Gradient Template Matching for Robust Object Recognition
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Solution Overview
Problem
Current machine vision systems struggle with object recognition in images with varying brightness and contrast, background noise, and cluttered scenes, leading to high error rates and limited applicability to specific environments.
Innovation Solution
A method and system that utilize spatial vectors indicative of image properties, including contrast magnitude and gradient, to perform transformations and convolutions with model templates for efficient object recognition, enabling robust identification under varied conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional template matching methods are used, then the system works well in controlled lighting conditions, but it fails in images with varying brightness and contrast, background noise, and cluttered scenes
Solution Approach 1:
The patent transforms the image representation by computing spatial vectors (gradients) and applying non-linear transformations to create a new parameter space. This changes the fundamental parameters used for matching from raw pixel intensities to transformed spatial characteristics, enabling the system to maintain reliability across varying lighting conditions by operating in a parameter space that is invariant to brightness and contrast changes.
Solution Approach 2:
The patent replaces traditional mechanical/image-based template matching with a computational approach using spatial vectors and transformations. Instead of directly comparing pixel intensities, the system substitutes this with gradient computation and transformation-based matching, which is more robust to environmental variations and achieves better adaptability while maintaining or improving recognition accuracy.
2Reliability
If additional components and systems are added to reduce lighting and optical drift, then object identification reliability improves, but device complexity increases
Solution Approach 1:
The patent substitutes physical/optical components designed to stabilize lighting conditions with a computational transformation approach. Instead of adding hardware to control lighting and reduce drift, the system uses spatial vector transformations that are inherently invariant to these variations, achieving the same reliability improvement without increasing device complexity.
Solution Approach 2:
The patent changes the parameter representation from raw pixel values sensitive to lighting conditions to transformed spatial vectors that are invariant to lighting and optical drift. This parameter transformation eliminates the need for additional components to stabilize conditions, as the transformation itself provides immunity to these variations.
3Ease of manufacture
If traditional object identification methods are used, then the system is simple to implement, but it produces high error rates in challenging images
Solution Approach 1:
The patent replaces simple pixel-based matching with a more sophisticated spatial vector transformation approach. While the underlying mathematics is more complex, the implementation remains computationally straightforward using standard image processing operations (gradient computation, transformation, convolution), achieving low error rates without excessive complexity.
Solution Approach 2:
The patent transforms the matching parameters from direct pixel intensity comparison to spatial vector-based representation with non-linear transformations. This parameter change fundamentally improves recognition accuracy by capturing edge and gradient information that is more robust to image variations, while the computational steps remain implementable with standard processing techniques.
Data Source
AI summary
Systems and methods for performing object identification via template matching. An example method includes obtaining one or more images of a target object and determining spatial vectors for pixels of the one or more images. The spatial vectors include a metric indicative of spatial differences in image properties of the pixels. The method then performs a transformation on the spatial vectors and determines a mapped pixel value for each pixel of the images. The method determines a distance transform from the mapped pixel values and performs a convolution between the distance transform and a model to generate a score map. The method further identifies peaks indicative of a potential target object match from the score map, and then determines target object matches from the one or more peaks of the score map. Finally, the method includes providing an indication of the target object matches to a user or system.