Interest Point Detection Hardware Using Line Buffer Pipeline
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Solution Overview
Problem
Current interest point detection algorithms for images are computationally expensive and require significant memory, making them costly and inefficient for real-time applications, especially when integrated into hardware like image sensor chips.
Innovation Solution
A silicon-based implementation of interest point detection that reduces memory requirements by using a line buffer and convolution engine to process image data in series, allowing for downscaled image data to be generated and processed without storing entire frames, and employing Hessian filters to identify candidate points at multiple scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interest point detection algorithms are used, then accurate feature extraction is achieved, but computational cost and memory requirements increase significantly
Solution Approach 1:
The patent segments the image processing task by processing one line of image data at a time through a pipeline, rather than loading entire frames into memory. This allows interest point detection to be performed on small portions of data sequentially, dramatically reducing memory requirements while maintaining detection accuracy through multi-scale analysis.
Solution Approach 2:
The patent introduces a temporal dimension to the processing pipeline by using buffered storage that holds multiple lines of data temporarily. This allows the system to accumulate sufficient data for multi-scale analysis without requiring all frame data to be simultaneously available in memory, resolving the contradiction between accuracy requirements and memory constraints.
2Measurement precision
If traditional interest point detection algorithms are used, then accurate feature extraction is achieved, but processing speed decreases due to computational complexity
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing scaled versions of the input image data in buffered storage before interest point detection begins. This allows the detection algorithm to access pre-prepared multi-scale data without performing computationally intensive scaling operations during the detection phase, thereby maintaining accuracy while improving processing speed.
Solution Approach 2:
The patent establishes continuous processing through a pipeline architecture where image data flows continuously through buffering, scaling, and detection stages. This eliminates idle time between processing steps and maintains high processor utilization, improving productivity while preserving the accuracy benefits of multi-scale analysis.
3Loss of information
If entire frames are stored for processing, then complete image analysis is possible, but system cost and memory usage increase
Solution Approach 1:
The patent extracts only the essential data needed for interest point detection by processing individual lines sequentially and discarding processed data after it has been analyzed across multiple scales. This extraction approach maintains the completeness of analysis for detected features while eliminating the need to store entire frames, significantly reducing memory requirements.
Solution Approach 2:
The patent implements a discard-and-recover strategy where lines of image data are discarded from the buffer after being processed at all required scales, but the ability to recover and re-process data is maintained through the buffered architecture if needed for refinement. This allows complete analysis without permanent storage of all frame data, reducing memory usage while preserving information completeness.
Data Source
AI summary
Interest points are markers anchored to a specific position in a digital image of an object. They are mathematically extracted in such a way that, in another image of the object, they will appear in the same position on the object, even though the object may be presented at a different position in the image, a different orientation, a different distance or under different lighting conditions. Methods are disclosed that are susceptible to implementation in hardware and corresponding hardware circuits are described.