to allow single-label and multi-label classification and propose a regularization term, which jointly weighs the tasks as well as their correlations. Furthermore, we propose a new Multi-Attribute Food dataset and a new metric, Multi-Task Accuracy. We prove that using both our uncertainty-based loss and the class regularization term, we are able to improve the coherence of outputs between different tasks. Moreover, we outperform the use of task-specific models on classical measures like accuracy or . | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ ABR2019 | Serial | 3298 | ||
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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 | |
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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 Roca; C. Alejandro Parraga; Maria Vanrell | ||||
Title | Chromatic settings and the structural color constancy index | Type | Journal Article | ||
Year | 2013 | Publication | Journal of Vision | Abbreviated Journal ![]() |
JV |
Volume | 13 | Issue | 4-3 | Pages | 1-26 |
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Abstract | Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects. | ||||
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Notes | CIC; 600.052; 600.051; 605.203 | Approved | no | ||
Call Number | Admin @ si @ RPV2013 | Serial | 2288 | ||
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Author | Arash Akbarinia; Karl R. Gegenfurtner | ||||
Title | Metameric Mismatching in Natural and Artificial Reflectances | Type | Journal Article | ||
Year | 2017 | Publication | Journal of Vision | Abbreviated Journal ![]() |
JV |
Volume | 17 | Issue | 10 | Pages | 390-390 |
Keywords | Metamer; colour perception; spectral discrimination; photoreceptors | ||||
Abstract | The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants.
We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur. |
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Address | Florida, USA; May 2017 | ||||
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ AkG2017 | Serial | 2899 | ||
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Author | Xim Cerda-Company; Xavier Otazu; Nilai Sallent; C. Alejandro Parraga | ||||
Title | The effect of luminance differences on color assimilation | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Vision | Abbreviated Journal ![]() |
JV |
Volume | 18 | Issue | 11 | Pages | 10-10 |
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Abstract | The color appearance of a surface depends on the color of its surroundings (inducers). When the perceived color shifts towards that of the surroundings, the effect is called “color assimilation” and when it shifts away from the surroundings it is called “color contrast.” There is also evidence that the phenomenon depends on the spatial configuration of the inducer, e.g., uniform surrounds tend to induce color contrast and striped surrounds tend to induce color assimilation. However, previous work found that striped surrounds under certain conditions do not induce color assimilation but induce color contrast (or do not induce anything at all), suggesting that luminance differences and high spatial frequencies could be key factors in color assimilation. Here we present a new psychophysical study of color assimilation where we assessed the contribution of luminance differences (between the target and its surround) present in striped stimuli. Our results show that luminance differences are key factors in color assimilation for stimuli varying along the s axis of MacLeod-Boynton color space, but not for stimuli varying along the l axis. This asymmetry suggests that koniocellular neural mechanisms responsible for color assimilation only contribute when there is a luminance difference, supporting the idea that mutual-inhibition has a major role in color induction. | ||||
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Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
Call Number | Admin @ si @ COS2018 | Serial | 3148 | ||
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