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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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2022-05-21
06:58
11 p, 3.2 MB Object Detection and Statistical Analysis of Microscopy Image Sequences / Gambini, Juliana (Instituto Tecnológico de Buenos Aires, Departamento de Ingeniería Informática) ; Hurovitz, Sasha (Instituto Tecnológico de Buenos Aires, Departamento de Ingeniería Informática) ; Chan, Debora (Universidad Tecnológica Nacional, Departamento de Matemática) ; Ramele, Rodrigo (Instituto Tecnológico de Buenos Aires, Departamento de Ingeniería Informática)
Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resolution. [...]
2022 - 10.5565/rev/elcvia.1482
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 1 (2022) , p. 47-58 (Regular Issue)  
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2022-05-14
07:00
16 p, 12.3 MB Pre-trained CNNs as Feature-Extraction Modules for Image Captioning : An Experimental Study / Al-Malla, Muhammad Abdelhadie (Higher Institute of Applied Science and Technology) ; Jafar, Assef (Higher Institute for Applied Sciences and Technology (HIAST)) ; Ghneim, Nada (Arab International University)
In this work, we present a thorough experimental study about feature extraction using Convolutional Neural Networks (CNNs) for the task of image captioning in the context of deep learning. We perform a set of 72 experiments on 12 image classification CNNs pre-trained on the ImageNet [29] dataset. [...]
2022 - 10.5565/rev/elcvia.1436
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 1 (2022) , p. 1-16 (Regular Issue)  
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2022-05-02
06:55
19 p, 1.3 MB Retinal Blood Vessels Segmentation using Fréchet PDF and MSMO Method / Kumar Saroj, Sushil (MMMUT gorakhpur) ; Kumar, Rakesh (MMMUT gorakhpur) ; Singh, Nagendra Pratap (NIT, Hamirpur)
Blood vessels of retina contain information about many severe diseases like glaucoma, hypertension, obesity, diabetes etc. Health professionals use this information to detect and diagnose these diseases. [...]
2022 - 10.5565/rev/elcvia.1453
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 21 Núm. 1 (2022) , p. 27-46 (Regular Issue)  
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2022-04-30
07:01
11 p, 498.2 KB Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images / Christinal, Hepzibah A. (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India) ; Kowsalya, G. (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India) ; Chandy D., Abraham (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India) ; Jebasingh, Stephenraj (Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India) ; Bajaj, Chandrajit (University of Texas)
Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. [...]
2021 - 10.5565/rev/elcvia.1412
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 102-113 (Regular Issue)  
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2022-04-29
07:02
18 p, 2.9 MB A Multi-staged Feature-Attentive Network for Fashion Clothing Classification and Attribute Prediction / Shajini, Majuran (University of Jaffna) ; Ramanan, Amirthalingam (University of Jaffna)
In the visual fashion clothing analysis, many researchers are attracted with the success of deep learning concepts. In this work, we introduce a multi-staged feature-attentive network to attain clothing category classification and attribute prediction. [...]
2021 - 10.5565/rev/elcvia.1409
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 83-100 (Regular Issue)  
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2022-04-28
07:02
18 p, 498.2 KB An Efficient BoF Representation for Object Classification / Vinoharan, Veerapathirapillai (University of Jaffna) ; Ramanan, Amirthalingam (University of Jaffna, Department of Computer Science)
The Bag-of-features (BoF) approach has proved to yield better performance in a patch-based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as scale-invariant feature transform and speeded up robust features, extracted from images to create a BoF vector) leads to huge computational cost and an increased storage requirement. [...]
2021 - 10.5565/rev/elcvia.1403
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 51-68 (Regular Issue)  
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2022-04-27
07:03
31 p, 6.0 MB Application of computer vision to egg detection on a production line in real time / Ulaszewski, Maciej (Warsaw School of Computer Science) ; Janowski, Robert (Warsaw School of Computer Science) ; Janowski, Andrzej
In this paper we investigate the application of computer vision to the problem of egg detection on a production line in real-time. For this purpose a dedicated software was designed and implemented that exploited the advantages of neural networks or template matching approaches. [...]
2021 - 10.5565/rev/elcvia.1390
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 113-143 (Regular Issue)  
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2022-04-26
07:08
13 p, 11.4 MB Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions / Ravisankar, Priyadharsini (Sri Sivasubramanya Nadar College of Engineering)
Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. [...]
2021 - 10.5565/rev/elcvia.1360
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 38-50 (Regular Issue)  
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2022-04-25
07:03
14 p, 753.8 KB Deep Learning Based Models for Offline Gurmukhi Handwritten Character and Numeral Recognition / Mahto, Manoj Kumar (Gurukula Kangri Vishwavidyalaya, Haridwar, U.K., India) ; Bhatia, Karamjit (Gurukula Kangri Vishwavidyalaya, Haridwar, U.K., India) ; Sharma, Rajendra Kumar (Thapar Institute of Engineering & Technology, Patiala, Punjab, India)
Over the last few years, several researchers have worked on handwritten character recognition and have proposed various techniques to improve the performance of Indic and non-Indic scripts recognition. [...]
2021 - 10.5565/rev/elcvia.1282
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 69-82 (Regular Issue)  
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2021-08-28
05:24
15 p, 1.2 MB Accuracy improvement of the inSAR quality-guided phase unwrapping based on a modified PDV map / Bentahar, Tarek (Larbi Tebessi University. Laboratory of electrical engineering-telecommunications-LABGET)
In this paper, an accuracy improvement of the quality-guided phase unwrapping algorithm is proposed. Our proposal is based on a modified phase derivative variance which provides more details on local variations especially for important patterns such as fringes and edges, hence distorted regions may be re-unwrapped according to this new reliable PDV. [...]
2021 - 10.5565/rev/elcvia.1220
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 20 Núm. 2 (2021) , p. 22-37 (Regular Issue)  
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