Image Processing Segmentation for Airborne Sensor Data
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Solution Overview
Problem
Conventional image processing techniques for airborne and space sensors are inefficient due to computational intensity and are not optimized for dynamic scenes, leading to communications bottlenecks, as they are geared towards TV/movie type scenes and fail to distinguish between moving and stationary objects effectively.
Innovation Solution
An image processing system that decomposes images into background, mover, and leaner pixels, using spatial and temporal compression techniques to efficiently transmit and store data, with the system identifying and separating movers and leaners using frame differencing and filtering methods, and applying adaptive freezing to reduce data transmission rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional image compression techniques (MPEG4/H.264) are applied to airborne/space sensor data, then standardization and ease of processing are improved, but computational intensity increases and communications bottlenecks occur
Solution Approach 1:
The patent segments the image data into distinct components: background pixels, mover pixels, and leaner pixels. This segmentation allows each component to be processed and transmitted separately, reducing the overall data volume and computational load while maintaining processing efficiency through standardized methods for each segment.
Solution Approach 2:
The patent extracts and separates moving objects (movers) from the static background using frame differencing and filtering techniques. By taking out only the essential motion information and separating it from redundant background data, the system reduces data transmission requirements and avoids communications bottlenecks.
2Productivity
If iterative compression techniques are used to characterize apparent motion, then compression efficiency is improved, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent performs preliminary frame differencing and filtering operations to identify and separate mover pixels from background pixels before applying compression. This preliminary action characterizes the motion content in advance, allowing subsequent compression to focus only on essential motion data rather than processing the entire image iteratively, thereby reducing computational intensity.
Solution Approach 2:
The patent dynamically adjusts the processing approach based on the identified content: static background regions are processed differently from dynamic mover regions. This dynamic processing allows the system to apply appropriate compression levels to each region, improving overall compression efficiency without uniformly increasing computational intensity across the entire image.
3Measurement precision
If frame differencing and filtering methods are applied to identify movers, then accuracy in distinguishing moving objects is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into distinct functional stages: frame differencing to identify potential movers, filtering to separate movers from leaners, and compression to reduce data volume. This segmentation breaks down the complex task into manageable steps, improving accuracy in distinguishing movers while controlling processing complexity through modular design.
Solution Approach 2:
The patent applies different processing quality levels to different regions: high-precision frame differencing and filtering are applied only to regions containing movers, while the static background is processed with simpler methods. This local quality approach maintains high accuracy for mover identification while reducing overall processing complexity by avoiding intensive operations across the entire image.
Data Source
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AI summary
In accordance with various aspects of the disclosure, a system, a method, and computer readable medium having instructions for processing images is disclosed. For example, the method includes receiving, at an image processor, a set of images corresponding to a scene changing with time, decomposing, at the image processor, the set of images to detect static objects, leaner objects, and mover objects in the scene, the mover objects being objects that change spatial orientation in the scene with time, and compressing, using the image processor, the mover objects in the scene separately at a rate different from that of the static objects and the leaner objects for storage and/or transmission.