Extremal region detection and selection with fuzzy encoding for food recognition
This study proposes the improvement of feature representation by using Maximally Stable Extremal Region (MSER) detector in Bag of Features (BoF) model which incorporates an interest points detection and selection, and fuzzy encoding for food recognition. Three algorithms were used to accomplish t...
Saved in:
Main Author: | Razali @ Ghazali, Mohd Norhisham |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://psasir.upm.edu.my/id/eprint/84594/1/FSKTM%202019%2048%20ir.pdf http://psasir.upm.edu.my/id/eprint/84594/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Extremal Region Selection for MSER Detection in Food Recognition
by: Mohd Norhisham Razali @ Ghazali, et al.
Published: (2021) -
Extremal region selection for MSER detection in food recognition
by: Razali, Mohd Norhisham, et al.
Published: (2021) -
Fuzzy encoding with hybrid pooling for visual dictionary in food recognition
by: Razali, Mohd Norhisham, et al.
Published: (2021) -
Fuzzy encoding with hybrid pooling for visual dictionary in food recognition
by: Mohd Norhisham Razali, et al.
Published: (2021) -
Improving invisible food texture detection by using adaptive extremal region detector in food recognition
by: Razali, Mohd Norhisham, et al.
Published: (2019)