scholarly journals Saliency-Aware Class-Agnostic Food Image Segmentation

2021 ◽  
Vol 2 (3) ◽  
pp. 1-17
Author(s):  
Sri Kalyan Yarlagadda ◽  
Daniel Mas Montserrat ◽  
David Güera ◽  
Carol J. Boushey ◽  
Deborah A. Kerr ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Prior Information ◽  
Dietary Assessment ◽  
Segmentation Method ◽  
Dietary Assessment Methods ◽  
Before And After ◽  
Dietary Study ◽  
Food Image ◽  
Food Images ◽  
Food Consumed

Advances in image-based dietary assessment methods have allowed nutrition professionals and researchers to improve the accuracy of dietary assessment, where images of food consumed are captured using smartphones or wearable devices. These images are then analyzed using computer vision methods to estimate energy and nutrition content of the foods. Food image segmentation, which determines the regions in an image where foods are located, plays an important role in this process. Current methods are data dependent and thus cannot generalize well for different food types. To address this problem, we propose a class-agnostic food image segmentation method. Our method uses a pair of eating scene images, one before starting eating and one after eating is completed. Using information from both the before and after eating images, we can segment food images by finding the salient missing objects without any prior information about the food class. We model a paradigm of top-down saliency that guides the attention of the human visual system based on a task to find the salient missing objects in a pair of images. Our method is validated on food images collected from a dietary study that showed promising results.

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.

scholarly journals Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study (Preprint)

2019 ◽  
Author(s):  
Stephanie Van Asbroeck ◽  
Christophe Matthys
Keyword(s):  
Image Recognition ◽  
Dietary Assessment ◽  
Application Programming Interface ◽  
Accurate Estimation ◽  
Performance Study ◽  
Comparative Performance ◽  
Dietary Assessment Methods ◽  
Food Quantity ◽  
Application Programming ◽  
Food Image

BACKGROUND In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. OBJECTIVE This is a comparative performance study of commercial image recognition platforms. METHODS A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. RESULTS Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. CONCLUSIONS Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods.

scholarly journals Image Segmentation for Review of Cerebral Apoplexy

2021 ◽  
Vol 38 (6) ◽  
pp. 1775-1782
Author(s):  
Na Jiang
Keyword(s):  
Feature Selection ◽  
Image Segmentation ◽  
Prior Information ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Multiple Lesion ◽  
Rehabilitation Process ◽  
Imaging Tool ◽  
Key Techniques ◽  
Selection Of

Brain computed tomography (CT) provides a medical imaging tool for reviewing cerebral apoplexy. It is of strong clinical significance to study the key techniques for lesion segmentation and feature selection of cerebral apoplexy. Most of the previous research fail to fully utilized the other prior information, or apply to the changing feature analysis on multiple lesion images generated in the rehabilitation process. Therefore, this paper aims to develop an image segmentation method for review of cerebral apoplexy. Based on the correlation between image series, the authors proposed a segmentation method for CT images of cerebral apoplexy, and developed a way to extract and select the changing lesion features, which assists with the diagnosis of cerebral apoplexy rehabilitation. The image segmentation and feature selection results were obtained through experiments, revealing the effectiveness of our method.

scholarly journals Prior-based Hierarchical Segmentation Highlighting Structures of Interest

2019 ◽  
Vol 3 (1) ◽  
pp. 29-44
Author(s):  
Amin Fehri ◽  
Santiago Velasco-Forero ◽  
Fernand Meyer
Keyword(s):  
Image Segmentation ◽  
Prior Information ◽  
Spatial Information ◽  
Regions Of Interest ◽  
The Other ◽  
Segmentation Method ◽  
Hierarchical Segmentation ◽  
Other Hand ◽  
Major Trend

AbstractImage segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.

scholarly journals Saliency-aware food image segmentation for personal dietary assessment using a wearable computer

2015 ◽  
Vol 26 (2) ◽  
pp. 025702 ◽  
Author(s):  
Hsin-Chen Chen ◽  
Wenyan Jia ◽  
Xin Sun ◽  
Zhaoxin Li ◽  
Yuecheng Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Dietary Assessment ◽  
Wearable Computer ◽  
Food Image

scholarly journals Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study

10.2196/15602 ◽  
2020 ◽  
Vol 4 (12) ◽  
pp. e15602
Author(s):  
Stephanie Van Asbroeck ◽  
Christophe Matthys
Keyword(s):  
Image Recognition ◽  
Dietary Assessment ◽  
Application Programming Interface ◽  
Accurate Estimation ◽  
Performance Study ◽  
Comparative Performance ◽  
Dietary Assessment Methods ◽  
Food Quantity ◽  
Application Programming ◽  
Food Image

Background In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. Objective This is a comparative performance study of commercial image recognition platforms. Methods A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. Results Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. Conclusions Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Sign in / Sign up

Export Citation Format

Share Document