Spatially Coherent Nearest Neighbor Fields for Image Motion
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Solution Overview
Problem
Conventional algorithms for computing nearest neighbor fields between images are computationally expensive and fail to enforce spatial coherency, leading to noisy estimates of optical flow and incorrect results, especially when large motions are involved.
Innovation Solution
A spatially coherent nearest neighbor field algorithm that determines initial matching patches between images and enforces spatial coherency using motion data of neighboring patches, followed by a multi-resolution iterative process to update and refine these patches, ensuring accurate matching across images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional exhaustive search algorithms are used to compute nearest neighbor fields, then matching accuracy is maintained, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the image into overlapping patches and processes them in a hierarchical manner through multiple resolution levels. Instead of exhaustively searching for every patch, the algorithm divides the problem into manageable segments that can be processed efficiently while maintaining matching accuracy through the hierarchical refinement process.
Solution Approach 2:
The patent introduces a hierarchical resolution dimension to the patch matching process. By computing matches at multiple resolution levels (coarse to fine) and enforcing spatial coherency constraints across these dimensions, the algorithm achieves accurate matches without exhaustive search at the finest resolution, thereby improving computational efficiency.
2Stability of the object's composition
If conventional optical flow algorithms are used to enforce spatial coherency, then spatial coherence is improved, but the algorithms fail to handle large motions between images
Solution Approach 1:
The patent segments the motion estimation into multiple resolution levels, allowing coarse-level matches to capture large displacements while fine-level matches refine the results. This hierarchical segmentation enables the algorithm to handle large motions effectively while maintaining spatial coherency through the multi-scale approach.
Solution Approach 2:
The patent performs preliminary patch matching at coarse resolution levels before refining at finer levels. This preliminary action at multiple scales allows the algorithm to establish rough correspondences for large motions early on, then progressively refine them, thereby handling large displacements effectively while maintaining spatial coherence.
3Productivity
If conventional nearest neighbor field algorithms are used, then computational speed is improved, but spatial coherency of matching patches is not enforced
Solution Approach 1:
The patent segments the image processing into hierarchical resolution levels and processes patches in an organized manner. By computing matches at multiple scales and enforcing spatial coherency constraints at each level, the algorithm achieves both computational efficiency and spatial coherence, avoiding the need for slow exhaustive search while maintaining matching quality.
Solution Approach 2:
The patent implements feedback mechanisms through the hierarchical refinement process, where matches from coarser levels inform and constrain matches at finer levels. This feedback ensures spatial coherency is enforced while maintaining computational efficiency, as the feedback guides the search process rather than requiring exhaustive evaluation.
4Device complexity
If conventional algorithms initialize motion field to zero everywhere, then algorithm simplicity is maintained, but large motion between images cannot be accounted for
Solution Approach 1:
The patent segments the motion estimation into hierarchical resolution levels, allowing the algorithm to handle large displacements through coarse-level matches without requiring complex initialization schemes. This segmentation maintains relative algorithmic simplicity while dramatically improving the ability to handle large motions between images.
Solution Approach 2:
The patent introduces hierarchical resolution levels as an additional dimension to the motion estimation process. This dimensional extension allows the algorithm to capture large displacements at coarse levels while maintaining simplicity in the overall framework, avoiding the need for complex initialization while handling large motions effectively.
Data Source
AI summary
In embodiments of spatially coherent nearest neighbor fields, initial matching patches of a nearest neighbor field can be determined at image grid locations of a first digital image and a second digital image. Spatial coherency can be enforced for each matching patch in the second digital image with reference to respective matching patches in the first digital image based on motion data of neighboring matching patches. A multi-resolution iterative process can then update each spatially coherent matching patch based on overlapping grid regions of the matching patches that are evaluated for matching regions of the first and second digital images. An optimal, spatially coherent matching patch can be selected for each of the image grid locations of the first and second digital images based on iterative interaction to enforce the spatial coherency of each matching patch and the multi-resolution iterative process to update each spatially coherent matching patch.