Model-Based Image Processing for EO/IR Target Identification
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Solution Overview
Problem
Traditional combat identification algorithms are ineffective for processing Electro-Optical/Infrared (EO/IR) sensor data, which is crucial for detecting and classifying objects in military and other applications, especially in outdoor conditions with noisy or degraded information.
Innovation Solution
A system combining pose determination, EO/IR sensor data, and novel computer graphics rendering techniques, utilizing a model-based image processing system with modules for orientation and distance extraction, and target identification, capable of operating in noisy conditions and incorporating synthetic imagery for improved image matching and shadow manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional combat identification algorithms are used, then processing of conventional sensor data is effective, but processing of EO/IR sensor data is ineffective
Solution Approach 1:
The patent transforms EO/IR sensor data by converting it into a format that resembles conventional optical imagery through image processing techniques. This parameter transformation allows traditional combat identification algorithms to effectively process EO/IR data by changing the data representation while preserving the essential target characteristics needed for recognition.
Solution Approach 2:
The patent introduces an intermediate image processing stage that acts as a mediator between the EO/IR sensor data and the traditional combat identification algorithms. This intermediary component processes the raw EO/IR data into a format compatible with conventional algorithms, enabling effective target identification without requiring complete algorithm redesign.
2Productivity
If image processing is performed in noisy or degraded conditions, then target detection must proceed with incomplete information, but processing quality deteriorates
Solution Approach 1:
The patent applies preliminary image processing and enhancement techniques to EO/IR data before it enters the main identification pipeline. By pre-processing the noisy data to improve its quality and characteristics in advance, the system enables faster and more accurate target identification even when starting with degraded information.
3Reliability
If shadows are present in outdoor conditions, then target identification becomes more difficult, but shadows can be manipulated to improve recognition
Solution Approach 1:
The patent converts the harmful effect of shadows into a beneficial feature for target identification. By detecting and analyzing shadow patterns in EO/IR imagery, the system uses shadows as additional cues for target recognition, transforming what was previously a source of confusion into a useful discriminative feature that enhances identification reliability.
Data Source
AI summary
A system for performing object identification combines pose determination, EO/IR sensor data, and novel computer graphics rendering techniques. A first module extracts the orientation and distance of a target in a truth chip given that the target type is known. A second is a module identifies the vehicle within a truth chip given the known distance and elevation angle from camera to target. Image matching is based on synthetic image and truth chip image comparison, where the synthetic image is rotated and moved through a 3-Dimensional space. To limit the search space, it is assumed that the object is positioned on relatively flat ground and that the camera roll angle stays near zero. This leaves three dimensions of motion (distance, heading, and pitch angle) to define the space in which the synthetic target is moved. A graphical user interface (GUI) front end allows the user to manually adjust the orientation of the target within the synthetic images. The system also includes the generation of shadows and allows the user to manipulate the sun angle to approximate the lighting conditions of the test range in the provided video.