SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

Tahir, Muhammad Adeel Ahmed; Feng, Xiao; Shaker, Zaryab

doi:10.21307/ijanmc-2021-022

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.

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SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks

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