View-Invariant Object Matching via Manifold Warping
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Solution Overview
Problem
Existing computer vision methods struggle to effectively identify and track small vessels across varying viewpoints and poses, due to high variability in vessel designs, sizes, and shapes, as well as challenges in obtaining stable images from large distances, which limits the applicability of 3D model-based pose inference and matching methods.
Innovation Solution
A method involving the construction of a codebook of image parts using neighborhood local patches, with a distance-based part encoding and warping of viewpoints between images to achieve view-invariant object matching, utilizing local linear embedding (LLE) for mapping between low-dimensional manifolds to compute view-rectified distances for similarity measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D model-based pose inference and matching methods are used, then pose-invariant distance measures can be provided, but the methods become inapplicable when truthful 3D reconstruction is not available due to large distances
Solution Approach 1:
The patent replaces 3D model-based geometric reasoning with a data-driven manifold learning approach. Instead of relying on explicit 3D models and trigonometric calculations, the system learns implicit warping functions from 2D image pairs, substituting complex geometric mechanisms with statistical patterns extracted from data.
Solution Approach 2:
The patent introduces low-dimensional manifolds as an intermediary representation between image pairs. These manifolds serve as a compressed intermediate space that captures pose variations without requiring full 3D reconstruction, enabling pose-invariant matching through learned warping functions rather than direct 3D geometry.
2Reliability
If viewpoint-invariant object matching methods are used, then pose-invariant distance measures can be obtained, but the methods fail to provide sufficient discriminability for refined object matching under wide variations in target object appearance
Solution Approach 1:
The patent applies local quality by learning different warping functions for different regions and scales of the image. Instead of using a single global warping model, the system adapts local warping parameters to capture pose variations specific to each region, thereby maintaining both pose invariance and fine-grained discriminability for object matching.
3Area of stationary object
If small vessels are tracked from large distances, then security monitoring coverage is expanded, but image stability deteriorates due to wakes, waves, and motion
Solution Approach 1:
The patent embraces the dynamic nature of maritime imagery by learning adaptive warping functions that account for motion, waves, and wake effects. Rather than assuming static geometric relationships, the system models dynamic pose variations through data-driven manifolds, enabling stable recognition despite changing image conditions.
Data Source
AI summary
A computer-implemented method for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invarient distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.