Integral Image Profiles for Hierarchical Motion Estimation
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Solution Overview
Problem
Existing motion estimation methods are computationally intensive and require significant memory, especially when dealing with hierarchical image pyramids, and struggle to efficiently estimate both global and local motion within image sequences.
Innovation Solution
The method employs integral image profiles to estimate motion between image frames, using a single integral image per frame sampled at required levels to produce a hierarchical displacement map, optimizing memory and computational complexity by dividing regions hierarchically and utilizing local minima of the error function to detect multiple motions within regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image pyramids are used for hierarchical motion estimation, then motion estimation precision is improved, but memory requirements and computational complexity increase significantly
Solution Approach 1:
The image is divided into blocks at different hierarchical levels. Instead of creating full image pyramids, the method segments the image into blocks and processes them at multiple levels of detail, allowing precise motion estimation without storing complete pyramid structures.
Solution Approach 2:
Integral images are pre-computed once and stored, allowing rapid calculation of block sums at any level of the hierarchy without repeatedly processing the original image data. This preliminary computation eliminates the need to store multiple pyramid levels.
2Measurement precision
If image pyramids are used for hierarchical motion estimation, then motion estimation precision is improved, but memory bandwidth requirements increase
Solution Approach 1:
Integral images are pre-computed and stored in memory, allowing any block sum to be retrieved through simple subtraction operations. This eliminates the need to repeatedly read and process large amounts of original image data from memory during motion estimation.
Solution Approach 2:
Instead of storing multiple copies of down-scaled pyramid levels, the method uses a single integral image that can generate all necessary block statistics on demand, significantly reducing memory bandwidth requirements.
3Measurement precision
If variable block size is used for motion estimation, then local motion precision is improved, but numerical complexity and processing iterations increase
Solution Approach 1:
The image is divided into blocks of various sizes according to a hierarchical structure. Coarse blocks are processed at higher levels while finer blocks are processed at lower levels, enabling precise local motion estimation without requiring multiple iterations of variable block size searches.
4Measurement precision
If hierarchical image pyramids are stored for motion estimation, then multi-scale motion analysis is improved, but memory storage requirements increase
Solution Approach 1:
The integral image is pre-computed once and stored in memory. This single pre-computed structure enables all multi-scale block statistics to be derived through simple arithmetic operations, eliminating the need to store multiple pyramid levels.
Solution Approach 2:
The integral image serves multiple functions: it enables block sum calculation at any scale and position, supports hierarchical motion estimation, and provides the basis for multi-scale analysis without requiring separate storage for each pyramid level.
Data Source
AI summary
A method of estimating motion between a pair of image frames of a given scene comprises calculating respective integral images for each of the image frames and selecting at least one corresponding region of interest within each frame. For each region of interest, an integral image profile from each integral image is calculated, each profile comprising an array of elements, each element comprising a sum of pixel intensities from successive swaths of the region of interest for the frame. Integral image profiles are correlated to determine a relative displacement of the region of interest between the pair of frames. Each region of interest is divided into a plurality of further regions of interest before repeating until a required hierarchy of estimated motion for successively divided regions of interest is provided.