Keypoint Detection via Localized Scale Space Pyramid
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing keypoint detectors, such as SIFT, are computationally intensive and prone to noise sensitivity, especially when detecting keypoints near contours or straight edges, which affects their robustness to noise and variations in rotation, scale, and other image degradations.
Innovation Solution
A method that uses localized scale space properties to identify stable keypoints by constructing a scale space pyramid layer representation of an input image, applying specific filters to produce an approximation, and combining outputs to form a single function of scale and space, identifying local peak values for keypoint locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SIFT and DoG-based approaches are used to detect keypoints, then scale invariance is achieved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the scale-space analysis by constructing a pyramid of Gaussian-filtered images at discrete scales, where each level is processed independently. This segmentation allows scale invariance to be achieved through selective comparison of local extrema across segmented scale levels, rather than continuous analysis, thereby reducing computational complexity while maintaining scale adaptability.
Solution Approach 2:
The patent extends the keypoint detection problem from 2D spatial domain to 3D scale-space by adding the scale dimension. Local extrema are detected in this extended 3D space (x, y, scale), which provides scale invariance. This dimensional extension allows the detector to identify keypoints that are stable across different scales without requiring complex multi-scale processing at each spatial location.
2Productivity
If Harris corner detector is used, then computational speed is improved, but scale invariance is lost
Solution Approach 1:
The patent adds the scale dimension to the traditional 2D Harris corner detector by analyzing the Hessian determinant across multiple scales in a pyramid structure. This dimensional extension enables the detector to identify corners that are stable across different scales while maintaining the computational efficiency of the Harris detector through localized Hessian matrix calculations at each scale level.
3Measurement precision
If local maxima are detected in DoG image pyramid, then keypoint localization is achieved, but noise sensitivity increases near contours and straight edges
Solution Approach 1:
The patent applies local quality assessment by evaluating the Hessian matrix determinant at each detected local extremum in the DoG pyramid. Regions with high Hessian determinant values (indicating strong corner-like structures) are selected as stable keypoints, while regions with low determinant values (such as contours and straight edges) are rejected. This local quality filter significantly reduces noise sensitivity while maintaining accurate keypoint localization.
Data Source
AI summary
A method and system is provided for finding stable keypoints in a picture image using localized scale properties. An integral image of an input image is calculated. Then a scale space pyramid layer representation of the input image is constructed at mulitple scales, wherein at each scale, a set of specific filters are applied to the input image to produce an approximation of at least a portion of the input image. Outputs from filters are combined together to form a single function of scale and space. Stable keypoint locations are identified in each scale at pixel locations at which the single function attains a local peak value. The stable keypoint locations which have been identified are then stored in a memory storage.