Parallel Pixel Processing Circuit for Real-Time Image Sieve Operations
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Solution Overview
Problem
Existing image processing methods, such as morphological scale-space transforms or sieves, are computationally intensive and dependent on image content, making real-time processing of images like video material challenging due to high computational demands and memory requirements.
Innovation Solution
A digital circuit with synchronously-operating pixel processing elements that perform fast sieve opening or closing operations independently of image content, using a series of iterations to replace pixel values based on adjacent values, reducing the number of clock periods required for processing and enabling real-time multiple operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If repeated opening and closing operators are applied to perform sieve operations, then image segmentation accuracy is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent divides the image processing task into independent pixel-level operations that can be executed in parallel. Each pixel processing element handles a specific pixel independently, allowing simultaneous computation across the entire image. This segmentation of the processing task enables the repeated opening and closing operators to be applied without sequential delays, resolving the contradiction between segmentation accuracy and processing time.
Solution Approach 2:
The patent transitions from sequential temporal processing to spatial parallel processing by distributing pixel processing across multiple concurrent processing elements arranged in a two-dimensional array. This dimensional shift from time-based iteration to space-based parallelism allows multiple sieve operations to occur simultaneously, maintaining accuracy while dramatically reducing processing time.
2Measurement precision
If repeated opening and closing operators are applied with incrementing scale factors, then object identification accuracy is improved, but memory requirements and device complexity increase
Solution Approach 1:
The patent designs identical pixel processing elements that can perform multiple sieve operations at different scale factors. Each processing element is universally capable of executing opening and closing operators with any scale factor, eliminating the need for separate specialized hardware for each operation. This multi-functionality reduces device complexity while maintaining the ability to perform repeated operations for accurate object identification.
Solution Approach 2:
The patent uses programmable control signals to change operational parameters (scale factors, operator types) rather than requiring separate hardware configurations. By dynamically adjusting parameters through control logic, the system can perform multiple sieve operations with different settings using the same hardware structure, reducing complexity while preserving object identification accuracy.
3Adaptability or versatility
If software-based sieve processing is used, then flexibility in processing different image types is maintained, but processing speed becomes picture-dependent and insufficient for real-time applications
Solution Approach 1:
The patent replaces software-based sequential processing with hardware-based parallel processing using dedicated pixel processing elements. This substitution of computational mechanics from software interpretation to hardware execution provides both real-time speed and processing flexibility. The hardware architecture can be controlled by programmable signals to handle different image types while maintaining constant high-speed operation independent of picture content.
Data Source
AI summary
In image processing, parallel and synchronous pixel processing elements have a pixel value register, a neighbor value register, and a processor receiving adjacent pixel and neighbor values for four adjacent pixels. In a series of iterations, the neighbor value register is updated with—in one mode—the maximum and—in another mode—the minimum of: the current value; the pixel value from any adjacent pixel whose pixel value is different from the current pixel value and the neighbor value from an adjacent pixel whose pixel value is the same as the current pixel value. The pixel value is then replaced by the minimum or maximum (depending on the mode) of the current pixel and neighbor values. The modes may alternate with the number of iterations in each series remaining constant or growing.