Image Region Correspondence via Iterative Patch Perturbation
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Solution Overview
Problem
Existing methods for determining correspondence between image regions are computationally demanding and not suitable for interactive applications, especially for large images, due to the complexity of bidirectional similarity calculations.
Innovation Solution
A method involving iterative evaluations with a propagation and perturbation phase to improve mapping between image regions, using an offset object and random search techniques to quickly converge on optimal correspondences, applicable in various image processing tasks such as image synthesis and retargeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bidirectional similarity calculation is performed using nearest neighbor patches, then correspondence accuracy between image regions is improved, but computational time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by performing a coarse-to-fine search strategy. Before computing the full bidirectional similarity for all patches, the algorithm first identifies candidate patches using a simplified distance metric or coarse sampling. This preliminary identification narrows down the search space, allowing the computationally intensive nearest neighbor calculation to be applied only to promising candidates, thus reducing overall computational time while maintaining correspondence accuracy.
Solution Approach 2:
The patent implements partial action by computing bidirectional similarity for only a subset of patches rather than all patches. The algorithm identifies and processes only those patches that are likely to contain meaningful correspondences based on initial filtering criteria (such as patch variance, edge detection, or coarse matching). This selective approach reduces computational time significantly while maintaining accuracy for the most important correspondences.
2Productivity
If approximation techniques are used to reduce computational complexity, then processing speed is improved, but accuracy of correspondence determination deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the correspondence determination process into multiple stages: (1) coarse filtering using simple distance metrics, (2) candidate selection based on preliminary matching quality, and (3) refined bidirectional similarity calculation for selected candidates. Each stage uses an appropriate level of computational complexity, with simpler methods used early for speed and more accurate methods applied later for precision, thus achieving both processing speed and accuracy.
Solution Approach 2:
The patent implements dynamics by adaptively adjusting the level of approximation based on local image characteristics. For regions with simple textures or repetitive patterns, more aggressive approximation techniques are used to speed up processing. For regions with complex textures, edges, or unique features, the algorithm switches to more accurate bidirectional similarity calculations. This dynamic adaptation maintains correspondence accuracy while optimizing processing speed for different image regions.
Data Source
AI summary
Determining correspondence between image regions includes identifying first and second regions of visual content including pixels in a computer system. The first region includes a first patch of pixels having a first mapping to a second patch of pixels in the second region. Iterative evaluations of the first and second regions are performed, each including at least (i) a first evaluation phase selecting a best mapping for the first patch, according to a distance metric, the best mapping selected from among the first mapping and a second mapping obtained from mappings of nearby pixels, and (ii) a second evaluation phase selecting one of the best mapping and a third mapping obtained by perturbing the second mapping. A result of the iterative evaluations is recorded in the computer system that indicates a third patch of pixels in the second region identified in the iterative evaluations.