Semantic segmentation

dc.contributor.author	Mammeri, Oumeima
dc.contributor.author	Medkour, Romaissa
dc.contributor.author	Chibani, Meriem
dc.date.accessioned	2022-11-15T01:06:52Z
dc.date.available	2022-11-15T01:06:52Z
dc.date.issued	2022
dc.description.abstract	Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. It is a building block for scene understanding by classifying all pixels of an image in a dense way; it is then possible to build abstract representations focusing on the objects and their shapes. In this project, our work exploits a convolutional neural network (CNN) architecture "unet" for semantic segmentation; the choice of this architecture is due to its phenomenal success, it can solve the most complex problems in deep learning. It is a particularly effective tool for many types of images. Our model is trained on the Oxford-iiit pet dataset.	ar
dc.identifier.uri	http://hdl.handle.net/123456789/14306
dc.language.iso	en	ar
dc.publisher	Université Larbi Ben M'hidi Oum El Bouaghi	ar
dc.subject	Computer vision	ar
dc.subject	Deep learning	ar
dc.subject	Semantic segmentation	ar
dc.subject	Unet	ar
dc.title	Semantic segmentation	ar
dc.type	Other	ar

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