Multi-View Landmark Validation for Image Pose Correctness
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Solution Overview
Problem
Existing image processing techniques, particularly in pose estimation and landmark recognition, lack the ability to accurately characterize the correctness of their results, which hinders the control and improvement of various image processing applications such as robotics, medical imaging, and self-driving vehicles.
Innovation Solution
A method for characterizing the correctness of image processing by receiving multiple images of an object at different relative poses, estimating the relative pose and landmark positions, and comparing these estimates with external or internal information to transfer and validate the poses, iteratively refining the results until a threshold level of correctness is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pose estimation and landmark recognition are performed using existing image processing techniques, then the processing can be completed, but the correctness of the results cannot be accurately characterized
Solution Approach 1:
The patent implements feedback by comparing estimated poses and landmark positions against external information (such as known object models or ground truth data) to generate correctness characterizations. This feedback loop enables continuous validation and improvement of pose estimation accuracy, allowing the system to identify and correct erroneous estimates.
Solution Approach 2:
The patent performs preliminary actions by acquiring multiple images of the object from different viewpoints before final pose estimation. This preliminary multi-view data collection provides additional constraints and information that enable more accurate characterization of pose correctness, preventing errors before they propagate through the processing pipeline.
2Measurement precision
If multiple images at different relative poses are acquired and compared iteratively, then the accuracy of pose estimation is enhanced, but the processing time and complexity increase
Solution Approach 1:
The patent applies partial action by performing iterative refinement only when necessary - comparing estimated poses against external information and continuing iterations only when correctness thresholds are not met. This approach achieves high accuracy when needed while avoiding unnecessary processing time when the initial estimation is already sufficient.
Solution Approach 2:
By acquiring multiple images at different relative poses as preliminary data before final pose estimation, the system establishes a stronger foundation for accurate characterization. This preliminary multi-view data reduces the number of iterative corrections needed, balancing accuracy enhancement with processing time efficiency.
3Reliability
If the correctness threshold is set high to ensure reliable results, then the reliability improves, but the productivity of image processing decreases
Solution Approach 1:
The system performs partial action by applying full iterative refinement only to cases where initial pose estimates fail to meet the correctness threshold. For estimates that already satisfy the threshold, processing is terminated early, maintaining high reliability for critical results while preserving productivity for cases where the initial estimation is already sufficient.
Data Source
AI summary
A method for characterizing correctness of image processing includes receiving images of an instance of an object, wherein the images were acquired at different relative poses, identifying positions of corresponding landmarks on the object in each of the received images, receiving information characterizing a difference in position or a difference in orientation of at least one of the instance of the object and one or more imaging devices when the images were acquired, transferring the positions of landmarks identified in a first of the images based on the difference in position or the difference in orientation, and comparing the positions of the transferred landmarks with the positions of the corresponding of the landmarks identified in a second of the received images, and characterizing a correctness of the identification of the positions of the landmarks in at least one of the received images.