A Comprehensive Review of Modern Object Segmentation Approaches
- URL: http://arxiv.org/abs/2301.07499v1
- Date: Fri, 13 Jan 2023 19:35:46 GMT
- Title: A Comprehensive Review of Modern Object Segmentation Approaches
- Authors: Yuanbo Wang, Unaiza Ahsan, Hanyan Li, Matthew Hagen
- Abstract summary: Image segmentation is the task of associating pixels in an image with their respective object class labels.
Deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding.
Extensions of image segmentation tasks include 3D and video segmentation, where units of vox point clouds, and video frames are classified into different objects.
- Score: 1.7041248235270654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image segmentation is the task of associating pixels in an image with their
respective object class labels. It has a wide range of applications in many
industries including healthcare, transportation, robotics, fashion, home
improvement, and tourism. Many deep learning-based approaches have been
developed for image-level object recognition and pixel-level scene
understanding-with the latter requiring a much denser annotation of scenes with
a large set of objects. Extensions of image segmentation tasks include 3D and
video segmentation, where units of voxels, point clouds, and video frames are
classified into different objects. We use "Object Segmentation" to refer to the
union of these segmentation tasks. In this monograph, we investigate both
traditional and modern object segmentation approaches, comparing their
strengths, weaknesses, and utilities. We examine in detail the wide range of
deep learning-based segmentation techniques developed in recent years, provide
a review of the widely used datasets and evaluation metrics, and discuss
potential future research directions.
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