1. SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks
- Author
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Zaryab Shaker, Muhammad Adeel Ahmed Tahir, and Xiao Feng
- Subjects
Computer engineering. Computer hardware ,business.industry ,Computer science ,Computer Vision ,Segmentation Model ,Classification Model ,Scene Understanding ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,QA75.5-76.95 ,Convolutional neural network ,TK7885-7895 ,Electronic computers. Computer science ,Semantic image segmentation ,Semantic Segmentation ,Artificial intelligence ,business - 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.
- Published
- 2021
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