PROJECT SEMINAR THEME Volume Estimation of Real world objects Joel Vilanilam Zachariah Roll No. 30 | CSU 161 30 | MDL16CS059 Govt. Model Engineering College 

BASE PAPER MUSEFood OTHER APPROACHES Kabaq WHY THIS THEME? ARomapp KEY HIGHLIGHTS 

PART 0: PROLOGUE OUR PURSUIT TO REDUCE FOOD WASTAGE 

PART 1: DEPTH ESTIMATION TECHNIQUES DERIVING 3D FROM 2D 

BASE PAPER MUSEFood MULTI-SENSOR-BASED FOOD VOLUME ESTIMATION ON SMARTPHONES 

PART 2: INTRODUCTION THE APPROACH OF THIS METHOD 

FOOD SEGMENTATION FROM BACKGROUND (1) CNN + GrabCut (2) Point Matching RELATIVE FOOD SIZE TO ACTUAL FOOD SIZE (1) Place reference object (2) Depth image from special device 

PART 3: RELATED WORK 

FOOD ITEM SEGMENTATION Location of Food item in image ACTUAL SIZE SCALING Reference Object OR Train to detect FOOD MODELING Deriving Deep maps from deep Neural Networks OR Stereo Matching 

PART 4: METHODS 

OVERVIEW 1) Sensing 2) Data Processing 3) Data Aggregation SENSING 1) Food Image sensing 2) Audio sensing DATA AGGREGATION 1) Actual Size Scaling 2) Food Modeling & Volume Calculation DATA PROCESSING 1) Audio Signal Processing for distance measurement 2) Food Item Segmentation 

SENSING Take a top-down as well as a side view photograph Ensure they are respectively parallel and perpendicular to surface Echo ranging is used with a Maximum Length Sequence (MLS) MLS is a pseudorandom binary sequence (contains 0's and 1's) Length of MLS = 2^n -1 Start recording the effect of emiting audio signal while taking photographs. Stop recording when done with capturing photographs. 1) FOOD IMAGE SENSING 2) AUDIO SENSING 

DATA PROCESSING 1) AUDIO SIGNAL PROCESSING FOR DISTANCE MEASUREMENT 

DATA PROCESSING Two Tasks: Food segmentation Task Container Classification Task 2) FOOD ITEM SEGMENTATION Hence, this is a Multi-task deep learning problem. 

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DATA AGGREGATION 1) ACTUAL SIZE SCALING 2) FOOD MODELING AND VOLUME CALCULATION 

PART 5: EVALUATION 

MLS RANGING 1) Ranging Accuracy 2) Ranging Robustness FOOD IMAGE SEGMENTATION 1) Dataset 2) Experimental Set Up 3) Evaluation Metric 4) Performance FOOD VOLUME ESTIMATION Testing it out 

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PART 6: CONCLUSION 

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PART 7: EPILOGUE REAL WORLD IMPLEMENTATIONS 

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THANK YOU