Multiview Camera Image Matching Using Pattern Model Segmentation
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Solution Overview
Problem
Current multiview image processing technologies face limitations due to high data processing requirements and expensive equipment, which restrict their development and application, especially in providing high-resolution and realistic content demands from users.
Innovation Solution
An apparatus and method for image matching between multiview cameras that utilize a pattern model stored with a gradient and corner representation, including a pattern model storing unit, a matching processing unit, and a parameter obtaining unit to enhance precision by primary, secondary, and tertiary matching processes, adjusting parameters for translation, rotation, and scale information to minimize Euclidean distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional corner-point matching scheme is employed, then device complexity is reduced, but manufacturing precision (matching precision) deteriorates
Solution Approach 1:
The patent divides the matching process into three distinct stages: primary matching (coarse alignment using corner points), secondary matching (refinement using gradient information), and tertiary matching (fine-tuning using intensity information). This segmentation allows each stage to focus on specific aspects of the pattern model, progressively improving precision while managing complexity through structured decomposition
Solution Approach 2:
The patent performs preliminary actions by first conducting primary matching to establish coarse alignment, then using the results to guide secondary matching with gradient information, and finally performing tertiary matching with intensity information. Each preliminary stage prepares the data and parameters for the next stage, ensuring that complex operations are performed on pre-aligned data, thereby improving overall precision
2Adaptability or versatility
If multiview image processing is implemented, then user freedom of view is increased, but data processing requirements increase
Solution Approach 1:
The patent extracts and utilizes specific key features from the point cloud data, namely corner points, gradient information, and intensity values. By focusing only on these extracted features rather than processing all point cloud data, the system maintains high matching precision while significantly reducing the computational burden of processing entire multiview image sets
Solution Approach 2:
The patent applies different processing qualities to different parts of the data: corner points receive precise geometric matching, gradient regions receive directional analysis, and intensity regions receive photometric comparison. This local quality approach ensures that each part of the pattern model is processed with the most appropriate method, improving overall efficiency while maintaining versatility across different viewing conditions
3Measurement precision
If entire structure of chess board including gradient pattern model is matched, then matching precision is increased, but loss of time increases
Solution Approach 1:
The patent implements periodic action through its three-stage matching process, where each stage processes specific aspects of the pattern model in sequence: corner points first, then gradient information, and finally intensity information. This periodic, staged approach ensures comprehensive utilization of the entire chess board structure for high precision while managing processing time by dividing work into manageable periodic tasks rather than processing all elements simultaneously
Data Source
AI summary
An apparatus for image matching between multiview cameras includes a pattern model storing unit to store a pattern model, a matching processing unit to match the stored pattern model with a point cloud obtained by at least one depth camera, and a parameter obtaining unit to obtain a parameter for each of the at least one depth camera, based on a result of the matching.