Parallel RANSAC Model Derivation via Hybrid Sequential-Parallel Processing
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Solution Overview
Problem
Current algorithms for deriving mathematical models from data sets with a high proportion of outliers, such as RANSAC, are inefficient due to sequential processing, consuming significant resources and time, especially when determining the number of iterations required.
Innovation Solution
Implementing a hybrid approach that combines sequential and parallel processing for the RANSAC algorithm, where subsets of data are generated sequentially but multiple proposed models are derived and tested in parallel, with the determination of the best fit and recalculation of iterations performed less frequently, utilizing multiple cores for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sequential processing is used for RANSAC algorithm, then resource consumption is reduced, but processing time increases significantly
Solution Approach 1:
The patent segments the RANSAC algorithm into independent parallelizable components: data subset generation, model derivation, and model testing. Multiple model derivations and tests are executed in parallel across different processing cores, while maintaining sequential control for iteration management. This segmentation enables simultaneous execution of multiple computational tasks without excessive resource overhead.
Solution Approach 2:
The patent transitions from single-threaded sequential execution to multi-core parallel execution, adding a spatial dimension to processing. By distributing model derivation and testing across multiple processing cores, the algorithm achieves significant speedup while maintaining manageable resource consumption through controlled parallelism.
2Loss of time
If parallel processing is used for all RANSAC operations, then processing time is reduced, but device complexity increases
Solution Approach 1:
The patent carefully segments which operations should be parallelized (model derivation and testing) versus which should remain sequential (iteration control and result aggregation). This selective segmentation reduces processing time for computationally intensive portions while avoiding the complexity overhead of parallelizing control flow and data management.
Solution Approach 2:
The patent applies parallel processing partially - only to the model derivation and testing phases - rather than attempting to parallelize the entire RANSAC algorithm. This partial application achieves significant time reduction while keeping device complexity manageable by leaving control-intensive operations sequential.
3Productivity
If multiple proposed models are derived and tested in parallel, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent segments the computational workload into parallel model derivation tasks and sequential energy-intensive operations. By parallelizing only the computationally intensive model testing portions while keeping control and aggregation sequential, the system improves throughput without proportionally increasing energy consumption across all operations.
Solution Approach 2:
The patent maintains continuous useful action by keeping the sequential control flow and result aggregation operations active while parallel model testing is performed. This ensures that energy is not wasted on idle processing while maintaining high productivity during the parallel computation phases.
Data Source
AI summary
Various embodiments are generally directed to techniques for employing a hybrid of sequential and parallel processing to perform random sample and consensus (RANSAC). A device to perform RANSAC includes a derivation component to derive a first set of proposed models in parallel from a first set of minimal sample sets of a data set; and a comparison component to recalculate a required quantity of proposed models to derive an accurate model if a proposed model of the first set of proposed models better fits the data set than any proposed model derived prior to derivation of the first set of proposed models, and to determine whether to derive a second set of proposed models following derivation of the first set of proposed models based on a comparison of the required quantity to a quantity of previously derived proposed models that includes the first set. Other embodiments are described and claimed.