Object recognition and semantic segmentation have been the two most common problems of traditional scene understanding in the computer vision domain. Major breakthroughs were reported in the last few years because of the increased utilization of deep learning, which offer a convincing alternative by learning the problem specific features on their own. In this paper, a summary of the frequently used framework – convolutional neural networks (CNN) is discussed. Accordingly a categorization scheme has been proposed to analyze the deep networks developed for image segmentation. Under this scheme, thirteen methods from the literature have been reviewed which are classified on the basis on how they perform segmentation operation i.e. semantic segmentation, instance segmentation and hybrid approaches. These method were reviewed from different aspects like their category, the novelty in the architecture of the method, and their special features in contrast with the traditional approaches. Latest review and analysis of these segmentation approaches, which provided outstanding results for image segmentation compared to the ordinary system, reveals that deep learning is increasingly becoming an important part of image segmentation and improvement in deep learning algorithms, which could resolve computer vision problems.
Hina Ajmal, Saad Rehman, Umar Farooq, Qurrat U. Ain, Farhan Riaz, and Ali Hassan, "Convolutional neural network based image segmentation: a review," Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490N (Presented at SPIE Defense + Security: April 19, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2304711.
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