Multiview Image Format Detection Using Cross-Correlation Maps
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Solution Overview
Problem
Existing multiview display systems rely on manual specification of image formats, which disrupts workflows and can lead to inaccurate results, particularly for images with repetitive patterns or textures, necessitating an automated and accurate method for detecting multiview formats.
Innovation Solution
The system employs cross-correlation maps generated through auto-correlation operations to identify the arrangement of view images within multiview images, combined with aspect ratio analysis, and uses a classifier trained on feature sets to autonomously detect and classify multiview formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual specification of image formats is used, then user control over format selection is maintained, but workflow disruption and inaccurate results occur particularly for images with repetitive patterns or textures
Solution Approach 1:
The system performs self-detection of multiview format by automatically analyzing image features and cross-correlation patterns, eliminating the need for manual user specification. The processor autonomously determines the format based on extracted features, allowing the system to serve itself rather than requiring external user input, thus maintaining workflow continuity while improving detection accuracy.
2Ease of operation
If automated detection methods are implemented, then workflow disruption is reduced, but detection accuracy deteriorates particularly for images with repetitive patterns or textures
Solution Approach 1:
The detection process is segmented into multiple independent feature extraction steps: cross-correlation map generation, aspect ratio calculation, and template matching. By dividing the automated detection into discrete analytical components, the system can process complex images with repetitive patterns through systematic feature analysis, maintaining both automation and accuracy.
Solution Approach 2:
The system transforms the detection problem from direct image analysis to cross-correlation map analysis. By converting spatial relationships into correlation domain representations, the system gains an additional analytical dimension that helps distinguish true multiview structures from repetitive patterns, improving detection accuracy while maintaining automation.
3Measurement precision
If feature extraction and classification are performed, then multiview format detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the most discriminative features (cross-correlation maps and aspect ratios) necessary for format detection, rather than analyzing all possible image properties. This selective extraction reduces computational complexity while maintaining high detection accuracy by focusing on features that most effectively distinguish between multiview formats.
Data Source
AI summary
Systems and methods are directed to multiview format detection. A multiview image that comprises a plurality of tiled view images is accessed. A cross-correlation map from the multiview image may be generated by autocorrelating the multiview image with a shifted copy of the multiview image. The cross-correlation map may be sampled at a plurality of predefined locations of the cross-correlation map to identify a set of cross-correlation values. A multiview format of the multiview image may be detected by classifying a feature set of the multiview image that comprises the set of cross-correlation values. The multiview image may be configured to be rendered on a multiview display based on the multiview format.