Edge-Based Template Matching for Efficient Object Detection
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Solution Overview
Problem
Existing template matching techniques face challenges in efficiently and accurately matching objects with few feature points, simple shapes, symmetry, or repetitive patterns, and are computationally expensive, especially under conditions of rotation, scaling, and illumination changes.
Innovation Solution
An edge-based matching technique that uses gradient-based edge detection, quantized angular templates, and coarse-to-fine matching processes to efficiently locate and orient objects in images, incorporating pre-filtering, down-sampling, and adaptive thresholding to reduce noise and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel template matching is used, then matching accuracy is improved, but computational complexity increases to O(W2×N2)
Solution Approach 1:
The patent segments the template and search image into edge maps by detecting edges first, then performs matching only on edge pixels rather than all pixels. This segmentation reduces the number of comparison points from W×N to approximately the number of edge pixels, significantly lowering computational complexity while preserving matching accuracy for edge-based objects.
Solution Approach 2:
The patent extracts only the edge information from the template and search image using edge detection algorithms, discarding non-edge pixel data. This extraction process creates simplified representations that contain the essential shape information needed for matching, reducing computational load while maintaining accuracy for objects defined by their edges.
2Measurement precision
If template matching is performed for arbitrary orientation by rotating the model image in every possible orientation, then matching accuracy is improved, but computational expense increases significantly
Solution Approach 1:
The patent performs preliminary edge detection on the template image to create an edge map before the actual matching process. This preliminary action identifies all edge pixels and their orientations in advance, allowing the algorithm to efficiently test multiple orientations by simply rotating the edge map rather than re-detecting edges for each orientation, thus reducing computational expense.
Solution Approach 2:
The patent implements dynamic orientation testing by rotating the template edge map at multiple discrete orientations (e.g., 0°, 30°, 60°, 90°) and selecting the orientation that produces the best match. This dynamic approach allows the system to adapt to objects at arbitrary orientations while avoiding the computational burden of continuous rotation by using a discrete set of test orientations.
3Measurement precision
If feature point based template matching is used, then matching accuracy is improved, but reliability decreases for objects with few feature points, symmetry, or repetitive patterns
Solution Approach 1:
The patent extracts global edge information from the template and search image, creating edge maps that represent the complete shape and structure of objects. This extraction of global information provides sufficient constraints for matching even when local feature points are scarce, symmetric, or repetitive, thereby improving reliability across diverse object types.
Solution Approach 2:
The patent transitions from relying solely on local feature point coordinates (2D positions) to utilizing edge pixel information that includes orientation data. By adding orientation as an additional dimension of information for each edge pixel, the system gains more discriminatory power for matching, making it more reliable for symmetric and repetitive patterns where traditional feature points fail.
4Productivity
If edge-based matching is implemented, then computational efficiency is improved, but robustness to noise and imaging imperfections may worsen
Solution Approach 1:
The patent applies noise filtering and preprocessing to the search image before performing edge detection and matching. This preliminary action removes or reduces noise artifacts that could create spurious edge detections, ensuring that the subsequent edge-based matching operates on cleaner data and maintains robustness while preserving computational efficiency.
Data Source
AI summary
A method for image processing includes determining edge pixels of a model image and determining features for the edge pixels of the model image. The image processing includes determining edge pixels of an input image and determining features for the edge pixels of the input image. The system matches the features of the model image with the features of the input image to determine candidate locations of an object within the input image.