Multi-View Image Segmentation Using Shared Codebook
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Solution Overview
Problem
Conventional methods for segmenting multi-view images into foreground and background face challenges in modeling complex backgrounds, are inefficient in memory usage, and perform poorly when applied to high-definition multi-view images, leading to increased memory demands and reduced performance.
Innovation Solution
A method and apparatus that generate a shared codebook and pixel-based codeword mapping tables for multi-view images, allowing for efficient segmentation by measuring distances between pixel information and codewords, and using morphology for post-processing to accurately separate foreground and background while minimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-based background models are generated for multi-view images, then segmentation accuracy is improved, but the amount of memory used is increased in proportion to the size of an image and the number of views
Solution Approach 1:
The patent merges the codebook generation process across multiple views by creating a single shared codebook that is common to all views, rather than generating separate codebooks for each view. This consolidation reduces the total memory requirement from O(N×M) to O(M) where N is the number of views and M is the codebook size, while maintaining segmentation accuracy through the shared probabilistic model.
Solution Approach 2:
The shared codebook serves multiple functions simultaneously: it acts as the background model for all views, enables consistent segmentation across different camera perspectives, and provides a universal representation that can be applied to any view in the multi-view image set without requiring view-specific models.
2Measurement precision
If the number of Gaussians is increased to model rapid variation in the background, then background modeling accuracy is improved, but slowly varying background is detected as foreground
Solution Approach 1:
The patent changes the fundamental parameter of background modeling from using multiple Gaussians (MOG method) to using a codebook of prototype pixels. This parameter change allows the system to model background variation through the codebook's representative samples rather than through Gaussian distributions, eliminating the trade-off between modeling complexity and false positive rates.
3Speed
If codebook method is used to focus on speed, then processing speed is improved, but redundant background models are generated and many codewords need to be searched
Solution Approach 1:
Instead of creating redundant background models for each view, the patent uses a single shared codebook that is copied and applied across all views. This approach maintains the speed advantage of codebook methods while eliminating redundancy by using one universal codebook structure that serves all views simultaneously.
Data Source
AI summary
Embodiments of the present invention provide methods and apparatuses for segmenting multi-view images into foreground and background based on a codebook. For example, in some embodiments, an apparatus is provided that includes: (a) a background model generation unit for extracting a codebook from multi-view background images and generating codeword mapping tables operating in conjunction with the codebook; and (b) a foreground and background segmentation unit for segmenting multi-view images into foreground and background using the codebook and the codeword mapping tables.