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Published on: April 16, 2017
On using the CAM concept for parametric curve extraction.
Summary
A novel parallel algorithm utilizing content-addressable memory (CAM) efficiently extracts parametric curves from 2-D images using the Hough transform (HT). This approach enables real-time shape extraction, promising advancements in pattern recognition platforms.
Area of Science:
- Computer Vision
- Image Processing
- Parallel Algorithms
Background:
- Parametric curve extraction from 2-D images is crucial for pattern recognition.
- Traditional Hough Transform (HT) methods can be computationally intensive.
- Content-Addressable Memory (CAM) offers potential for high-speed parallel processing.
Discussion:
- A parallel algorithm based on the Hough Transform (HT) is proposed for parametric curve extraction in 2-D image spaces.
- Content-Addressable Memory (CAM) is employed as the core processing unit, enabling efficient voting, thresholding, and peak extraction.
- A weighted Hough Transform (WHT) algorithm is introduced to mitigate quantization errors through weighted voting.
Key Insights:
- Simulated results demonstrate effective circular shape extraction.
- The CAM-based HT achieves efficient execution of voting, thresholding, and 3-D peak extraction.
- The proposed weighted Hough Transform (WHT) reduces quantization errors.
Outlook:
- Experimental results show real-time shape extraction is achievable for 256x256 images with minimal hardware.
- CAM-based HT presents a promising direction for next-generation pattern recognition systems.
- This approach facilitates efficient and rapid analysis of image data for complex shape identification.
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