<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the process of classifying each pixel in respective category. In this paper, we exploit the problem of scene understanding and perform the segmentation by combining different classification models as a feature encoder and segmentation models as a feature decoder. All of the experiments were performed on Camvid dataset. It covers a wide range of real-world applications such as autonomous driving, virtual/augmented reality, indoor navigation, etc.</p>
</abstract>

Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the process of classifying each pixel in respective category. In this paper, we exploit the problem of scene understanding and perform the segmentation by combining different classification models as a feature encoder and segmentation models as a feature decoder. All of the experiments were performed on Camvid dataset. It covers a wide range of real-world applications such as autonomous driving, virtual/augmented reality, indoor navigation, etc.

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks"}

Semantic image segmentation is a vast area of interest for computer vision which has gained exceptional attention from the research community. It is the...

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Pubblicato online: 22 feb 2021

Pagine: 9 - 17

DOI: https://doi.org/10.21307/ijanmc-2021-022

Parole chiave
Semantic Segmentation, Computer Vision, Scene Understanding, Classification Model, Segmentation Model

© 2021 Muhammad Adeel Ahmed Tahir et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Pubblicato online: 22 feb 2021

Pagine: 9 - 17

DOI: https://doi.org/10.21307/ijanmc-2021-022

Parole chiaveSemantic Segmentation, Computer Vision, Scene Understanding, Classification Model, Segmentation Model

© 2021 Muhammad Adeel Ahmed Tahir et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Parole chiave
Semantic Segmentation, Computer Vision, Scene Understanding, Classification Model, Segmentation Model