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Author | Albin Soutif; Antonio Carta; Joost Van de Weijer | ||||
Title | Improving Online Continual Learning Performance and Stability with Temporal Ensembles | Type | Conference Article | ||
Year | 2023 | Publication | 2nd Conference on Lifelong Learning Agents | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature. | ||||
Address | Montreal; Canada; August 2023 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | COLLAS | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ SCW2023 | Serial | 3922 | ||
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Author | Lasse Martensson; Anders Hast; Alicia Fornes | ||||
Title | Word Spotting as a Tool for Scribal Attribution | Type | Conference Article | ||
Year | 2017 | Publication | 2nd Conference of the association of Digital Humanities in the Nordic Countries | Abbreviated Journal | |
Volume | Issue | Pages | 87-89 | ||
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Address | Gothenburg; Suecia; March 2017 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-91-88348-83-8 | Medium | ||
Area | Expedition | Conference | DHN | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MHF2017 | Serial | 2954 | ||
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Author | Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Continual learning in cross-modal retrieval | Type | Conference Article | ||
Year | 2021 | Publication | 2nd CLVISION workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3628-3638 | ||
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Abstract | Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. | ||||
Address | Virtual; June 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120; 600.141; 600.147; 601.379 | Approved | no | ||
Call Number | Admin @ si @ WHW2021 | Serial | 3566 | ||
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Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias | ||||
Title | Scene Representations for Autonomous Driving: an approach based on polygonal primitives | Type | Conference Article | ||
Year | 2015 | Publication | 2nd Iberian Robotics Conference ROBOT2015 | Abbreviated Journal | |
Volume | 417 | Issue | Pages | 503-515 | |
Keywords | Scene reconstruction; Point cloud; Autonomous vehicles | ||||
Abstract | In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Address | Lisboa; Portugal; November 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ROBOT | ||
Notes | ADAS; 600.076; 600.086 | Approved | no | ||
Call Number | Admin @ si @ OSS2015a | Serial | 2662 | ||
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Author | J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa | ||||
Title | Visible-Thermal Fusion based Monocular Visual Odometry | Type | Conference Article | ||
Year | 2015 | Publication | 2nd Iberian Robotics Conference ROBOT2015 | Abbreviated Journal | |
Volume | 417 | Issue | Pages | 517-528 | |
Keywords | Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. | ||||
Abstract | The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Address | Lisboa; Portugal; November 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2194-5357 | ISBN | 978-3-319-27145-3 | Medium | |
Area | Expedition | Conference | ROBOT | ||
Notes | ADAS; 600.076; 600.086 | Approved | no | ||
Call Number | Admin @ si @ PAD2015 | Serial | 2663 | ||
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Author | Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil | ||||
Title | A Novel Cochlear Reference Frame Based On The Laplace Equation | Type | Conference Article | ||
Year | 2015 | Publication | 29th international Congress and Exhibition on Computer Assisted Radiology and Surgery | Abbreviated Journal | |
Volume | 10 | Issue | 1 | Pages | 1-312 |
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Abstract | Poster | ||||
Address | Barcelona; Spain; June 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CARS | ||
Notes | IAM; 600.075 | Approved | no | ||
Call Number | Admin @ si @ VGG2015 | Serial | 2615 | ||
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Author | Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote | ||||
Title | MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | multi-view; cross-view; semantic segmentation; synthetic dataset | ||||
Abstract | We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . | ||||
Address | Bordeaux; France; October2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ AWW2022 | Serial | 3781 | ||
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Author | Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva | ||||
Title | Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring | ||||
Abstract | Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. | ||||
Address | Bordeaux; France; October 2022 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ SBC2022 | Serial | 3789 | ||
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Author | Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji | ||||
Title | Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding | Type | Conference Article | ||
Year | 2022 | Publication | 29th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics | ||||
Abstract | In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM. | ||||
Address | Bordeaux; France; October 2022 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MACO | Approved | no | ||
Call Number | Admin @ si @ ZWM2022 | Serial | 3790 | ||
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Author | Jun Wan; Yibing Zhao; Shuai Zhou; Isabelle Guyon; Sergio Escalera | ||||
Title | ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
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Abstract | In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented. |
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Address | Las Vegas; USA; July 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ WZZ2016 | Serial | 2771 | ||
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Author | Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo | ||||
Title | Learning cross-spectral similarity measures with deep convolutional neural networks | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains. | ||||
Address | Las vegas; USA; June 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.086; 600.076 | Approved | no | ||
Call Number | Admin @ si @AAS2016 | Serial | 2809 | ||
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Author | Sergio Escalera; Mercedes Torres-Torres; Brais Martinez; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Georgios Tzimiropoulos; Ciprian Corneanu; Marc Oliu Simón; Mohammad Ali Bagheri; Michel Valstar | ||||
Title | ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org. | ||||
Address | Las Vegas; USA; June 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MV; | Approved | no | ||
Call Number | ETM2016 | Serial | 2849 | ||
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Author | German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez | ||||
Title | The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes | Type | Conference Article | ||
Year | 2016 | Publication | 29th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3234-3243 | ||
Keywords | Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation | ||||
Abstract | Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task | ||||
Address | Las Vegas; USA; June 2016 | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPR | ||
Notes | ADAS; 600.085; 600.082; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ RSM2016 | Serial | 2739 | ||
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Author | Xavier Otazu; Maria Vanrell | ||||
Title | Several lightness induction effects with a computational multiresolution wavelet framework | Type | Journal | ||
Year | 2006 | Publication | 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 | Abbreviated Journal | |
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Address | Saint-Petersburg (Russia) | ||||
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Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ OtV2006 | Serial | 659 | ||
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Author | Graham D. Finlayson; Javier Vazquez; Fufu Fang | ||||
Title | The Discrete Cosine Maximum Ignorance Assumption | Type | Conference Article | ||
Year | 2021 | Publication | 29th Color and Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 13-18 | ||
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Abstract | the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between wavelengths (many of the assumed reflectances are, in reality, not possible). In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely. Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets. Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set. |
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Address | Virtual; November 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | FVF2021 | Serial | 3596 | ||
Permanent link to this record |