Dense Pixel Correspondence Mapping with Bit-Hash Patches
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Solution Overview
Problem
Conventional image processing methods for determining scene geometry or motion either output sparse correspondence maps that lack sufficient informational density or are resource-intensive and slow, making them impractical for real-time or near-real-time applications.
Innovation Solution
A system and method that generates a deterministic, dense correspondence map between images using pixel representations, low-discrepancy sequences, and bit-hashes to calculate correspondence vectors, enabling efficient and rapid pixel matching, even in featureless scenes, without the need for global optimization or rectified images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional methods are used to determine scene geometry or motion, then correspondence maps can be generated, but the output is either sparse and lacks sufficient informational density or is resource-intensive and slow
Solution Approach 1:
The patent segments the image processing task by dividing the image into multiple patches and processing them independently. Each patch is hashed to generate a bit-string representation, allowing parallel processing and reducing the computational burden while maintaining dense correspondence information across the entire image.
Solution Approach 2:
The patent transforms pixel data into a compressed bit-string parameter representation through hashing. This parameter transformation reduces the data dimensionality and computational complexity while preserving the essential visual information needed for correspondence matching, enabling real-time processing.
2Measurement precision
If conventional methods generate correspondence maps, then pixel matching can be performed, but the process is resource-intensive and slow
Solution Approach 1:
The patent creates a compressed bit-string copy of each image patch through hashing, rather than working with the full original pixel data. This copy contains the essential visual information for correspondence matching but requires significantly fewer computational resources for comparison and processing.
Solution Approach 2:
The patent extracts only the essential visual information from each patch by generating a bit-string hash representation. This extraction removes redundant data while preserving the critical features needed for accurate correspondence matching, thereby reducing processing resource requirements.
3Reliability
If conventional methods are used, then correspondence maps can be determined, but they are not suitable for real-time or near-real-time applications
Solution Approach 1:
The patent performs preliminary hashing of image patches into bit-strings before correspondence matching is required. This pre-processing step creates ready-to-compare representations that can be rapidly matched in real-time, eliminating the need for computationally intensive analysis during time-critical operations.
Solution Approach 2:
The patent replaces complex mechanical image processing operations with more efficient bit-string comparison operations. By substituting the heavy computational machinery of conventional correspondence algorithms with simpler hash-based comparisons, the system achieves real-time performance without sacrificing reliability.
Data Source
AI summary
A system and method for determining a correspondence map between a first and second image by determining a set of correspondence vectors for each pixel in the first image and selecting a correspondence vector from the set of correspondence vectors based on a cost value.