Patient Representation Model for Depth Ambiguity Resolution
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Solution Overview
Problem
Conventional methods for estimating patient depth images in medical environments face challenges such as depth ambiguity and the lack of constraints to ensure realistic human pose and shape estimation, limiting their accuracy in providing medical guidance.
Innovation Solution
A computer-implemented method and system that utilize a patient representation model, including feature extraction and parameter determining models, to receive and process depth and two-dimensional images, generate feature vectors, concatenate them, and determine three-dimensional patient parameters, while incorporating constraints to reduce ambiguity and ensure realistic estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional estimation methods are used to estimate patient depth images, then the process is simple and fast, but depth ambiguity arises where multiple three-dimensional configurations may explain the same two-dimensional image
Solution Approach 1:
The system uses a feedback loop where the estimated three-dimensional patient parameters are fed back to refine the depth image estimation. The model iteratively adjusts parameters based on the consistency between projected two-dimensional images and the actual input images, resolving depth ambiguity through multiple iterations of refinement.
Solution Approach 2:
The patent transitions from two-dimensional image input to three-dimensional parameter estimation by introducing depth as an additional dimension. The system estimates three-dimensional patient parameters (including depth information) from two-dimensional images, effectively adding the depth dimension to resolve ambiguities that exist in purely two-dimensional representations.
2Reliability
If conventional estimation methods are used, then computation time is short, but the estimation lacks constraints for realistic human shape and pose
Solution Approach 1:
The system incorporates preliminary constraints during the estimation process that encode realistic human shape and pose information. Before final parameter determination, the model applies prior knowledge about human anatomy and pose variability to guide the estimation, ensuring results are physically plausible while reducing the search space for computation.
Solution Approach 2:
The patent dynamically adjusts estimation parameters based on the input image characteristics and iterative refinement. The model changes parameters such as pose angles, body segment positions, and depth values in a controlled manner guided by consistency checks between projected and actual images, achieving realistic results through parameter optimization.
3Measurement precision
If two-dimensional images are used for patient estimation, then the system is simple to implement, but depth information is lost and multiple three-dimensional configurations are possible
Solution Approach 1:
The system creates a virtual three-dimensional copy or representation of the patient from two-dimensional images. By generating a parameterized three-dimensional model that replicates the patient's geometry and pose, the system preserves depth information indirectly through the modeled parameters, allowing accurate representation without direct depth measurement.
Solution Approach 2:
The patent recovers lost depth information by transforming the problem from two-dimensional to three-dimensional parameter space. The system estimates three-dimensional parameters (including depth) that, when projected, reproduce the observed two-dimensional image, effectively reconstructing the lost depth dimension through computational modeling.
4Measurement precision
If the patient representation model parameters are changed to reduce deviation from ground truth, then estimation accuracy improves, but the model complexity increases
Solution Approach 1:
The system employs feedback mechanisms where estimated parameters are compared against ground truth or consistency checks, and the model adjusts parameters iteratively based on the deviation. This feedback loop enables automatic optimization of parameter estimation accuracy without requiring manual intervention or overly complex model structures.
Solution Approach 2:
The patent optimizes model parameters through controlled changes and adjustments rather than using complex fixed structures. The system modifies parameters such as weightings, thresholds, and iterative refinement steps to balance accuracy improvement with model simplicity, achieving high precision through parameter tuning rather than structural complexity.
Data Source
AI summary
Methods and systems for using a patient representation model including a feature extraction model and a parameter determining model. For example, a computer-implemented method includes receiving, by a first feature extraction model, a depth image; generating, by the first feature extraction model, a first feature vector corresponding to the depth image; determining, by a parameter determining model, a plurality of three-dimensional model parameters based at least in part on the first feature vector; receiving a ground truth; determining a deviation between the ground truth and information associated with the plurality of three-dimensional model parameters; changing, based at least in part on the deviation, one or more parameters of the patient representation model; receiving a first patient image; determining a plurality of three-dimensional patient parameters based at least in part on the first patient image; and providing the plurality of three-dimensional patient parameters as medical guidance.