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A New Objective Supervised Edge Detection Assessment Using Hysteresis Thresholds Read chapter Read first chapter

2017 | OriginalPaper | Chapter

On Comparing Color Spaces for Food Segmentation

Authors : Sinem Aslan, Gianluigi Ciocca, Raimondo Schettini

Published in: New Trends in Image Analysis and Processing – ICIAP 2017

Publisher: Springer International Publishing

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Abstract

Accurate segmentation of food regions is important for both food recognition and quantity estimation and any error would degrade the accuracy of the food dietary assessment system. Main goal of this work is to investigate the performance of a number of color encoding schemes and color spaces for food segmentation exploiting the JSEG algorithm. Our main outcome is that significant improvements in segmentation can be achieved with a proper color space selection and by learning the proper setting of the segmentation parameters from a training set.

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previous chapter Learning CNN-based Features for Retrieval of Food Images
next chapter Pocket Dietitian: Automated Healthy Dish Recommendations by Location
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Metadata
Title
On Comparing Color Spaces for Food Segmentation
Authors
Sinem Aslan
Gianluigi Ciocca
Raimondo Schettini
Copyright Year
2017
Book
New Trends in Image Analysis and Processing – ICIAP 2017

Print ISBN: 978-3-319-70741-9

Electronic ISBN: 978-3-319-70742-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-70742-6_42

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