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| Author | C. Alejandro Parraga; Arash Akbarinia | ||||
Title  |
NICE: A Computational Solution to Close the Gap from Colour Perception to Colour Categorization | Type | Journal Article | ||
| Year | 2016 | Publication | PLoS One | Abbreviated Journal | Plos |
| Volume | 11 | Issue | 3 | Pages | e0149538 |
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| Abstract | The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms. | ||||
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| Notes | NEUROBIT; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ PaA2016a | Serial | 2747 | ||
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| Author | Ferran Diego; G.D. Evangelidis; Joan Serrat | ||||
| Title |
Night-time outdoor surveillance by mobile cameras | Type | Conference Article | ||
| Year | 2012 | Publication | 1st International Conference on Pattern Recognition Applications and Methods | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 365-371 | |
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| Abstract | This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. | ||||
| Address | Algarve, Portugal | ||||
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| Area | Expedition | Conference | ICPRAM | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ DES2012 | Serial | 2035 | ||
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| Author | Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat | ||||
| Title |
Nightime Vehicle Detecion for Intelligent Headlight Control | Type | Conference Article | ||
| Year | 2008 | Publication | Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, | Abbreviated Journal | |
| Volume | 5259 | Issue | Pages | 113–124 | |
| Keywords | Intelligent Headlights; vehicle detection | ||||
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| Address | Juan-les-Pins, France | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ACIVS | ||
| Notes | ADAS;CIC | Approved | no | ||
| Call Number | ADAS @ adas @ LHB2008a | Serial | 1098 | ||
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| Author | Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte | ||||
| Title |
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement | Type | Conference Article | ||
| Year | 2024 | Publication | 38th AAAI Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. | ||||
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| Area | Expedition | Conference | AAAI | ||
| Notes | CIC; MACO | Approved | no | ||
| Call Number | Admin @ si @ CVB2024 | Serial | 3872 | ||
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| Author | Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera | ||||
| Title |
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging | Type | Conference Article | ||
| Year | 2022 | Publication | International MICCAI Brainlesion Workshop | Abbreviated Journal | |
| Volume | 12963 | Issue | Pages | 540–551 | |
| Keywords | Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN | ||||
| Abstract | In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ JOM2022 | Serial | 3800 | ||
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| Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
| Title |
No more meta-parameter tuning in unsupervised sparse feature learning | Type | Miscellaneous | ||
| Year | 2014 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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| Notes | MILAB; LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ RRG2014 | Serial | 2471 | ||
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| Author | Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez | ||||
| Title |
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest | Type | Miscellaneous | ||
| Year | 2016 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Pedestrian detection; Random Forest | ||||
| Abstract | Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ MVJ2016 | Serial | 2868 | ||
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| Author | Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown | ||||
| Title |
Noise Prism: A Novel Multispectral Visualization Technique | Type | Journal Article | ||
| Year | 2021 | Publication | 31st Color and Imaging Conference | Abbreviated Journal | |
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| Abstract | A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique. |
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| Area | Expedition | Conference | CIC | ||
| Notes | MACO; CIC | Approved | no | ||
| Call Number | Admin @ si @ CVL2021 | Serial | 4000 | ||
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| Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
| Title |
Noise suppression over bi-level graphical documents using a sparse representation | Type | Conference Article | ||
| Year | 2012 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
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| Address | Bordeaux | ||||
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| Area | Expedition | Conference | CIFED | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DTR2012b | Serial | 2136 | ||
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| Author | J.R. Serra; S. Casadei; J.B. Subirana | ||||
| Title |
Non-Cartesian Networks for Middle Level Vision. | Type | Miscellaneous | ||
| Year | 1995 | Publication | VI National Simposium on Pattern Recognition and image Analysis | Abbreviated Journal | |
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| Address | Cordoba | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ SCS1995 | Serial | 232 | ||
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| Author | Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas | ||||
| Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings | Type | Conference Article | ||
| Year | 2018 | Publication | International Workshop on Reproducible Research in Pattern Recognition | Abbreviated Journal | |
| Volume | 11455 | Issue | Pages | 71-82 | |
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| Abstract | Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ NDC2018 | Serial | 3178 | ||
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| Author | Patricia Suarez; Dario Carpio; Angel Sappa | ||||
| Title |
Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture | Type | Conference Article | ||
| Year | 2021 | Publication | 16th International Symposium on Visual Computing | Abbreviated Journal | |
| Volume | 13018 | Issue | Pages | 178–190 | |
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| Abstract | This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result. | ||||
| Address | Virtual; October 2021 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ISVC | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SCS2021 | Serial | 3668 | ||
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| Author | David Guillamet; Jordi Vitria | ||||
| Title |
Non-negative Matrix Factorization for Face Recognition. | Type | Miscellaneous | ||
| Year | 2002 | Publication | Abbreviated Journal | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GuV2002a | Serial | 280 | ||
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| Author | David Guillamet; Jordi Vitria | ||||
| Title |
Non-negative Matrix Factorization to Extract Part-Based Representations. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Abbreviated Journal | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GuV2001a | Serial | 100 | ||
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| Author | David Guillamet; Jordi Vitria | ||||
| Title |
Non-negative Matrix Factorization to Extract Part-Based Representations. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Accepted for 4th Catalan Conference for Artificial Intelligence. | Abbreviated Journal | |
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| Address | Barcelona | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GVi2001a | Serial | 105 | ||
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