Thermal Imaging Camera Image Stabilization via Translation Vectors
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Solution Overview
Problem
Handheld thermal imaging cameras face challenges in accurately aiming and tracking small objects over a lengthy period due to uncontrolled movement, making long-term measurements difficult.
Innovation Solution
An image processing method that uses feature and pattern recognition algorithms to determine a translation vector between IR images, allowing for the compensation of camera movement and enabling continuous tracking of objects across multiple images by selecting corresponding image regions and calculating pixel correspondences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If handheld thermal imaging camera is used for long-term measurements, then portability and ease of operation are improved, but measurement precision and stability deteriorate due to uncontrolled camera movement
Solution Approach 1:
The patent replaces mechanical stabilization systems (such as tripods or gimbals) with a software-based image processing method. By using feature extraction, pattern recognition, and translation vector calculation, the system digitally compensates for camera movement, allowing handheld operation while maintaining measurement precision.
Solution Approach 2:
The patent changes the parameter of image position by calculating translation vectors that describe the displacement between consecutive images. By applying these parameter changes to register images, the system compensates for camera movement and maintains accurate object tracking throughout the measurement period.
2Reliability
If image processing is performed on sequences of IR images, then object tracking capability is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by extracting features from each image and pre-calculating translation vectors during the image acquisition process. This allows the system to maintain a library of registered images and tracking data, reducing processing time when analyzing object movement patterns or generating final measurement results.
Solution Approach 2:
The patent segments the image processing task into distinct steps: feature extraction, translation vector calculation, and image registration. By dividing the processing into manageable segments that can be performed incrementally on each image in the sequence, the system reduces overall computational burden while maintaining tracking reliability.
3Measurement precision
If feature extraction and pattern recognition algorithms are used to determine translation vectors, then image registration accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service by using the thermal imaging camera's own image data to generate the translation vectors needed for registration. The system extracts features from the captured images themselves and uses these features to calculate alignment parameters, eliminating the need for external calibration targets or additional hardware components.
Solution Approach 2:
The patent introduces translation vectors as an intermediary element that mediates between raw image data and registered images. These vectors serve as a computational bridge, translating the spatial relationship between consecutive images into actionable alignment parameters that simplify the registration process.
Data Source
AI summary
For a thermal imaging camera (1), features are extracted from a series of at least two infrared images (10, 11) or visible images (19, 20) associated therewith by a feature analysis, and an optimal correspondence between features extracted from the images (9, 10, 19, 20) is determined, and a translation vector (18) that relates the pixels of the first infrared image (10) to pixels of the second infrared image (11) is determined for the image positions (16, 17) of the corresponding features.