Particular person-Background segmentation
Step one within the processing chain is to separate the foreground individual from the background. That is achieved by a bespoke CNN that was educated on a pixel-wise labelled subset of the coaching set to realize >98% check pixel-wise accuracy. After background elimination, the back and front photographs are normalized to a canonical measurement to take away the impact of various digicam distance (perspective foreshortening).
Coaching to estimate physique fats
Utilizing the normalized back and front photographs as inputs for coaching, a second CNN mannequin was pre-trained to routinely extract discriminative visible options (form, texture) related to physique composition. Discover that with trendy deep studying strategies it’s now not essential to design visible options (filter banks and so forth.) by hand. The structure of our CNN makes use of a number of convolutional blocks with extra branches for multi-scale characteristic extraction. The multi-scale extension permits the community to routinely choose and make the most of excessive decision picture options and seize high-quality particulars (e.g., pores and skin bumps) throughout the physique fats spectrum. This CNN is educated to be resilient to noise within the enter photographs, sturdy to anticipated variations in illumination and digicam orientation, and to work throughout completely different telephone fashions, digicam gadgets and color-spaces (grayscale/RGB). Subsequent, switch studying is utilized to fine-tune the preliminary, pre-trained mannequin utilizing DXA %BF coaching information. DXA right here is used solely to supply reference measurements of physique fats, and DXA photographs are by no means used.
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