Image Segmentation via Multi-Image Fingerprint Merging
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Solution Overview
Problem
Traditional image segmentation methods fall short in identifying novel objects and classifying common objects across different images due to reliance on a priori knowledge and color, lighting, and texture variations, often resulting in incomplete region identification.
Innovation Solution
A system that segments images by analyzing a collection of images to derive fingerprints from distinctive features, comparing these fingerprints to identify similar images with common regions, and merging these regions into semantically-coherent objects using a feature detection, patch generation, image fingerprint generation, similar image identification, geometric matching, and common object generation modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image segmentation examines individual images based on a priori knowledge of color, texture, or models, then the segmentation process is simple and fast, but it fails to identify novel objects and cannot handle color variations across different images
Solution Approach 1:
The patent merges multiple individual images into groups based on similarity metrics, then performs segmentation on the grouped images. This allows the system to leverage information from multiple images to identify common objects and regions, improving reliability while maintaining reasonable processing speed through efficient grouping strategies.
Solution Approach 2:
The patent performs preliminary actions by pre-processing individual images to extract features and organize them into groups before the actual segmentation process. This preliminary organization based on image similarity enables the subsequent segmentation to focus on common regions across multiple images, improving both accuracy and efficiency.
2Device complexity
If image segmentation is performed on a single image at a time, then the processing is computationally simple, but common objects in different images are classified differently due to color, lighting, and texture variations
Solution Approach 1:
The patent combines multiple images into groups based on similarity metrics, then performs segmentation on the grouped images. This merging approach ensures that common objects across different images are classified consistently by leveraging the aggregated information from multiple images, overcoming variations in color, lighting, and texture.
Solution Approach 2:
The patent uses feature extraction to create simplified representations (fingerprints) of images and their regions. These fingerprints serve as copies that capture essential characteristics while ignoring variations in color, lighting, and texture, enabling consistent classification of common objects across different images.
3Ease of operation
If traditional segmentation relies on a priori knowledge of what is being segmented, then the segmentation is straightforward for known objects, but it falls short for novel or yet-to-be-characterized objects
Solution Approach 1:
The patent enables the system to automatically learn and adapt to novel objects by analyzing groups of images and identifying common regions without requiring pre-defined models or a priori knowledge. The system serves itself by building representations of objects from the data, making it adaptable to both known and novel objects equally.
Solution Approach 2:
The patent performs preliminary feature extraction and image grouping to organize data before segmentation, creating a foundation that works for both known and novel objects. This preliminary organization allows the system to handle novel objects effectively by relying on the structural patterns learned from multiple images rather than pre-defined knowledge.
4Measurement precision
If color variations within an object in an image are considered, then the segmentation can capture detailed variations, but the segmentation may only identify sub-regions and not the complete region
Solution Approach 1:
The patent merges segmentation results from multiple images that contain the same object. By combining the segmented regions across images, the system recovers complete object regions even when individual images only capture sub-regions due to color variations. The merging process aggregates partial information into complete regional coverage.
Solution Approach 2:
The patent uses feedback from multiple image segmentations to refine and complete region identification. When one image provides incomplete segmentation of an object due to color variations, the system uses segmentation results from other images as feedback to identify and complete the full region, ensuring both precision and completeness.
Data Source
AI summary
An image segmentation system selects candidate images from an image collection. Image analysis on individual images proceeds by first detecting salient features on each image. Patches are centered on points of interest located on the salient features of the image. Each patch is gridded into blocks. The block feature vectors are merged to generate a patch feature vector. A fingerprint for each image is obtained by merging patch feature vectors across the image. Images with similar fingerprints are identified and geometric matching is performed to further select images with similar objects. Common regions are tabulated and merged into single regions to segment out coherent objects.