Resultats globals: 4 registres trobats en 0.01 segons.
Articles, 4 registres trobats
Articles 4 registres trobats  
1.
19 p, 1.6 MB Deep Learning-based Detection, Segmentation of Prostate Cancer from mp-MRI Images / Bouslimi, Yahya (University of Tunis) ; Ben Aïcha, Takwa (University of Tunis) ; Kacem Echi, Afef (University of Tunis)
Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. [...]
2023 - 10.5565/rev/elcvia.1620
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 22 Núm. 1 (2023) , p. 52-70 (Regular Issue)  
2.
23 p, 667.6 KB Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor / Saïdani, Asma (University of Tunis) ; Kacem Echi, Afef (University of Tunis) ; Belaïd, Abdel (University of Lorraine)
In this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. [...]
2015 - 10.5565/rev/elcvia.762
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 núm. 2 (2015) , p. 1-23 (Regular Issue)  
3.
16 p, 2.8 MB A PGM-based System for Arabic HandwrittenWord Recognition / Kacem Echi, Afef (University of Tunis) ; Khémiri, Akram (University of Tunis) ; Belaïd, Abdel (University of Lorraine)
This paper describes a system for off-line recognition of handwritten Arabic words. It uses simple andeasily extractable features to construct feature vectors for words in the vocabulary. Some of these features are statistical, based on pixel distributions and local pixel configurations. [...]
2014 - 10.5565/rev/elcvia.575
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13 Núm. 3 (2014) , p. 41-62 (Regular Issue)  
4.
17 p, 592.5 KB How to separate between Machine-Printed/Handwritten and Arabic/Latin Words? / Kacem Echi, Afef (University of Tunis) ; Saïdani, Asma (University of Tunis) ; Belaïd, Abdel (University of Lorraine)
This paper gathers some contributions to script and its nature identification. Different sets of features have been employed successfully for discriminating between handwritten and machine-printed Arabic and Latin scripts. [...]
2014 - 10.5565/rev/elcvia.572
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 1 (2014) , p. 1-16  

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