Image Transformation Model for Body Condition Projection
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Solution Overview
Problem
Current technologies lack an effective platform to motivate individuals to change their lifestyle by objectively visualizing the effects of being overweight or underweight, which are often linked to psychological and health issues such as obesity and anorexia, and do not provide a reliable method to project future body conditions based on current habits.
Innovation Solution
A computing platform that transforms submitted images of individuals based on their associated health data, using deformation vectors and transformation models to render predicted images of their body condition, allowing for the visualization of potential health outcomes and guiding lifestyle changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a transformation model is used to project future body conditions, then the ability to motivate lifestyle change is improved, but the accuracy and reliability of the projection may deteriorate due to psychological distortion and subjective perception
Solution Approach 1:
The system creates a virtual copy of the user's body by mapping their current physical appearance to a digital avatar. This avatar serves as an objective representation that can be transformed and projected into future states, eliminating the need to directly manipulate or interpret the user's subjective self-perception while maintaining accurate visual feedback for motivation purposes
Solution Approach 2:
The patent introduces a computational transformation model as an intermediary between the user's current body state and the projected future state. This intermediary process objectively applies physiological parameters (such as weight change rates, body composition changes) to generate accurate predictions, separating the scientific projection mechanism from the user's potentially distorted self-perception
2Measurement precision
If detailed transformation parameters are applied to accurately render body changes, then the visualization accuracy is improved, but the computational complexity and processing time deteriorate
Solution Approach 1:
The transformation model is divided into separate modular components: a registration module that handles image alignment and feature point identification, and a transformation module that applies the actual geometric and textural changes. This segmentation allows each module to be optimized independently, reducing overall computational complexity while maintaining high visualization accuracy
Solution Approach 2:
The system performs preliminary registration and feature extraction on the user's submitted image before applying the transformation. By pre-processing the image to establish accurate feature correspondences and create a structured representation, the subsequent transformation operations become computationally more efficient and produce more accurate results
Data Source
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AI summary
Apparatuses, computer media, and methods for altering a submitted image of a person. The submitted image is transformed in accordance with associated data regarding the person's condition. Global data may be processed by a statistical process to obtain cluster information, and the transformation parameter is then determined from cluster information. The transformation parameter is then applied to a portion of the submitted image to render a transformed image. A transformation parameter may include a texture alteration parameter, a hair descriptive parameter, or a reshaping parameter. An error measure may be determined that gauges a discrepancy between a transformed image and an actual image. A transformation model is subsequently reconfigured with a modified model in order to reduce the error measure. Also, the transformation model may be trained to reduce an error measure for the transformed image.