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ELCVIA : Electronic Letters on Computer Vision and Image Analysis
ELCVIA (ISSN electrónico 1577-5097) es una revista exclusivamente electrónica conocida a nivel internacional sobre investigación y aplicaciones de la visión por computadora y el análisis de imágenes. Su comité editorial está formado por expertos reconocidos internacionalmente. Todos los artículos son revisados por especialistas mediante peer-review y actualmente tiene un porcentaje de aceptación del 25% de los artículos recibidos. Los principales objetivos de la revista son:
  • Ser una revista de referencia a nivel internacional en el campo de la Visión por Computador.
  • Ser una publicación de calidad.
  • Ser un medio de comunicación rápido y dinámico, con una revisión rápida de los artículos.
  • Tener índice de impacto dentro de la base de datos de SCI.
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2025-11-21
04:11
19 p, 4.0 MB A reversible data hiding techniques for improved embedding capacity using image interpolation / D., Shahi (Noorul Islam Center for Higher Education (Índia)) ; Kumar R. S., Vinod (Noorul Islam Center for Higher Education (Índia)) ; A. R., Bushara (KMEA Engineering College (Índia))
High capacity steganography is still challenging today in the field of information security. The demand for the exact retrieval of the cover media from stego-image after the extraction of secret data is also increasing. [...]
2025 - 10.5565/rev/elcvia.1909
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 104-122 (Regular Issue)  
Registro completo - Creative Commons
2025-11-21
04:11
11 p, 1.3 MB Deep learning approach for the morphological differentiation of corn seed types / Setiawan, Doni Rizqi (Universitas Brawijaya (Indonèsia)) ; Prakasa, Esa (National Research and Innovation Agency (Indonèsia)) ; Riza, Dimas Firmanda Al (University of Brawijaya (Indonèsia)) ; Sumarlan, Sumardi Hadi (University of Brawijaya (Indonèsia)) ; Aqil, Muhammad (National Research and Innovation Agency (Indonèsia))
Corn is one of Indonesia's main food ingredients that contains the second largest source of carbohydrates after rice. Classification of the type and quality of corn seeds is still conducted manually by farmers. [...]
2025 - 10.5565/rev/elcvia.2193
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 70-80 (Regular Issue)  
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2025-11-21
04:11
21 p, 2.4 MB Improving slow-moving object detection in complex environments using a feature pooling enhanced encoder-decoder model : EDM-SMOD / Panigrahi, Upasana (C V Raman Global University (Índia)) ; Prabodh Kumar Sahoo (Parul University (Índia)) ; Kumar Panda, Manoj (GIET University (Índia)) ; Panda, Ganapati (C V Raman Global University (Índia))
The ability to detect moving objects is of great importance in a wide range of visual surveillance systems, playing a vital role in maintaining security and ensuring effective monitoring. However, the primary aim of such systems is to detect objects in motion and tackle real-world challenges effectively. [...]
2025 - 10.5565/rev/elcvia.2023
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 49-69 (Regular Issue)  
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2025-10-19
03:15
21 p, 8.6 MB DAE-MLP based feature extraction for hyperspectral image classification of Saint Clair river / Attallah, Youcef (University of Science and Technology of Oran (Algèria)) ; Zigh, Ehlem (University of Science and Technology of Oran (Algèria)) ; Adda, Ali Pacha (University of Science and Technology of Oran (Algèria))
Hyperspectral remote sensing has emerged as a powerful tool for vegetation classification due to its ability to capture detailed spectral information. This study introduces a novel methodology for vegetation classification using exclusively hyperspectral imagery. [...]
2025 - 10.5565/rev/elcvia.1827
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 28-48 (Regular Issue)  
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2025-10-19
03:15
27 p, 799.2 KB State-of-the-art DNN techniques for lung cancer diagnosis using chest CT scans : a review / Sakshiwala (National Institute of Technology Patna (Índia)) ; Singh, Maheshwari Prasad (National Institute of Technology Patna (Índia))
This paper reviews state-of-the-art literature on the early diagnosis of lung cancer with deep neural network techniques and chest CT scans. First, a brief introduction to the significance of lung cancer and the need for this review is stated. [...]
2025 - 10.5565/rev/elcvia.1597
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 1-27 (Regular Issue)  
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2025-09-01
10:08
16 p, 1.6 MB Enhanced bell pepper and grape leaf disease classification using a depthwise separable VGG19-capsule network / Mathew, Midhun P (n University of Science and Technology (Índia)) ; Abubeker, K M ; Elayidom, Sudheep (Cochin University of Science and Technology (Índia)) ; Raj, V.P. Jagathy (Amal Jyothi College of Engineerin (Índia))
Crop disease is a significant problem in the agricultural sector, leading to decreased food production and causing substantial economic losses for farmers in farming regions. Nowadays, computer vision and deep learning models can detect and diagnose leaf diseases in their early stages, which may assist farmers and contribute to ensuring food security. [...]
2025 - 10.5565/rev/elcvia.1964
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 179-194 (Regular Issue)  
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2025-09-01
10:07
26 p, 9.6 MB A computational approach to color vision enhancement using deep learning, TensorFlow and Keras / Pendhari, Nazneen (University of Mumbai (Índia)) ; Shaikh, Danish (University of Mumbai (Índia)) ; Shaikh, Nida (University of Mumbai (Índia)) ; Nagori, Abdul Gaffar (University of Mumbai (Índia)) ; Rathod, Kiran (K.J Somaiya Institute of Technology (Índia))
Individuals afflicted with color vision deficiency (CVD) often face obstacles in effectively navigating and engaging with their surroundings due to challenges in accurately discerning colors. Such limitations can hinder a range of daily activities, compelling these individuals to rely on external assistance for color-centric tasks, potentially curtailing their autonomy and inclusiveness. [...]
2025 - 10.5565/rev/elcvia.1892
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 153-179 (Regular Issue)  
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2025-05-22
05:57
18 p, 2.4 MB Deep learning-based video anomaly detection using optimised attention-enhanced autoencoders / Anjali, S. (Amrita Vishwa Vidyapeetham) ; Don, S. (Amrita Vishwa Vidyapeetham)
Anomaly detection in video is essential for applications like surveillance, healthcare, and industrial monitoring. Through the reconstruction of normal patterns and the computation of reconstruction error in relation to ground truth, convolutional autoencoders detect anomalies. [...]
2025 - 10.5565/rev/elcvia.2043
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 134-152 (Regular Issue)  
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2025-05-22
05:57
14 p, 961.8 KB Implementation of explainable Ai in deep learning methods for multiclass classification of plant diseases in mango lLeaves / Radhakrishnan, Menaka (Vellore Institute of Technology (Índia)) ; Monish, Neerajaksha (Vellore Institute of Technology (Índia)) ; Dev, Parimi Siva (Vellore Institute of Technology (Índia)) ; Kesavan, Neha ; Thomas, Nora Sara (Vellore Institute of Technology (Índia))
Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. [...]
2025 - 10.5565/rev/elcvia.2009
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 104-117 (Regular Issue)  
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2025-05-22
05:56
18 p, 1.6 MB Enhanced bird species image recognition and classification using MobileNet and InceptionV3 transfer learning architectures / G, Sakthi Priya (Ramco Institute of Technology (Índia)) ; K, Vignesh Saravanan (Ramco Institute of Technology (Índia)) ; K, Dheetchana (Ramco Institute of Technology (Índia))
The proposed study explores the application of transfer learning techniques in bird species image classification, specifically focusing on the MobileNet and InceptionV3 models. Utilizing the CUB-200-2011 dataset, the transfer learning is employed to enhance classification accuracy. [...]
2025 - 10.5565/rev/elcvia.2020
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 118-133 (Regular Issue)  
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