Global Motion Detection via Image Block Grouping
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Solution Overview
Problem
Existing digital image stabilization techniques face challenges in accurately detecting global motion parameters in video sequences, particularly in handling hand-shake and platform vibrations, which affect the quality of captured images by introducing translation, rotation, and scaling components.
Innovation Solution
A method that calculates local motion vectors for image blocks, groups them, and determines global motion parameters by iteratively judging and adjusting group sizes based on threshold values, using random sample consensus and mean shift mode seeking to select dominant motion vectors for stabilizing images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing digital image stabilization techniques are used to detect global motion parameters, then image stabilization can be achieved, but the detection accuracy deteriorates when handling hand-shake and platform vibrations with multiple motion components
Solution Approach 1:
The patent divides the image into multiple blocks and calculates local motion vectors for each block. These local motion vectors are then grouped and processed to derive global motion parameters. This segmentation approach allows the system to handle complex vibrations by analyzing motion at multiple local levels before synthesizing global motion information, thereby improving detection accuracy despite the presence of hand-shake and platform vibrations.
2Reliability
If iterative judging and adjusting of group sizes is performed to select dominant motion vectors, then the reliability of global motion detection is improved, but the processing time increases
Solution Approach 1:
The patent employs an iterative process where group sizes are dynamically adjusted based on the distribution of motion vectors. The system starts with an initial grouping, calculates motion vectors, and then adapts the group sizes in subsequent iterations based on the results. This dynamic adjustment allows the system to converge on reliable global motion parameters while minimizing unnecessary processing steps, thus balancing reliability improvement with time efficiency.
3Productivity
If local motion vectors are calculated for all image blocks and grouped iteratively, then the speed of global motion detection is improved, but the complexity of the detection algorithm increases
Solution Approach 1:
The patent changes parameters such as group size, threshold values, and iteration counts to optimize the detection process. By adjusting these parameters, the system achieves a balance between processing speed and algorithm complexity. The iterative adjustment of group sizes and the use of statistical thresholds allow the system to process images efficiently while maintaining manageable algorithmic complexity through parameter optimization rather than structural simplification.
Data Source
AI summary
Disclosed is a global motion detecting method which includes receiving a video sequence of input images, calculating local motion vectors, one for each image block of a current input image, grouping image blocks of the current input image into image block groups, calculating a group motion parameter of each of the image block groups based on local motion vectors of the image blocks in each respective image block group, and determining a global motion parameter of the currently input image according to the group motion parameters.