Media Stream Alignment via Feature Extraction and Redundancy Removal
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Solution Overview
Problem
Existing methods fail to efficiently align and manage multiple media streams of images or videos captured asynchronously by different digital cameras for the same event, due to unreliable time/date information and challenges in deriving common objects, scenery, or locations using current automatic image analysis algorithms.
Innovation Solution
A method that extracts image features, analyzes them to align media streams chronologically, and produces a master collection by removing redundant content using a cost function, while incorporating geo-locations and user tags to ensure accurate alignment and redundancy reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and arrangement of photos from different digital cameras is performed, then photo alignment accuracy can be improved, but operation complexity and time consumption increase significantly
Solution Approach 1:
The system enables automatic alignment of photos from different cameras by extracting and matching features independently without requiring manual intervention. The algorithm self-adjusts time offsets by analyzing feature correspondences across multiple photo streams, making the system self-sufficient in resolving synchronization issues.
Solution Approach 2:
The patent replaces manual mechanical operations (hand-sorted alignment) with automated computational processes. Feature extraction algorithms, time offset calculation, and automatic photo stream synchronization substitute for manual selection and arrangement, dramatically reducing time consumption while maintaining alignment accuracy.
2Productivity
If automatic alignment using computer algorithms is implemented, then operation efficiency is improved, but alignment accuracy deteriorates due to uncoordinated camera settings
Solution Approach 1:
The patent introduces intermediate feature representations (extracted photo features) that serve as mediators between uncoordinated camera streams. These features act as a common language that enables accurate matching despite differences in camera settings, time zones, and clock synchronizations, bridging the gap between disparate photo streams.
Solution Approach 2:
The system dynamically adjusts time offset parameters for each camera stream based on feature matching results. By treating time synchronization as a variable parameter rather than a fixed setting, the algorithm can optimize alignment accuracy for each pair of photo streams, adapting to different camera configurations and clock drifts.
3Quantity of substance
If multiple photo streams from different cameras are collected, then photo collection completeness is improved, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex task of aligning multiple photo streams into distinct processing stages: feature extraction from individual streams, time offset calculation between pairs of streams, and iterative refinement of synchronization parameters. This segmentation transforms an intractable complex problem into manageable sequential tasks.
Solution Approach 2:
The feature extraction and matching algorithm is designed to be universal across different camera types, settings, and photo streams. The same computational framework handles diverse inputs (different resolutions, formats, time zones, and camera configurations) through a unified approach, reducing processing complexity despite data diversity.
4Ease of operation
If time settings of multiple digital cameras are assumed to be synchronized, then alignment process is simplified, but alignment reliability deteriorates due to actual time discrepancies
Solution Approach 1:
The patent performs preliminary time offset estimation before final alignment by analyzing feature correspondences across photo streams. This preliminary action identifies and corrects time synchronization issues proactively, ensuring reliable alignment before the main processing occurs, rather than assuming synchronization and correcting errors later.
Data Source
AI summary
A method for organizing individual collections of images or videos captured for the same event by different cameras into a master collection, wherein each individual collection forms a media stream in chronological order, includes using a processor to provide the following steps: extracting image features for each image or video of the media stream of each individual collection; analyzing the extracted features to align the media streams to form a master stream in chronological order of the event over a common timeline; producing a master collection of images or videos of the event from the master stream by removing redundant images or videos; and storing, displaying or transmitting the master collection.