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Author |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
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Journal Article |
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Year |
2021 |
Publication |
International Journal of Imaging Systems and Technology |
Abbreviated Journal |
IMA |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
31 |
Issue |
4 |
Pages |
2170-2188 |
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Abstract |
Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods. |
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MILAB; no menciona |
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no |
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Admin @ si @ DGR2021a |
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3630 |
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Author |
Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
Towards real-time accurate safety helmets detection through a deep learning-based method |
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Year |
2023 |
Publication |
Ingeniare. Revista chilena de ingenieria |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
31 |
Issue |
12 |
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Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.
These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability. |
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MILAB |
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Admin @ si @ CAA2023 |
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3846 |
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Sergio Escalera; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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2010 |
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IEEE on Pattern Analysis and Machine Intelligence |
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TPAMI |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
32 |
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1 |
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120–134 |
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A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ EPR2010b |
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1277 |
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Author |
Sergio Escalera; David Masip; Eloi Puertas; Petia Radeva; Oriol Pujol |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Online Error-Correcting Output Codes |
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Journal Article |
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2011 |
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Pattern Recognition Letters |
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PRL |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
32 |
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3 |
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458-467 |
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IF JCR CCIA 1.303 2009 54/103
This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier. |
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Elsevier |
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North Holland |
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0167-8655 |
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MILAB;OR;HuPBA;MV |
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Admin @ si @ EMP2011 |
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1714 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Minimal Design of Error-Correcting Output Codes |
Type |
Journal Article |
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2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume ![sorted by Volume (numeric) field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
33 |
Issue |
6 |
Pages |
693-702 |
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Keywords |
Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
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Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Elsevier |
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0167-8655 |
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MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BEB2011a |
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1800 |
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Permanent link to this record |