Tongue Image Identification System on Congestion of Fungiform Papillae (CFP)

2010 ◽  
pp. 73-82 ◽  
Author(s):  
Bo Huang ◽  
Naimin Li
Keyword(s):  
Identification System ◽  
Fungiform Papillae ◽  
Image Identification

Validating an automated image identification process of a passive image-assisted dietary assessment method: proof of concept

2020 ◽  
Vol 23 (15) ◽  
pp. 2700-2710
Author(s):  
Tsz-Kiu Chui ◽  
Jindong Tan ◽  
Yan Li ◽  
Hollie A. Raynor
Keyword(s):  
Dietary Assessment ◽  
False Negative ◽  
Assessment Method ◽  
Identification Accuracy ◽  
Identification System ◽  
True Negative ◽  
Image Identification ◽  
Effect Of Food ◽  
Food Image ◽  
Food Images

AbstractObjective:To validate an automated food image identification system, DietCam, which has not been validated, in identifying foods with different shapes and complexities from passively taken digital images.Design:Participants wore Sony SmartEyeglass that automatically took three images per second, while two meals containing four foods, representing regular- (i.e., cookies) and irregular-shaped (i.e., chips) foods and single (i.e., grapes) and complex (i.e., chicken and rice) foods, were consumed. Non-blurry images from the meals’ first 5 min were coded by human raters and compared with DietCam results. Comparisons produced four outcomes: true positive (rater/DietCam reports yes for food), false positive (rater reports no food; DietCam reports food), true negative (rater/DietCam reports no food) or false negative (rater reports food; DietCam reports no food).Setting:Laboratory meal.Participants:Thirty men and women (25·1 ± 6·6 years, 22·7 ± 1·6 kg/m2, 46·7 % White).Results:Identification accuracy was 81·2 and 79·7 % in meals A and B, respectively (food and non-food images) and 78·7 and 77·5 % in meals A and B, respectively (food images only). For food images only, no effect of food shape or complexity was found. When different types of images, such as 100 % food in the image and on the plate, <100 % food in the image and on the plate and food not on the plate, were analysed separately, images with food on the plate had a slightly higher accuracy.Conclusions:DietCam shows promise in automated food image identification, and DietCam is most accurate when images show food on the plate.

Image identification system based on an optical broadcast neural network processor: erratum

2006 ◽  
Vol 45 (16) ◽  
pp. 3781
Author(s):  
Marta Ruiz-Llata ◽  
Horacio Lamela-Rivera
Keyword(s):  
Neural Network ◽  
Network Processor ◽  
Identification System ◽  
Image Identification

An adult image identification system employing image retrieval technique

2007 ◽  
Vol 28 (16) ◽  
pp. 2367-2374 ◽  
Author(s):  
Jau-Ling Shih ◽  
Chang-Hsing Lee ◽  
Chang-Shen Yang
Keyword(s):  
Image Retrieval ◽  
Identification System ◽  
Image Identification ◽  
Retrieval Technique

Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae)

2016 ◽  
Vol 73 (7) ◽  
pp. 1511-1528 ◽  
Author(s):  
Jiang-ning Wang ◽  
Xiao-lin Chen ◽  
Xin-wen Hou ◽  
Li-bing Zhou ◽  
Chao-Dong Zhu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Fruit Flies ◽  
Economic Importance ◽  
Identification System ◽  
Image Identification
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