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Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Which tone-mapping operator is the best? A comparative study of perceptual quality |
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Journal Article |
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2018 |
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Journal of the Optical Society of America A |
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JOSA A |
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35 |
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4 |
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626-638 |
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Abstract |
Tone-mapping operators (TMO) are designed to generate perceptually similar low-dynamic range images from high-dynamic range ones. We studied the performance of fifteen TMOs in two psychophysical experiments where observers compared the digitally-generated tone-mapped images to their corresponding physical scenes. All experiments were performed in a controlled environment and the setups were
designed to emphasize different image properties: in the first experiment we evaluated the local relationships among intensity-levels, and in the second one we evaluated global visual appearance among physical scenes and tone-mapped images, which were presented side by side. We ranked the TMOs according
to how well they reproduced the results obtained in the physical scene. Our results show that ranking position clearly depends on the adopted evaluation criteria, which implies that, in general, these tone-mapping algorithms consider either local or global image attributes but rarely both. Regarding the
question of which TMO is the best, KimKautz [1] and Krawczyk [2] obtained the better results across the different experiments. We conclude that a more thorough and standardized evaluation criteria is needed to study all the characteristics of TMOs, as there is ample room for improvement in future developments. |
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NEUROBIT; 600.120; 600.128 |
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no |
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Admin @ si @ CPO2018 |
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3088 |
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Author |
Xim Cerda-Company; Olivier Penacchio; Xavier Otazu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Chromatic Induction in Migraine |
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Journal |
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2021 |
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VISION |
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5 |
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3 |
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37 |
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migraine; vision; colour; colour perception; chromatic induction; psychophysics |
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The human visual system is not a colorimeter. The perceived colour of a region does not only depend on its colour spectrum, but also on the colour spectra and geometric arrangement of neighbouring regions, a phenomenon called chromatic induction. Chromatic induction is thought to be driven by lateral interactions: the activity of a central neuron is modified by stimuli outside its classical receptive field through excitatory–inhibitory mechanisms. As there is growing evidence of an excitation/inhibition imbalance in migraine, we compared chromatic induction in migraine and control groups. As hypothesised, we found a difference in the strength of induction between the two groups, with stronger induction effects in migraine. On the other hand, given the increased prevalence of visual phenomena in migraine with aura, we also hypothesised that the difference between migraine and control would be more important in migraine with aura than in migraine without aura. Our experiments did not support this hypothesis. Taken together, our results suggest a link between excitation/inhibition imbalance and increased induction effects. |
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NEUROBIT; no proj |
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Admin @ si @ CPO2021 |
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3589 |
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Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly |
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Journal Article |
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2018 |
Publication |
Machine Vision and Applications |
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MVAP |
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29 |
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5 |
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765–788 |
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Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology |
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We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach. |
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HUPBA; no proj |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPP2018 |
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3125 |
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Author |
S. Chanda; Umapada Pal; Oriol Ramos Terrades |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Word-Wise Thai and Roman Script Identification |
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Journal |
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2009 |
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ACM Transactions on Asian Language Information Processing |
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TALIP |
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8 |
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3 |
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1-21 |
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In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. |
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1530-0226 |
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DAG |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2009f |
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1869 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Human Activity Recognition from Accelerometer Data using a Wearable Device |
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Conference Article |
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2011 |
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5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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289-296 |
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Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%. |
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Las Palmas de Gran Canaria. Spain |
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Springer Berlin Heidelberg |
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Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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MILAB;HuPBA |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2011a |
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1735 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Approximate Convex Hulls Family for One-Class Cassification |
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Conference Article |
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Year |
2011 |
Publication |
10th International Workshop on Multiple Classifier Systems |
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6713 |
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106-115 |
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In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification. |
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Napoli, Italy |
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Springer Berlin Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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0302-9743 |
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978-3-642-21556-8 |
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MCS |
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MILAB;HuPBA |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2011b |
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1761 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
User Verification From Walking Activity. First Steps Towards a Personal Verification System |
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Conference Article |
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2011 |
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1st International Conference on Pervasive and Embedded Computing and Communication Systems |
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Algarve, Portugal |
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PECCS |
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MILAB;HuPBA |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2011c |
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1762 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Personalization and User Verification in Wearable Systems using Biometric Walking Patterns |
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2012 |
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Personal and Ubiquitous Computing |
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PUC |
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16 |
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5 |
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563-580 |
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In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies. |
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Springer-Verlag |
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1617-4909 |
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MILAB;HuPBA |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2012 |
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1706 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Approximate polytope ensemble for one-class classification |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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2 |
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854-864 |
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One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning |
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In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. |
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MILAB; 605.203 |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2014a |
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2469 |
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Author |
Francesco Ciompi; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
ECOC-DRF: Discriminative random fields based on error correcting output codes |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
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6 |
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2193-2204 |
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Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models |
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We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. |
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LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPR2014b |
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2470 |
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Author |
T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
The achromatic locus: Effect of navigation direction in color space |
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Journal Article |
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2014 |
Publication |
Journal of Vision |
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VSS |
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14 (1) |
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25 |
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1-11 |
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achromatic; unique hues; color constancy; luminance; color space |
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5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes. |
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DAG; 600.077 |
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no |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CPX2014 |
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2418 |
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Author |
Qingshan Chen; Zhenzhen Quan; Yifan Hu; Yujun Li; Zhi Liu; Mikhail Mozerov |
![goto web page url](img/www.gif)
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MSIF: multi-spectrum image fusion method for cross-modality person re-identification |
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Journal Article |
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2023 |
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International Journal of Machine Learning and Cybernetics |
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IJMLC |
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Sketch-RGB cross-modality person re-identification (ReID) is a challenging task that aims to match a sketch portrait drawn by a professional artist with a full-body photo taken by surveillance equipment to deal with situations where the monitoring equipment is damaged at the accident scene. However, sketch portraits only provide highly abstract frontal body contour information and lack other important features such as color, pose, behavior, etc. The difference in saliency between the two modalities brings new challenges to cross-modality person ReID. To overcome this problem, this paper proposes a novel dual-stream model for cross-modality person ReID, which is able to mine modality-invariant features to reduce the discrepancy between sketch and camera images end-to-end. More specifically, we propose a multi-spectrum image fusion (MSIF) method, which aims to exploit the image appearance changes brought by multiple spectrums and guide the network to mine modality-invariant commonalities during training. It only processes the spectrum of the input images without adding additional calculations and model complexity, which can be easily integrated into other models. Moreover, we introduce a joint structure via a generalized mean pooling (GMP) layer and a self-attention (SA) mechanism to balance background and texture information and obtain the regional features with a large amount of information in the image. To further shrink the intra-class distance, a weighted regularized triplet (WRT) loss is developed without introducing additional hyperparameters. The model was first evaluated on the PKU Sketch ReID dataset, and extensive experimental results show that the Rank-1/mAP accuracy of our method is 87.00%/91.12%, reaching the current state-of-the-art performance. To further validate the effectiveness of our approach in handling cross-modality person ReID, we conducted experiments on two commonly used IR-RGB datasets (SYSU-MM01 and RegDB). The obtained results show that our method achieves competitive performance. These results confirm the ability of our method to effectively process images from different modalities. |
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Call Number ![sorted by Call Number field, ascending order (up)](img/sort_asc.gif) |
Admin @ si @ CQH2023 |
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3885 |
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Author |
Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov |
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Title |
An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification |
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Journal Article |
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2023 |
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IEEE Sensors Journal |
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IEEE-SENS |
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23 |
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24 |
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Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov |
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Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%. |
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LAMP |
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no |
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Admin @ si @ CQL2023 |
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3884 |
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Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
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Title |
Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications |
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Journal Article |
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2020 |
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IEEE Access |
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ACCESS |
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8 |
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77344 - 77363 |
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Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. |
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MILAB; no proj |
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Admin @ si @ CRD2020 |
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3436 |
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Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
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Modeling long-term interactions to enhance action recognition |
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2021 |
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25th International Conference on Pattern Recognition |
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10351-10358 |
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In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
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January 2021 |
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ICPR |
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MILAB; |
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Admin @ si @ CRD2021 |
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3626 |
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