scholarly journals Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System: A Validation Study Using a Smartphone Application (CALO mama) (Preprint)

10.2196/31875 ◽  
2021 ◽  
Author(s):  
Yuki Sasaki ◽  
Koryu Sato ◽  
Satomi Kobayashi ◽  
Keiko Asakura
Keyword(s):  
Image Recognition ◽  
Validation Study ◽  
Smartphone Application ◽  
Recognition System ◽  
Food Group ◽  
System A ◽  
Automated Image Recognition

Validity of nutrient and food group contents estimated automatically by an image recognition system: A smartphone application for health promotion (Preprint)

2021 ◽  
Author(s):  
Yuki Sasaki ◽  
Koryu Sato ◽  
Satomi Kobayashi ◽  
Keiko Asakura
Keyword(s):  
Food Intake ◽  
Image Recognition ◽  
Smartphone Application ◽  
Recognition System ◽  
Food Group ◽  
Automatic Identification ◽  
Dietary Intakes ◽  
Food Groups ◽  
System A ◽  
Nutritional Recommendations

BACKGROUND A smartphone image recognition application is expected to be a novel tool to measure nutrients and food intake, but its performance has not been well evaluated. OBJECTIVE We assessed the performance of an image recognition application called CALO mama in terms of the nutrient and food group contents automatically estimated by the application. METHODS We prepared 120 sample meals for which the nutrients and food groups were already calculated. Next, we predicted the nutrients and food groups included in the meals from their photographs using 1) automated image recognition only and 2) manual modification after automatic identification. RESULTS Predictions using only image recognition were similar to the actual data in weight of meals, 11 out of 30 nutrients, and 4 out of 15 food groups; it underestimated energy, 19 nutrients, and 9 food groups; it overestimated dairy products and confectioneries. After manual modification, predictions were similar in energy, 29 out of 30 nutrients, and 10 out of 15 food groups; it underestimated pulses, fruits, and meats; it overestimated weight, vitamin C, vegetables, and confectioneries. CONCLUSIONS The results of this study suggest that manual modification after prediction using image recognition improves the performance of the assessment of nutrients and food intake. Our findings suggest the potential of image recognition to achieve a description of the dietary intakes of populations using “precision nutrition” (a comprehensive and dynamic approach to develop tailored nutritional recommendations) for individuals.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Face Mask Recognition System with YOLOV5 Based on Image Recognition

2020 ◽  
Author(s):  
Guanhao Yang ◽  
Wei Feng ◽  
Jintao Jin ◽  
Qujiang Lei ◽  
Xiuhao Li ◽  
...  
Keyword(s):  
Image Recognition ◽  
Face Mask ◽  
Recognition System

Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy

2016 ◽  
Vol 83 (3) ◽  
pp. 643-649 ◽  
Author(s):  
Yoko Kominami ◽  
Shigeto Yoshida ◽  
Shinji Tanaka ◽  
Yoji Sanomura ◽  
Tsubasa Hirakawa ◽  
...  
Keyword(s):  
Image Recognition ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Recognition System ◽  
Colorectal Polyp ◽  
Magnifying Colonoscopy ◽  
Computer Aided ◽  
Real Time Image ◽  
Time Image ◽  
Aided Diagnosis

Design and Implementation of Contour Tracing Image Recognition System

2014 ◽  
Vol 543-547 ◽  
pp. 2209-2212
Author(s):  
Chun Hua Xiong ◽  
You Jie Zhou ◽  
Gao Jun An ◽  
Chang Bo Lu
Keyword(s):  
Embedded System ◽  
Image Recognition ◽  
Integrated Design ◽  
Recognition System ◽  
Image System ◽  
Intelligent Monitoring ◽  
Contour Tracing ◽  
The Embedded System ◽  
Imaging Sensor ◽  
Very High

Based on the existing contour tracing image recognition technology, combining the embedded system technology and the computer storage control technology, the author makes an integrated design, adopts the image processing chip, USB controller, the imaging sensor and other hardware circuits and develops an intelligent image system. The system can make real-time monitoring the size and change of millimeter-sized irregular target objects. Its applicable value in the fields such as intelligent monitoring of oil equipment, medical imaging and criminal investigation is very high.

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