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Validating Few Contemporary Approaches in Image Segmentation – A Quantitative Approach

  • Syed FasiuddinEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)

Abstract

In this paper, we present an extensive study and quantitative evaluation of six segmentation techniques on images of Berkeley Segmentation Database. Image segmentation plays a vital role in many computer vision applications and benchmarking such algorithms may assist research community in present and future research efforts in the field of image segmentation. Color space models, Hybrid color space and wavelet, Gradient Magnitude Techniques, K – means, C-Means & Fuzzy C-Means (FCM) and Edison’s Mean – shift approaches are evaluated using at least six metrics with respect to ground-truth boundaries of entire images in BSD 300/500 dataset images. The results stated here gives useful insights to above mentioned approaches and its significance in aligning upcoming research avenues in image segmentation.

Keywords

Computer vision Image segmentation Quantitative analysis Contemporary approaches 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Blackbuck Engineers Pvt LtdHyderabadIndia

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