An integration of minimum local feature representation methods to recognize large variation of foods-Reference-Cited by-同舟云学术

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An integration of minimum local feature representation methods to recognize large variation of foods

Published:2017 Issue: Volume: Page:
ISSN:0094-243X
Container-title:AIP Conference Proceedings
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Author:

Razali Mohd Norhisham bin,Manshor Noridayu,Halin Alfian Abdul,Mustapha Norwati,Yaakob Razali

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http://aip.scitation.org/doi/pdf/10.1063/1.5005456

Reference28 articles.

1. Distinctive Image Features from Scale-Invariant Keypoints

2. Towards Effective Image Classification Using Class-Specific Codebooks and Distinctive Local Features

3. Bay H, Tuytelaars T, Van Gool L. SURF: Speeded up robust features. Lecture Notes in Comput Science (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2006; 3951 LNCS: 404–417.

4. Csurka G, Dance C, Fan L, Willamowski J, Cedric Bray. Visual categorization with bag of keypoints. International Workshop in Statistical Learning on Computer Vision 2004: 1–22.

5. DietCam: Multi-view regular shape food recognition with a camera phone

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review;Advances in Nutrition;2022-11

2. Food Classification using Deep Learning Techniques;2022 2nd International Conference on Advances in Engineering Science and Technology (AEST);2022-10-24

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