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@article{171000,
        author = {Ruchitha R and Vanajakshi V and Soujanya SN and Rashmi J},
        title = {A Review On Food Recognition With Computer Vision For Nutrit. Insights And Dietary Recommendations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1896-1903},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171000},
        abstract = {With increasing global health concerns like obesity and malnutrition, there is a growing need for tools that can analyze diets and provide personalized recommendations. Computer vision, combined with artificial intelligence, has made it possible to automate nutrition analysis and dietary suggestions using images of food. These systems can recognize food items, estimate their portions, and calculate nutritional content, either through step-by-step methods or end-to-end models. To address challenges such as limited datasets and the complexity of meals, researchers are using innovative techniques like synthetic data generation, advanced segmentation models like Mask R-CNN, and multi-task learning. These advancements make it easier to analyze food in real-world settings accurately. This paper highlights the potential of such technologies for improving health monitoring and suggests future improvements in areas like detailed food classification and better volume estimation techniques for more accurate dietary assessments.},
        keywords = {Computer Vision, Nutrition Analysis, Dietary Recommendation, Food Recognition, Food Segmentation, Deep Learning, Synthetic Data, Mask R-CNN, Volume Estimation, Personalized Health Monitoring},
        month = {December},
        }