Spiral Feature Search Algorithm for Image Processing
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Solution Overview
Problem
Traditional brute-force methods for feature detection in computer vision applications are inefficient, as they traverse all pixels in a region of interest regardless of the probabilistic distribution of feature occurrence, leading to increased computational time and power consumption.
Innovation Solution
A spiral feature search method that starts from the center of a region of interest and traverses outward, utilizing a lookup table to efficiently identify local features, significantly reducing the expected time of feature detection by leveraging the probabilistic concentration of feature occurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional brute-force methods traverse all pixels in a region of interest, then all pixels are examined for feature detection, but computational time and power consumption increase significantly
Solution Approach 1:
The patent applies local quality by varying the search strategy based on the probabilistic distribution of features. The spiral search pattern concentrates examination density in the central region where features are more likely to occur, while reducing examination in peripheral regions. This non-uniform search strategy matches the local probability distribution, improving efficiency without sacrificing detection reliability.
Solution Approach 2:
The patent uses preliminary action by pre-computing and storing the spiral traversal sequence in a lookup table. This pre-processed search pattern is prepared in advance, allowing the feature detection algorithm to follow an optimized path without real-time computation overhead, thus reducing computational time while maintaining complete pixel coverage.
2Reliability
If traditional brute-force methods traverse all pixels in a region of interest, then all pixels are examined for feature detection, but power consumption increases
Solution Approach 1:
The spiral search pattern implements local quality by concentrating computational effort in the central region of interest where features are more likely to occur. By following a predetermined spiral trajectory, the algorithm examines pixels in an order that prioritizes high-probability regions, reducing the effective computational workload and thus lowering power consumption while maintaining detection reliability.
Solution Approach 2:
The lookup table containing the spiral traversal sequence is pre-computed and stored, eliminating the need for real-time path calculation. This preliminary preparation reduces runtime computational operations and associated power consumption, especially important for mobile or embedded devices with limited power supplies.
3Ease of operation
If a full lookup table stores all pixel coordinates sorted by distance from center, then complete spiral sequence is available, but storage requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the complete set of pixel coordinates into meaningful groups based on their distance from the center. The lookup table stores coordinates organized by concentric rings or distance levels, allowing the system to load or process only the relevant segment of the search space at each iteration, reducing memory requirements while maintaining complete search capability.
Solution Approach 2:
The patent transforms the two-dimensional spatial arrangement of pixels into a one-dimensional sorted sequence based on distance from the center. By mapping pixel coordinates to their radial distance and sorting accordingly, the system creates a compact linear representation that preserves spatial relationships while reducing storage complexity and enabling efficient sequential access during spiral traversal.
Data Source
AI summary
A computing system configured to identify a region of interest in an image having a plurality of pixels, each of which corresponds to a feature score. The region of interest is a section of the image where feature points reside. The computing system is also configured to traverse one or more pixels in the region of interest in a spiral sequence starting from a center of the region of interest to edges of the region of interest to determine whether the corresponding pixel is a feature point.