Composite Image Formation for Moving Objects
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Solution Overview
Problem
Current imaging systems face challenges in forming a composite image of moving objects with varying speeds and different sensor modalities, leading to image degradation and difficulties in registering images from multiple wavebands, especially when object speed is not constant and sensors have different scan rates and operating wavelengths.
Innovation Solution
The system employs motion estimating means to generate an estimate of object image motion between images and display processing means to form a composite image, using likelihood measures based on pixel intensity comparisons and noise standard deviations, allowing for registration of images from different sensors operating at various wavelengths and speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images are collected at different scan rates and wavelengths, then multi-sensor imaging capability is improved, but image registration accuracy deteriorates
Solution Approach 1:
The system uses feedback from pixel intensity comparisons between consecutive frames to generate motion estimates. These motion estimates are fed back into the image registration process to correct for differences in scan rates and wavelengths, allowing accurate composite image formation despite varying sensor characteristics
Solution Approach 2:
The system dynamically adjusts registration parameters based on detected motion between frames. By changing the spatial positioning parameters of image pixels according to calculated motion vectors, the system compensates for different scan rates and wavelengths across multiple sensors
2Device complexity
If constant speed assumption is made for image reconstruction, then processing complexity is reduced, but image quality deteriorates when object speed varies
Solution Approach 1:
The system transitions from a static constant-speed model to a dynamic motion estimation approach. Motion vectors are calculated for each frame based on pixel intensity changes, allowing the system to adapt to varying object speeds in real-time while maintaining reasonable processing complexity through efficient difference calculations
Solution Approach 2:
The system uses the image data itself to determine motion parameters. By comparing pixel intensities between consecutive frames, the system self-determines the object's motion without requiring external speed sensors or complex preprocessing, thereby maintaining low processing complexity while improving image quality
3Measurement precision
If motion estimation is performed using pixel intensity comparisons, then motion detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs motion estimation only where necessary by focusing on pixel intensity differences in regions where motion is detected. This partial action approach maintains high motion detection accuracy while reducing overall processing time by avoiding exhaustive analysis of entire images when motion is minimal
Data Source
AI summary
Apparatus for scanning a moving object includes a visible waveband sensor 12 oriented to collect a series of images of the object as it passes through a field of view 16. An image processor 14 uses the series of images to form a composite image. The image processor 14 stores image pixel data for a current image and a predecessor image in the series. It uses information in the current image and its predecessor to analyse images and derive likelihood measures indicating probabilities that current image pixels correspond to parts of the object. The image processor 14 estimates motion between the current image and its predecessor from likelihood weighted pixels. It generates the composite image from frames positioned according to respective estimates of object image motion. Image motion may alternatively be detected by a speed sensor such as a Doppler radar 200 sensing object motion directly and providing image timing signals.