Abstract
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.
Highlights
Semantic segmentation [1] is the process of alloting class labels to each pixel in an image
Semantic image segmentation [1] is an immense area of interest for computer vision, machine learning [2], and deep learning [3] researchers with many challenges. It has a wide array of practical applications like remote sensing, autonomous driving, indoor navigation, video surveillance and virtual or augmented reality systems etc
We have demonstrated the concept of combining different pre-trained classification models and segmentations models for the semantic image segmentation
Summary
Semantic segmentation [1] is the process of alloting class labels to each pixel in an image. Pixel-wise labels provides us better descriptions of images than bounding box labels. Concluding such labels is a much more challenging task because it involves extremely complex structured prediction problem. Semantic image segmentation [1] (pixellevel classification) is an immense area of interest for computer vision, machine learning [2], and deep learning [3] researchers with many challenges. It has a wide array of practical applications like remote sensing, autonomous driving, indoor navigation, video surveillance and virtual or augmented reality systems etc. Nowadays Deep Learning techniques [4] provide state-of-the-art performance for image segmentation and classification as well as for detection tasks and captioning using Convolutional Neural Network models and have been mainly accelerating the recent breakthroughs in semantic segmentation using different combinations of CNN models such as VGGNet [5], AlexNet [6], and ResNet [7]
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