Adaptive Local Windows for Video Object Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video segmentation technologies face high computational complexity and fragmentation issues, making it difficult to accurately extract objects from videos with unclear boundaries and varying characteristics.
Innovation Solution
A video segmentation apparatus and method that uses adaptive local windows, adjusting their size and spacing based on image characteristics such as motion, color, and shape, to efficiently extract desired objects from videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If region expansion technique or segment integration technique is used to separate homogenous regions, then regions having similar characteristics can be identified, but computational complexity increases and object extraction precision deteriorates
Solution Approach 1:
The patent divides the video frame into multiple local windows and processes each window independently to identify homogenous regions. This segmentation approach reduces computational complexity by avoiding global search while maintaining precision through localized analysis of regions with similar characteristics.
Solution Approach 2:
The patent applies different processing strategies to different local windows based on their specific characteristics. Each window is analyzed independently with adaptive thresholding, allowing precise object extraction in each region without requiring computationally intensive global optimization.
2Measurement precision
If watershed technique is used to gradually approach desired precision by dividing and re-combining regions, then segmentation precision improves, but computational complexity increases and fragmentation problems occur
Solution Approach 1:
Instead of using watershed technique that requires iterative division and re-combining, the patent directly segments the video into local windows and processes each independently. This eliminates the need for repeated iterative processes while avoiding fragmentation by maintaining contiguous boundaries through local analysis.
Solution Approach 2:
The patent performs sufficient local analysis in each window to achieve desired precision without over-processing. By focusing computational resources on localized regions with appropriate thresholding, the system achieves accurate segmentation without the excessive computational action required by global watershed methods.
3Reliability
If traditional video segmentation algorithms are used to extract objects, then object extraction can be performed, but fragmentation occurs and contiguous boundaries cannot be found
Solution Approach 1:
The patent segments the video into local windows and processes each window independently to identify homogenous regions. This localized approach prevents fragmentation by ensuring that boundaries are determined based on local characteristics rather than global optimization that may split contiguous regions.
Solution Approach 2:
Each local window is analyzed with adaptive thresholding that considers local image characteristics. This ensures that boundaries are determined based on local homogeneity, maintaining contour continuity and preventing fragmentation that occurs when global methods impose uniform segmentation criteria across the entire video frame.
Data Source
AI summary
A method for controlling a video segmentation apparatus is provided. The method includes receiving an image corresponding to a frame of a video; estimating a motion of an object in the received image to be extracted from the received image, determining a plurality of positions of windows corresponding to the object; adjusting at least one of a size and a spacing of at least one window located at a position of the plurality of determined positions of the windows based on an image characteristic; and extracting the object from the received image based on the at least one window of which the at least one of the size and the spacing is adjusted.