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ELCVIA : Electronic Letters on Computer Vision and Image Analysis
ELCVIA (ISSN electrònic 1577-5097) és una revista exclusivament electrònica coneguda a nivell internacional sobre recerca i aplicacions de la visió per computador i l’anàlisi d’imatges. El seu comitè editorial està format per experts reconeguts a nivell internacional. Tots els articles són revisats per especialistes mitjançant peer-review, i actualment té un percentatge d'acceptació del 25% dels articles rebuts. Els principals objectius de la revista són:
  • Ser una revista de referència a nivell internacional dins del camp de la Visió per Computador.
  • Ser una publicació de qualitat.
  • Ser un mitjà de comunicació ràpid i dinàmic, amb una revisió ràpida dels articles.
  • Tenir índex d'impacte dins la base de dades de l'SCI.
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2024-11-16
10:01
15 p, 19.0 MB An Efficient Deep Learning based License Plate Recognition for Smart Cities / Swati (Patel National Institute of Technology (India)) ; Dinesh Kawa, Shubh (Patel National Institute of Technology (India)) ; Kamble, Shubham (Patel National Institute of Technology (India)) ; Desai, Darshit (Patel National Institute of Technology (India)) ; Himanshu Karelia, Pratik (Patel National Institute of Technology (India)) ; Engineer, Pinalkumar (Patel National Institute of Technology (India))
Computer vision algorithm with the amalgamation of deep learning technologies has provided endless possible applications. Currently, with the high load of vehicle traffic it is very difficult to trace and capture vehicular information over traffic surveillance on roads, parking or for safety concerns. [...]
2024 - 10.5565/rev/elcvia.1917
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 50-64 (Regular Issue)  
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2024-11-16
10:01
20 p, 4.7 MB Dr DAH-Unet : A modified UNet for Semantic Segmentation of MRI images for brain tumour detection / Potnuru, Mohankrishna (GITAM University (India)) ; Suribabu Naick, B. (GITAM University (India))
Using sophisticated image processing techniques on brain MR images for medical image segmentation significantly improves the ability to detect tumors. It takes a lot of time and requires a doctor's training and experience to manually segment a brain tumor. [...]
2024 - 10.5565/rev/elcvia.1755
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 29-49 (Regular Issue)  
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2024-11-16
10:01
20 p, 5.4 MB A Labeled Array Distance Metric for Measuring Image Segmentation Quality / Berijanian, Maryam (Michigan State University (USA)) ; Gensterblum, Katrina (Michigan State University (USA)) ; Mutlu, Doruk Alp (Michigan State University (USA)) ; Reagan, Katelyn (University of Wisconsin-Madison (USA)) ; Hart, Andrew (Michigan State University (USA)) ; Colbry, Dirk (Michigan State University (USA))
This work introduces two new distance metrics for comparing labeled arrays, which are common outputs of image segmentation algorithms. Each pixel in an image is assigned a label, with binary segmentation providing only two labels ('foreground' and 'background'). [...]
2024 - 10.5565/rev/elcvia.1941
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 65-84 (Regular Issue)  
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2024-09-07
08:55
28 p, 4.1 MB Deep Learning based-framework for Math Formulas Understanding / Kacem Echi, Afef (University of Tunis (Tunísia)) ; Ben Aïcha, Takwa (University of Tunis (Tunísia)) ; Kawther Khazri Ayeb (University of Tunis (Tunísia))
Extracting mathematical formulas from images of scientific documents and converting them into structured data for storage in a database is essential for their further use. However, recognizing and extracting math formulas automatically, rapidly, and effectively can be challenging. [...]
2024 - 10.5565/rev/elcvia.1833
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) (Regular Issue)  
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2024-07-13
10:39
13 p, 1.1 MB Classification of radiological patterns of tuberculosis with a Convolutional neural network in x-ray images / Trueba Espinosa, Adrian (Universidad Autónoma del Estado de México (México)) ; Sanchez -Arrazola, Jessica (Universidad Autónoma del Estado de México (México)) ; Cervantes, Jair (Universidad Autónoma del Estado de México (México)) ; Garcia-Lamont, Farid (Universidad Autónoma del Estado de México (México)) ; Ruiz Castilla, José Sergio (Universidad Autónoma del Estado de México (México)) ; Karthik Kantipudi (National Institutes of Health (US))
In this paper we propose the classification of radiological patterns with the presence of tuberculosis in X-ray images, it was observed that two to six patterns (consolidation, fibrosis, opacity, opacity, pleural, nodules and cavitations) are present in the radiographs of the patients. [...]
2024 - 10.5565/rev/elcvia.1561
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 47-59 (Regular Issue)  
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2024-07-05
06:45
15 p, 559.1 KB Multi-Biometric System Based On The Fusion Of Fingerprint And Finger-Vein / Vijayarajan, Jeyalakshmi (Mepco Schlenk Engineering College (Índia)) ; Pooja, K (Mepco Schlenk Engineering College (Índia)) ; Pethanatchi, C (Mepco Schlenk Engineering College (Índia))
Biometrics is the process of measuring the unique biological traits of an individual for identification and verification purposes. Multiple features are used to enhance the security and robustness of the system. [...]
2024 - 10.5565/rev/elcvia.1822
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 32-46 (Regular Issue)  
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2024-06-08
10:41
16 p, 969.7 KB Off-line identifying Script Writers by Swin Transformers and ResNeSt-50 / Kacem Echi, Afef (University of Tunis (Tunísia)) ; Ben Aïcha, Takwa (University of Tunis (Tunísia))
In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50. Swin Transformer is known for its robustness to variations and ability to model long-range dependencies, which helps capture context and make robust predictions. [...]
2024 - 10.5565/rev/elcvia.1787
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 15-31 (Regular Issue)  
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2024-04-24
06:00
14 p, 1.2 MB A multimodal biometric authentication system using of autoencoders and Siamese networks for enhanced security / Gueuret, Théo (University Gustave Eiffel (França)) ; Kerkeni, Leila (Capgemini engineering (França))
Ensuring secure and reliable identity verification is crucial, and biometric authentication plays a significant role in achieving this. However, relying on a single biometric trait, unimodal authentication, may have accuracy and attack vulnerability limitations. [...]
2024 - 10.5565/rev/elcvia.1811
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 1-14 (Regular Issue)  
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2024-03-16
09:25
16 p, 867.9 KB ERNet : Enhanced ResNet for classification of breast histopathological images / Chandana, Mani RK (Vellore Institute of Technology. School of Information Technology and Engineering (Índia)) ; Kamalakannan, J. (Vellore Institute of Technology. School of Information Technology and Engineering (Índia))
Inspite of expeditious approaches in field of breast cancer, histopathological analysis is considered as gold standard in diagnosis of cancer. Researchers are working tremendously to automate the detection and analysis of breast histology images, which confess in improving the accuracy and also induce the mimisation of processing time. [...]
2023 - 10.5565/rev/elcvia.1614
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 22 Núm. 2 (2023) , p. 53-68 (Regular Issue)  
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2024-03-16
09:25
12 p, 489.1 KB Robust fingerprint recognition approach based on diagonal slice of polyspectra in the polar space / Ismaili Alaoui, E.M. (University of Moulay Ismail. Faculty of Sciences (Meknès, Marroc))
Although fingerprint recognition is a mature technology and nowadays commercial state-of-the-art systems can be successfully used in a number of real applications, not all the problems have been solved and the research is still very active in the field. [...]
2023 - 10.5565/rev/elcvia.1523
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 22 Núm. 2 (2023) , p. 41-52 (Regular Issue)  
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