Smartphone-based Calorie Estimation From Food Image Using Distance Information

Sadeq, Nafis; Rahat, Fazle Rabbi; Rahman, Ashikur; Ahamed, Sheikh Iqbal; Hasan, Kamrul

doi:10.1109/nsyss.2018.8631368

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…For one food-item images, this method has achieved the highest food classification accuracy of 100% and for five food item images, this method has achieved an accuracy of 76%. Sadeq et al [87] have used K(=3)-means clustering for their food image segmentation. They have demonstrated that food segmentation using clustering decreases the standard error rate for some foods.…”

Section: A Food Image Segmentationmentioning

confidence: 99%

“…In some previous research studies [38], [82], scientists have utilized food shape templates for segmentation. This technique is not applicable to amorphous-shaped food [23], [87]. Researchers in [13] and [87] have used algorithms such as Canny edge detection to identify the edges of the food shapes for amorphous-shaped food segmentation.…”

Section: A Food Image Segmentationmentioning

confidence: 99%

“…This technique is not applicable to amorphous-shaped food [23], [87]. Researchers in [13] and [87] have used algorithms such as Canny edge detection to identify the edges of the food shapes for amorphous-shaped food segmentation. In recent times, researchers are utilizing DL frameworks such as Mask R-CNN, SegNet, CNN, DeepLabV3+, and so on for food segmentation but the issue of insufficient food annotation is still prevalent [27].…”

Section: A Food Image Segmentationmentioning

confidence: 99%

“…Some studies have used reference cards with known size and scale [23], [60] to reconstruct 3D food images from 2D images. Sadeq et al [87] use the user's forearm as reference length for food volume estimation of the images. This technique has achieved low standard error for some of the food classes.…”

Section: Reference Objectsmentioning

confidence: 99%

“…In real life, food images do not contain reference objects in the frames. Thus, the food volume estimation frameworks with reference objects [87], [124] will not have good performance in everyday life. Few works [5], [23] have used top and side views of meals for volume estimation.…”

Section: G Histogram Pre-trained 3d Model Gan Deep Cnnmentioning

confidence: 99%

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A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

et al. 2023

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Monitoring nutritional values in food can help an individual in planning a healthy diet. In addition, regular dietary assessment can improve and maintain the physical and mental health of individuals. Recent advancement in computer vision using Deep Learning has enabled researchers to develop various techniques for automatic food nutrition estimation frameworks. Researchers have also contributed to prepare large food image datasets consisting of various food classes for this purpose. However, automatic estimation of nutritional values from food images still remains a challenging task. This review paper critically analyzes and summarizes existing methodologies and datasets used for automated estimation of nutritional value from food images. We first define the taxonomies in order to categorize the existing research works. Then, we study different methods to detect the food value estimation from food images in those categories. We have critically analyzed existing methods and compared the performance of various approaches for estimating food value using conventional performance metrics such as Accuracy, Error Rate, Intersection over Union (IoU), Sensitivity, Specificity, Precision, etc. In particular, we emphasize the current trends and techniques of Deep Learning-based approaches for food value estimation from images. Moreover, we have identified the ongoing challenges associated with automated food estimation systems and outlined the potential future directions. This review can immensely benefit researchers and practitioners, including computer scientists, health practitioners, and nutritionists.

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Section: A Food Image Segmentationmentioning

confidence: 99%