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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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2026-04-10
15:12
17 p, 2.4 MB A hybrid feature fusion for retinal OCT image classification using traditional and deep learning methods / Yadav, Mithilesh Kumar Singh (Dr B R Ambedkar National Institute of Technology (Índia)) ; Singh, Nagendra Pratap (Dr B R Ambedkar National Institute of Technology (Índia))
Early and accurate detection of diabetic macular edema (DME) is essential to avoid permanent loss of vision. This paper introduces Fusion-WideNet, a new hybrid classification model that combines handcrafted and deep features for the analysis of retinal OCT images. [...]
2026 - 10.5565/rev/elcvia.2280
ELCVIA, Vol. 25, Num. 1 (2026) , p. 43-59 (Regular Issue)  
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2026-04-10
17:12
23 p, 16.6 MB Self-supervised multimodal 3-D garment reconstruction from a single consumer image for energy-efficient virtual try-on systems / Chekhmestruk, Roman (Vinnytsia National Technical University (Ucraïna)) ; Voitsekhovska, Olena (Vinnytsia National Technical University (Ucraïna))
Accurate 3-D reconstruction of garments from a single consumer-grade image remains a critical barrier to truly immersive and resource-aware virtual try-on systems. We introduce a self-supervised, multimodal pipeline that fuses visual tokens extracted by a Vision Transformer with textual garment descriptors to synthesise high-fidelity cloth geometry and texture while operating within the stringent power envelope of mobile neural-processing units (NPUs). [...]
2026 - 10.5565/rev/elcvia.2276
ELCVIA, Vol. 25, Num. 1 (2026) , p. 60-82 (Regular Issue)  
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2026-04-10
17:12
15 p, 21.3 MB Optimized detection of Urdu signatures in real-world images using YOLO v7 / Hussain, Muzammal (Government College University Faisalabad (Pakistan)) ; Rafiq, Muhammad Ahsan (Government College University Faisalabad (Pakistan))
Authentication plays an important role in managing security. Signature is one of the first broadly practiced method to authenticate an individual. However, existing research is solely based on the English signature detection and recognition with limited work on low-resource languages. [...]
2026 - 10.5565/rev/elcvia.2267
ELCVIA, Vol. 25, Num. 1 (2026) , p. 83-97 (Regular Issue)  
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2026-04-10
18:12
21 p, 6.0 MB Enhanced underwater fish image processing by noise elimination, saturation adjustment, and edge sharpening technique / Khan, Samra Urooj (University Malaysia Pahang Al-Sultan Abdullah (Malàisia)) ; Faisal, Sundas (Silesian University of Technology) ; Ghazali, Kamarul Hawari (University Malaysia Pahang Al-Sultan Abdullah (Malàisia))
Analyzing underwater images and improving their quality is a difficult task for researchers. Processing underwater images is extremely difficult because of the low contrast, noise, and blurriness brought on by light scattering and absorption. [...]
2026 - 10.5565/rev/elcvia.2285
ELCVIA, Vol. 25, Num. 1 (2026) , p. 42-62 (Regular Issue)  
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2026-04-10
18:12
21 p, 2.8 MB Optimized deep learning architecture for melanoma detection : leveraging ResNet50V2.5 in dermatological imaging / Subaida Beevi, Shafeena (Noorul Islam Centre for Higher Education (Índia)) ; R. S., Vinod Kumar (Noorul Islam Centre for Higher Education (Índia)) ; D., Shahi (Noorul Islam Centre for Higher Education (Índia)) ; S. S., Kumar (Noorul Islam Centre for Higher Education (Índia))
Melanoma is a highly aggressive form of skin cancer that greatly impacts the global mortality rate related to skin cancer. Accurate identification and precise assessment of illness severity are essential for improving patient outcomes. [...]
2026 - 10.5565/rev/elcvia.2271
ELCVIA, Vol. 25, Num. 1 (2026) , p. 22-42 (Regular Issue)  
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2026-04-08
14:12
18 p, 4.5 MB Thermal image super-resolution : trends and challenges / Rivadeneira, Rafael E. (Escuela Superior Politécnica del Litoral (Ecuador)) ; Sappa, Angel (Centre de Visió per Computador) ; Hammoud, Riad
Thermal Image Super-Resolution has become pivotal for security, autonomous driving, industrial inspection, and surveillance. This work consolidates six editions of the TISR challenge held within the Perception Beyond the Visible Spectrum workshop at CVPR from 2020 to 2025, detailing the evolution of tasks, datasets, evaluation protocols, and participation. [...]
2025 - 10.5565/rev/elcvia.2374
ELCVIA, Vol. 24, Num. 2 (2025) , p. 355-372 (Regular Issue)  
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2026-04-10
18:12
21 p, 6.1 MB Preserving the heritage : Evaluating the character segmentation quality in palm leaf manuscripts by comparing the classical and Noise2Void denoising techniques / Unnikrishnan, Deepa (Presidency University (India)) ; Radhakrishnan, Vignesh (Presidency University (India))
The Palm Leaf Manuscripts are a rich source of information about ancient India. It shares an enormous amount of knowledge about the past in terms of art, culture, literature and medicine. As the Manuscripts were developed organically, it is prone to getting damaged very fast. [...]
2026 - 10.5565/rev/elcvia.2320
ELCVIA, Vol. 25, Num. 1 (2026) , p. 1-21 (Regular Issue)  
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2026-04-08
17:12
17 p, 1.9 MB Multi-biased models for hyperspectral anomaly detection : a paradigm to improve performance and generalizability / Wheeler, Bradley (University of Pittsburgh) ; Karimi, Hassan (University of Pittsburgh)
Hyperspectral anomaly detection (HAD) poses a significant challenge as it requires modeling data with hundreds of measurements for each location in space. Many algorithms have been proposed to address problems in HAD, but most originate from one of several biases assumed of the data. [...]
2025 - 10.5565/rev/elcvia.2318
ELCVIA, Vol. 24, Num. 2 (2025) , p. 338-354 (Regular Issue)  
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2026-04-08
17:12
26 p, 18.1 MB PFM-TurtleNet : sea turtle species identification using a parallel fusion module / Prasetya, Hebron (Sam Ratulangi University (Indonèsia)) ; Nguyen, Duy-Linh (University of Ulsan (Corea)) ; Oktavia Pantouw, Shara (Sam Ratulangi University (Indonèsia)) ; Kutika, Imanuel (Sam Ratulangi University (Indonèsia)) ; Diane Kambey, Feisy (Sam Ratulangi University (Indonèsia)) ; Dwisnanto Putro, Muhamad (Sam Ratulangi University (Indonèsia))
Sea turtle species identification is vital for marine biodiversity conservation, as sea turtles impact marine ecosystem balance by consuming dead seagrass and maintaining coral reefs. They help preserve the health of seagrass beds and coral reefs that benefit commercially valuable species. [...]
2025 - 10.5565/rev/elcvia.2297
ELCVIA, Vol. 24, Num. 2 (2025) , p. 312-337 (Regular Issue)  
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2026-04-08
17:12
27 p, 4.9 MB Binary Feature Map-Splitting Architecture (BFMSA): a computationally efficient approach for plant leaf disease classification / Tabbahk, Amer (VIT-AP University (Índia)) ; Sankar Barpanda, Soubhagya (VIT-AP University (Índia))
This paper presents a novel approach of reconstructing topology of a deep learning model to reduce model's trainable parameters, called Binary Feature Map-Splitting Architecture (BFMSA). The proposed approach is trained using the PlantVillage dataset for plant disease classification. [...]
2025 - 10.5565/rev/elcvia.2217
ELCVIA, Vol. 24, Num. 2 (2025) , p. 285-311 (Regular Issue)  
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