|
Records |
Links |
|
Author |
Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization |
Type |
Journal Article |
|
Year |
2021 |
Publication |
International Journal of Imaging Systems and Technology |
Abbreviated Journal |
IMA |
|
|
Volume |
31 |
Issue |
4 |
Pages |
2170-2188 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
|
|
|
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 ![sorted by ISSN field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGR2021a |
Serial |
3630 |
|
Permanent link to this record |
|
|
|
|
Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
Type |
Journal Article |
|
Year |
2021 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
9 |
Issue |
|
Pages |
17495-17506 |
|
|
Keywords |
|
|
|
Abstract |
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
|
|
Address |
|
|
|
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 ![sorted by ISSN field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ MGR2021 |
Serial |
3637 |
|
Permanent link to this record |
|
|
|
|
Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Bayesian deep learning for semantic segmentation of food images |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Computers and Electrical Engineering |
Abbreviated Journal |
CEE |
|
|
Volume |
103 |
Issue |
|
Pages |
108380 |
|
|
Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
|
|
Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
|
|
Address |
October 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Science Direct |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN ![sorted by ISSN field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ ANR2022 |
Serial |
3763 |
|
Permanent link to this record |
|
|
|
|
Author |
Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar; Petia Radeva |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Deep ensemble-based hard sample mining for food recognition |
Type |
Journal Article |
|
Year |
2023 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
|
|
Volume |
95 |
Issue |
|
Pages |
103905 |
|
|
Keywords |
|
|
|
Abstract |
Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics. |
|
|
Address |
|
|
|
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 ![sorted by ISSN field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ NBA2023 |
Serial |
3844 |
|
Permanent link to this record |
|
|
|
|
Author |
Roger Max Calle Quispe; Maya Aghaei Gavari; Eduardo Aguilar Torres |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Towards real-time accurate safety helmets detection through a deep learning-based method |
Type |
Journal |
|
Year |
2023 |
Publication |
Ingeniare. Revista chilena de ingenieria |
Abbreviated Journal |
|
|
|
Volume |
31 |
Issue |
12 |
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
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. |
|
|
Address |
|
|
|
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 ![sorted by ISSN field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ CAA2023 |
Serial |
3846 |
|
Permanent link to this record |