Gradient-Magnitude Template Matching in Cluttered Vision Scenes
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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 controlled environments.
Innovation Solution
A method and system that utilize spatial vectors to determine image properties, perform transformations, and convolve with model templates to identify potential object matches, enabling robust object recognition across diverse conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional template matching methods are used, then object identification can be performed, but the system fails under varying lighting conditions, background noise, and cluttered scenes
Solution Approach 1:
The patent transforms the image data by converting pixel intensity values into gradient magnitude values, which are then used to create a distance transform. This parameter transformation makes the representation invariant to lighting conditions and background noise, allowing reliable object identification across diverse environments without requiring controlled lighting or simple backgrounds.
2Measurement precision
If controlled lighting and contrast conditions are enforced, then object recognition accuracy improves, but the system becomes limited to specific applications and environments
Solution Approach 1:
Instead of requiring controlled lighting conditions as a prerequisite for accurate recognition, the patent converts the harmful effect of varying lighting into a beneficial feature by using gradient magnitude, which is invariant to illumination changes. This allows the system to achieve high recognition accuracy in uncontrolled environments where traditional methods would fail.
3Reliability
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 replaces complex mechanical and optical systems designed to control lighting and optical drift with a computational approach using gradient magnitude and distance transform. This software-based solution achieves the same reliability goal without requiring additional hardware components, thereby reducing overall system complexity.
Data Source
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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.