Food object recognition using a mobile device: Evaluation of currently implemented systems
| Autor: | Luka Šajn, Simon Knez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
business.industry Computer science 010401 analytical chemistry Feature extraction Cognitive neuroscience of visual object recognition Image processing 02 engineering and technology Image segmentation Machine learning computer.software_genre 01 natural sciences Field (computer science) 0104 chemical sciences 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Mobile device computer Food Science Biotechnology |
| Zdroj: | Trends in Food Science & Technology. 99:460-471 |
| ISSN: | 0924-2244 |
| DOI: | 10.1016/j.tifs.2020.03.017 |
| Popis: | Background Food object recognition systems present an attractive and useful research field since they enable objective measurements of eating activity. This feature is helpful and welcome in many dieting related instances, especially for managing health conditions or for analyzing eating patterns of research subjects. Scope and approach We evaluate current food object recognition systems that were implemented on a mobile device. The evaluation was provided by analysing each particular system through its food recognition process. The whole recognition process was divided into 6 distinct stages: image acquisition, image processing, image segmentation, feature extraction, image classification, and volume estimation. Key findings and conclusions Through the analysis, the authors provide a categorization of mobile food recognition systems: recorder systems, suggester systems, and clinical responders. Each group is aimed at a different scenario which helps to identify features a particular system should focus its development on. |
| Databáze: | OpenAIRE |
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