- Automate Diet using Computer Vision
- Computer information -> Nutritional output
- What is Food Item
- What is the Quantity
- Recognition (Classify Food Items)
- Segmentation (Proportion of Each Item)
- 3D Model
- Identify Food
- Size and Shape to Capture proportions
- Find Nutrients
System Functionality
- Food Image
- Detect Dish
- Segment Dish
- Recognize Food
- 3D Model
- Nutrients Content
- Image - Find Edges, RANSAC
- Random Sampling, Group Edge points by proximity
- Automatic Segmentation, Semi-Automatic Segmentation
- Compute Gradient Map (CNN to detect semantic Edges)
- Identify Dishes and Objects
- Intensity Range Correction
- Ordinal matching filters
- Ordinal Depth range from matches
- Hierarchical reconstruction with robust matchers
- Ellipse for calibration optimisation
- Circle Calibration
- Algebric Work
- Dense Matching using Normalized Cross Correlation
My Thoughts - OpenCV, Contour Detection
Paper to Check - Food Image Segmentation for Dietary Assessment
Volume Estimation
- Depth Scans
- Heavy Algorithm
- Method Manually Segmented
- Single View, Multiple-View, Dense Multi-View
- Food / Non-Food Separation
- Semantic Structures
- Paper Check - Computer vision based Corbohydrate estimation type
- Clinicals trials, ground truth comparison
- Paper Check - Mobile Phone Based Estimation Vs Self Checks
- Identify Metrics of Health
Happy
Next List of Learnings
- Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach
- Optimization modelling and machine learning techniques towards smarter systems and processes
- Deep Learning for Arbitrary Code Generation: Thesis Presentation
- Understanding visual appearance on the web using large-scale crowdsourcing and deep learning.
- Matrix Factorization Methods for Training Embeddings in Selected Machine Learning Problems
Happy Mastering DL!!!
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