We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve ...this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier's confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback.
Obtaining accurate food portion estimation automatically is challenging since the processes of food preparation and consumption impose large variations on food shapes and appearances. The aim of this ...paper was to estimate the food energy numeric value from eating occasion images captured using the mobile food record. To model the characteristics of food energy distribution in an eating scene, a new concept of "food energy distribution" was introduced. The mapping of a food image to its energy distribution was learned using Generative Adversarial Network (GAN) architecture. Food energy was estimated from the image based on the energy distribution image predicted by GAN. The proposed method was validated on a set of food images collected from a 7-day dietary study among 45 community-dwelling men and women between 21-65 years. The ground truth food energy was obtained from pre-weighed foods provided to the participants. The predicted food energy values using our end-to-end energy estimation system was compared to the ground truth food energy values. The average error in the estimated energy was 209 kcal per eating occasion. These results show promise for improving accuracy of image-based dietary assessment.
The development of a mobile telephone food record has the potential to ameliorate much of the burden associated with current methods of dietary assessment. When using the mobile telephone food ...record, respondents capture an image of their foods and beverages before and after eating. Methods of image analysis and volume estimation allow for automatic identification and volume estimation of foods. To obtain a suitable image, all foods and beverages and a fiducial marker must be included in the image.
To evaluate a defined set of skills among adolescents and adults when using the mobile telephone food record to capture images and to compare the perceptions and preferences between adults and adolescents regarding their use of the mobile telephone food record.
We recruited 135 volunteers (78 adolescents, 57 adults) to use the mobile telephone food record for one or two meals under controlled conditions. Volunteers received instruction for using the mobile telephone food record prior to their first meal, captured images of foods and beverages before and after eating, and participated in a feedback session. We used chi-square for comparisons of the set of skills, preferences, and perceptions between the adults and adolescents, and McNemar test for comparisons within the adolescents and adults.
Adults were more likely than adolescents to include all foods and beverages in the before and after images, but both age groups had difficulty including the entire fiducial marker. Compared with adolescents, significantly more adults had to capture more than one image before (38% vs 58%, P = .03) and after (25% vs 50%, P = .008) meal session 1 to obtain a suitable image. Despite being less efficient when using the mobile telephone food record, adults were more likely than adolescents to perceive remembering to capture images as easy (P < .001).
A majority of both age groups were able to follow the defined set of skills; however, adults were less efficient when using the mobile telephone food record. Additional interactive training will likely be necessary for all users to provide extra practice in capturing images before entering a free-living situation. These results will inform age-specific development of the mobile telephone food record that may translate to a more accurate method of dietary assessment.
This study investigated age-related changes in turkey welfare measures (wounds, feather quality (FQ), feather cleanliness, and footpad condition (FCON)) and walking ability (gait) as influenced by ...different types of environmental enrichment (EE). Tom turkeys (n = 420) were randomly assigned to: straw bale (S), platform (P), platform + straw bale (PS), pecking block (B), tunnel (T) or control (C; no enrichment) group. Welfare measures and gait were assessed at 8, 12, 16 and 19 wk and analyzed using PROC LOGISTIC with Firth bias-correction. Better wing FQ with age was observed in turkeys in S and T groups. Turkeys in the S group had better wing FQ at 16 (P = 0.028) and 19 wk (P = 0.011) vs. 8 wk. Wing FQ (P = 0.008) was better at 19 vs. 8 wk for T turkeys. FCON worsened over time for turkeys in all treatment groups except for the S group. FCON was worse at 19 vs.8 wk for P (P = 0.024), PS (P = 0.039), B (P = 0.011), T (P = 0.004) and C (P = 0.014) turkeys and was worse at 19 vs. 12 wk for B (P = 0.038), T (P = 0.015) and C (P = 0.045) turkeys. FCON was worse at 19 vs. 16 wk for T (P = 0.007) and C (P = 0.048) turkeys. FCON was also worse at 16 vs. 8 wk for B (P = 0.046) turkeys. Gait worsened with increasing age in all treatment groups. Gait was worse at 19 wk for S (P < 0.001), P (P < 0.001), PS (P < 0.001) and B turkeys (P < 0.001) vs. earlier ages, while gait in T (P < 0.001) and C turkeys (P < 0.001) worsened starting at 16 wk.
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, foods in real-world scenarios are typically long-tail ...distributed, where a small number of food types are consumed more frequently than others, which causes a severe class imbalance issue and hinders the overall performance. In addition, none of the existing long-tailed classification methods focus on food data, which can be more challenging due to the inter-class similarity and intra-class diversity between food images. In this work, two new benchmark datasets for long-tailed food classification are introduced, including Food101-LT and VFN-LT, where the number of samples in VFN-LT exhibits real-world long-tailed food distribution. Then, a novel two-phase framework is proposed to address the problem of class imbalance by (1) undersampling the head classes to remove redundant samples along with maintaining the learned information through knowledge distillation and (2) oversampling the tail classes by performing visually aware data augmentation. By comparing our method with existing state-of-the-art long-tailed classification methods, we show the effectiveness of the proposed framework, which obtains the best performance on both Food101-LT and VFN-LT datasets. The results demonstrate the potential to apply the proposed method to related real-life applications.
Abstract
Aerospace servo motor has the characteristics of strong load capacity, fast response and high control accuracy. To satisfy the design target of high dynamic performance and low weight, the ...high magnetic loading ironless-core array rotor structure is applied to balance the low rotational inertia and high torque density of permanent magnet servo system.
Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel ...level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results.
New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify ...food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.
Objective:
Pirarubicin (THP), one of the anthracycline anticancer drugs, is widely used in the treatment of various cancers, but its cardiotoxicity cannot be ignored. Schisandrin B (SchB) has the ...ability to upregulate cellular antioxidant defense mechanism and promote mitochondrial function and antioxidant status. However, it has not been reported whether it can resist THP-induced cardiotoxicity. The aim of this study was to investigate the effect of SchB on THP cardiotoxicity and its mechanism.
Methods:
The rat model of cardiotoxicity induced by THP was established, and SchB treatment was performed at the same time. The changes of ECG, cardiac coefficient, and echocardiogram were observed. The changes of myocardial tissue morphology were observed by H&E staining. Apoptosis was detected by TUNEL. The levels of LDH, BNP, CK-MB, cTnT, SOD, and MDA in serum were measured to observe the heart damage and oxidative stress state of rats. The expression of cleaved-caspase 9, pro/cleaved-caspase 3, Bcl-2/Bax, and cytosol and mitochondrial Cyt C and Bax was evaluated by western blot. H9c2 cardiomyocytes were cocultured with THP, SchB, and mPTP inhibitor CsA to detect the production of ROS and verify the above signaling pathways. The opening of mPTP and mitochondrial swelling were detected by mPTP kit and purified mitochondrial swelling kit.
Results:
After 8 weeks, a series of cardiotoxicity manifestations were observed in THP rats. These adverse effects can be effectively alleviated by SchB treatment. Further studies showed that SchB had strong antioxidant and antiapoptotic abilities in THP cardiotoxicity.
Conclusion:
SchB has an obvious protective effect on THP-induced cardiotoxicity. The mechanism may be closely related to the protection of mitochondrial function, inhibition of mPTP opening, and alleviation of oxidative stress and apoptosis of cardiomyocytes.
Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone ...eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper we compare two techniques of estimating food portion size from images of food. The techniques are based on 3D geometric models and depth images. An expectation-maximization based technique is developed to detect the reference plane in depth images, which is essential for portion size estimation using depth images. Our experimental results indicate that volume estimation based on geometric models is more accurate for objects with well-defined 3D shapes compared to estimation using depth images.
PDF
