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Author J. Weickert; Bart M. Ter Haar Romeny; Antonio Lopez; W. Van Enk
Title (down) Orientation Analysis by Coherence-Enhancing Diffusion. Type Miscellaneous
Year 1997 Publication Proc. of the Real World Computing Symposium. Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ WTL1997 Serial 211
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Author Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund
Title (down) Organ Segmentation in Poultry Viscera Using RGB-D Type Journal Article
Year 2018 Publication Sensors Abbreviated Journal SENS
Volume 18 Issue 1 Pages 117
Keywords semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN
Abstract We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ PVJ2018 Serial 3072
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Author Javier Vazquez; C. Alejandro Parraga; Maria Vanrell
Title (down) Ordinal pairwise method for natural images comparison Type Journal Article
Year 2009 Publication Perception Abbreviated Journal PER
Volume 38 Issue Pages 180
Keywords
Abstract 38(Suppl.)ECVP Abstract Supplement
We developed a new psychophysical method to compare different colour appearance models when applied to natural scenes. The method was as follows: two images (processed by different algorithms) were displayed on a CRT monitor and observers were asked to select the most natural of them. The original images were gathered by means of a calibrated trichromatic digital camera and presented one on top of the other on a calibrated screen. The selection was made by pressing on a 6-button IR box, which allowed observers to consider not only the most natural but to rate their selection. The rating system allowed observers to register how much more natural was their chosen image (eg, much more, definitely more, slightly more), which gave us valuable extra information on the selection process. The results were analysed considering both the selection as a binary choice (using Thurstone's law of comparative judgement) and using Bradley-Terry method for ordinal comparison. Our results show a significant difference in the rating scales obtained. Although this method has been used in colour constancy algorithm comparisons, its uses are much wider, eg to compare algorithms of image compression, rendering, recolouring, etc.
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Notes CIC Approved no
Call Number CAT @ cat @ VPV2009b Serial 1191
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Author Vacit Oguz Yazici; Abel Gonzalez-Garcia; Arnau Ramisa; Bartlomiej Twardowski; Joost Van de Weijer
Title (down) Orderless Recurrent Models for Multi-label Classification Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
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Abstract Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPR
Notes LAMP; 600.109; 601.309; 600.141; 600.120 Approved no
Call Number Admin @ si @ YGR2020 Serial 3408
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Author Idoia Ruiz; Bogdan Raducanu; Rakesh Mehta; Jaume Amores
Title (down) Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation Type Journal Article
Year 2020 Publication Engineering Applications of Artificial Intelligence Abbreviated Journal EAAI
Volume 87 Issue Pages 103309
Keywords Person re-identification; Network distillation; Image retrieval; Model compression; Surveillance
Abstract Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ RRM2020 Serial 3401
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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title (down) Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain Type Journal Article
Year 2021 Publication Biomedical Signal Processing and Control Abbreviated Journal BSPC
Volume 68 Issue Pages 102535
Keywords
Abstract Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ DGR2021b Serial 3636
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Author Cristina Cañero; Petia Radeva; Oriol Pujol; Ricardo Toledo; Debora Gil; J. Saludes; Juan J. Villanueva; B. Garcia del Blanco; J. Mauri; E. Fernandez-Nofrerias; J.A. Gomez-Hospital; E. Iraculis; J. Comin; C. Quiles; F. Jara; A. Cequier; E. Esplugas
Title (down) Optimal Stent Implantation: Three-dimensional Evaluation of the Mutual Position of Stent and Vessel via Intracoronary Ecography Type Conference Article
Year 1999 Publication Proceedings of International Conference on Computer in Cardiology (CIC´99) Abbreviated Journal
Volume Issue Pages
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Abstract We present a new automatic technique to visualize and quantify the mutual position between the stent and the vessel wall by considering their three-dimensional reconstruction. Two deformable generalized cylinders adapt to the image features in all IVUS planes corresponding to the vessel wall and the stent in order to reconstruct the boundaries of the stent and the vessel in space. The image features that characterize the stent and the vessel wall are determined in terms of edge and ridge image detectors taking into account the gray level of the image pixels. We show that the 30 reconstruction by deformable cylinders is accurate and robust due to the spatial data coherence in the considered volumetric IVUS image. The main clinic utility of the stent and vessel reconstruction by deformable’ cylinders consists of its possibility to visualize and to assess the optimal stent introduction.
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Notes MILAB; RV; IAM; ADAS; HuPBA Approved no
Call Number IAM @ iam @ CRP1999a Serial 1491
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Author Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil
Title (down) Optimal Medial Surface Generation for Anatomical Volume Representations Type Book Chapter
Year 2012 Publication Abdominal Imaging. Computational and Clinical Applications Abbreviated Journal LNCS
Volume 7601 Issue Pages 265-273
Keywords Medial surface representation; volume reconstruction
Abstract Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Address Nice, France
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Yoshida, Hiroyuki and Hawkes, David and Vannier, MichaelW.
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33611-9 Medium
Area Expedition Conference STACOM
Notes IAM Approved no
Call Number IAM @ iam @ VGG2012b Serial 1988
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Author Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny
Title (down) Optimal Linear Combination for Two-class Classifiers Type Conference Article
Year 2007 Publication Proceedings of the International Conference on Advances in Pattern Recognition Abbreviated Journal
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Address Kolkata (India)
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Area Expedition Conference ICAPR
Notes DAG Approved no
Call Number DAG @ dag @ RTV2007a Serial 894
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Author Gemma Roig; Xavier Boix; Fernando De la Torre
Title (down) Optimal Feature Selection for Subspace Image Matching Type Conference Article
Year 2009 Publication 2nd IEEE International Workshop on Subspace Methods in conjunction Abbreviated Journal
Volume Issue Pages
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Abstract Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features.
Address Kyoto, Japan
Corporate Author Thesis
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference ICCV
Notes Approved no
Call Number Admin @ si @ RBT2009 Serial 1233
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Author Oriol Pujol; Petia Radeva
Title (down) Optimal extension of Error Correcting Output Codes Type Miscellaneous
Year 2006 Publication Abbreviated Journal
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Address Perpignan (France)
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Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2006d Serial 695
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Author Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
Title (down) Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework Type Journal Article
Year 2009 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 31 Issue 9 Pages 1630–1644
Keywords
Abstract The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.
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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 0162-8828 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ RVT2009 Serial 1220
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Author Maria Vanrell; Jordi Vitria
Title (down) Optimal 3x3 decomposable disks for morphological transformations Type Journal
Year 1997 Publication Image and Vision Computing, 15(2): 845–854 Abbreviated Journal
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Notes OR;CIC;MV Approved no
Call Number BCNPCL @ bcnpcl @ VaV1997c Serial 543
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
Title (down) Optical Music Recognition by Recurrent Neural Networks Type Conference Article
Year 2017 Publication 14th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 25-26
Keywords Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory
Abstract Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference ICDAR
Notes DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ BRC2017 Serial 3056
Permanent link to this record
 
 
Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
Title (down) Optical Music Recognition by Long Short-Term Memory Networks Type Book Chapter
Year 2018 Publication Graphics Recognition. Current Trends and Evolutions Abbreviated Journal
Volume 11009 Issue Pages 81-95
Keywords Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
Abstract Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor A. Fornes, B. Lamiroy
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-030-02283-9 Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no
Call Number Admin @ si @ BRC2018 Serial 3227
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