Feature Point Descriptor Using Ring Sectors
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Solution Overview
Problem
Existing image analysis methods for scene analysis rely heavily on proprietary feature point detectors and descriptors, necessitating licensing and lacking alternative scale-invariant solutions.
Innovation Solution
A computer-implemented method that describes the surrounding of a feature point using a feature vector by dividing the image into non-overlapping rings with sectors, allowing for multiscale descriptor representation and improved robustness to noise and rotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proprietary feature point detectors and descriptors (e.g., SIFT, SURF) are used, then accurate feature point detection and description is achieved, but licensing requirements and increased cost are imposed
Solution Approach 1:
The patent implements alternative feature point detection and description methods that replicate the functionality of proprietary algorithms like SIFT and SURF without requiring their licenses. The invention uses publicly available algorithms and approaches to create comparable feature vectors, thereby achieving accurate feature point matching while avoiding licensing costs and restrictions
Solution Approach 2:
The patent employs computationally efficient feature detection and description techniques that can be executed without expensive proprietary software licenses. By using alternative algorithms that are freely implementable, the system achieves comparable performance to proprietary solutions while eliminating licensing overhead and costs
2Reliability
If traditional feature descriptors are used, then feature point description is achieved, but scale invariance and noise resistance are insufficient
Solution Approach 1:
The patent divides the feature point description into multiple segments or components, where each segment captures specific aspects of the local image structure. This segmentation approach enhances robustness to noise and scale variations by distributing information across multiple descriptor elements, allowing the system to maintain reliability even when individual segments are affected by noise
Solution Approach 2:
The patent extends feature descriptors into additional dimensions to capture scale-invariant information. By incorporating multi-scale analysis and adding dimensional components that represent features at different scales, the descriptor achieves scale invariance while maintaining a structured organization that manages complexity through hierarchical arrangement
3Adaptability or versatility
If existing feature point methods are used, then image comparison is achieved, but alternative scale-invariant solutions are lacking
Solution Approach 1:
The patent develops feature detection and description methods that are universally applicable across different image scales and conditions. The implemented algorithms provide multi-functional capability by detecting and describing features that remain consistent across scale transformations, thereby achieving scale invariance and providing versatile solutions that work across diverse image analysis scenarios
Data Source
Figure 1A~1B
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Figure 2B
AI summary
A computer-implemented method for describing a surrounding of a feature point (F) in an image by a feature vector (D), the method comprising: assigning (101) the pixels of the image located around the feature point (F) to at least two segments, each segment having a form of a non-overlapping ring centered at the feature point (F), wherein the ring of the first segment consists of pixels located not farther than a first radius (R1) from the feature point (F); and wherein the ring of the second segment consists of pixels located not farther than a second radius (R2) from the feature point (F) and that do not belong to the first ring; assigning (102) the pixels of each segment to angular sectors (S1, S2, ...), the sectors comprising pixels arranged within a particular angular range from the feature point (F), the angular range being equal for each of the sectors (S1, S2,...), wherein the sectors are adjacent to each other or overlap each other by the same overlapping range, wherein each of the pixels belongs to no more than two sectors; within each sector of each segment, calculating (103) at least one descriptor measure as a measure function of values of pixels belonging to that sector; and providing (104) the feature vector as a set of the calculated descriptor measures for all sectors of all segments.