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ELCVIA : Electronic Letters on Computer Vision and Image Analysis
ELCVIA (ISSN 1577-5097 E) is an exclusively electronic journal known internationally for research and applications of computer vision and image analysis. Its editorial board consists of internationally renowned experts. All articles are peer reviewed by peer-review and currently has an acceptance rate of 25% of the items received. The main objectives of the magazine are:
    Being a journal of international reference in the field of computer vision. To be a quality publication. Being a media fast and dynamic, with a quick review of the articles. Being Index impact within the SC database.
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2025-02-26
08:57
20 p, 1.0 MB Supervised deep learning approaches for anomaly detection and recognition in crowd scenes / Joshi, Kinjal (Charutar Vidya Mandal University (Índia)) ; Patel, Narendra (Birla Vishvakarma Mahavidyalaya (Índia))
These days consciousness about public safety increases and CCTV cameras are installed at almost all public places. But generally automatic smart surveillance systems are not available. In this manuscript, emphasis is given to detect and classify abnormal events in surveillance video especially in crowd environments. [...]
2025 - 10.5565/rev/elcvia.2412025
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 31-50 (Regular Issue)  
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2025-02-12
15:56
18 p, 571.9 KB A novel framework based on deep neural network fordetermining the melting point of crystalline chemical substances / Shrivastava, Anurag (Bennett University (Índia)) ; Shrivastava, Bhavana (DIT University (Índia))
The melting point detection apparatus is a tool employed in the pharmaceutical and chemical industriesto ascertain the melting point of chemical substances. It plays a crucial role in assessing the quality andclassification of these substances by identifying their purity or impurities. [...]
2024 - 10.5565/rev/elcvia.1527
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 47-67 (Regular Issue)  
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2025-01-29
06:39
30 p, 10.3 MB A panoptic segmentation for indoor environments using MaskDINO : an experiment on the impact of contrast / Putri, Khalisha (Universitas Gadjah Mada (Indonèsia)) ; Candradewi, Ika (Universitas Gadjah Mada (Indonèsia))
Robot perception involves recognizing the surrounding environment, particularly in indoor spaces like kitchens, classrooms, and dining areas. This recognition is crucial for tasks such as object identification. [...]
2025 - 10.5565/rev/elcvia.1861
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 1-29 (Regular Issue)  
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2024-12-22
01:51
20 p, 726.6 KB A study on CNN-based and handcrafted extraction methods with machine learning for automated classification of breast tumors from ultrasound images / Benaouali, Mohamed (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ; Bentoumi, Mohamed (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ; Abed, Mansour (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ; Mimi, Malika (University Abdel Hamid Ibn Badis of Mostaganem (Algèria)) ; Taleb-Ahmed , Abdelmalik (IEMN DOAE UMR CNRS 8520, Universite Polytechnique des Hauts de France, Valenciennes, France)
In this paper, we present an efficient procedure for automatically classifying ultrasound images of benign and malignant breast tumors. We evaluated our approach using four openly available datasets and investigated two categories of feature extraction methods: handcrafted methods (Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG)) and methods based on convolutional neural network (CNN) models. [...]
2024 - 10.5565/rev/elcvia.1887
ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 85-104 (Regular Issue)  
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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.2322024
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)) ; Khazri Ayeb, Kawther (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, 571.9 KB Classification of radiological patterns of tuberculosis with a Convolutional neural network in x-ray images / Trueba Espinosa, Adrian (Universidad Autónoma del Estado de México) ; Sanchez -Arrazola, Jessica (Universidad Autónoma del Estado de México) ; Cervantes, Jair (Universidad Autónoma del Estado de México) ; Garcia-Lamont, Farid (Universidad Autónoma del Estado de México) ; Ruiz Castilla, José Sergio (Universidad Autónoma del Estado de México) ; Kantipudi, Karthik (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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