Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices: IBCFHA

The rise in consumer awareness of nutritional habits has brought attention to automatic food analysis, but food-logging is cumbersome and requires knowledge of the food item consumed. Digital imaging accurately estimates dietary intake in many environments, but how to derive the food information effectively and efficiently remains a challenging and open research problem. Computer Vision specialists have developed new methods for automatic food intake monitoring and food logging, using 4.0 Industrial Revolution technologies such as deep learning and computer vision robotics. AI-based technologies that allow tracking of physical activities and nutrition habits are rapidly increasing, and automatic analysis of food images plays an important role in nutrition tracking. Image-Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing-based solutions to enhance and improve the accuracy of current measurements of dietary intake.

Format: Hardback
Length: 246 pages
Publication date: 26 March 2023
Publisher: Springer International Publishing AG

In recent years, there has been a significant surge in consumer awareness regarding their nutritional habits, which has brought automatic food analysis into the spotlight. However, the process of food logging is cumbersome and requires a thorough understanding of the food items consumed. Additionally, maintaining a record of every meal can become quite tedious. Accurately documenting dietary caloric intake is essential for managing weight loss, but it also presents challenges due to the reliance on memory to recall foods eaten. Food understanding from digital media has become increasingly important in various domains, with significant applications. Substantial research has demonstrated that digital imaging can accurately estimate dietary intake in various environments, offering several advantages over traditional methods. Nevertheless, there remains a challenging and open research problem regarding how to effectively and efficiently derive food information from digital media.

To address this issue, several recommendations can be made. One approach could involve calorie counting, emphasizing the consumption of healthy foods with specific nutritional compositions. Additionally, considering a system that logs the food consumed by individuals over time could provide valuable health-related recommendations in the long term.

In recent years, Computer Vision specialists have developed innovative methods for automatic food intake monitoring and food logging. The Fourth Industrial Revolution (4.0 IR) technologies, including deep learning and computer vision robotics, play a crucial role in sustainable food understanding. The demand for AI-based technologies that enable tracking of physical activities and nutrition habits is rapidly growing, and automatic analysis of food images assumes an increasingly important role. Computer vision and image processing offer remarkable advancements to various applications, such as food analytics and healthcare analytics, and can greatly benefit patients.

In conclusion, the increase in consumer awareness of nutritional habits has placed automatic food analysis in the spotlight. While food logging presents challenges, digital imaging offers a promising solution for accurately estimating dietary intake. The integration of 4.0 IR technologies, such as deep learning and computer vision robotics, holds great potential for sustainable food understanding and has the potential to improve health outcomes. By leveraging these advancements, we can develop more efficient and effective methods for tracking and analyzing food intake, ultimately promoting healthier lifestyles.

Weight: 553g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031229589
Edition number: 1st ed. 2023