Date of Award


Degree Name

Doctor of Philosophy


Electrical and Computer Engineering

First Advisor

Gupta, Lalit


This dissertation focuses on two distinct but related problems involving detection in multiple images. The first problem focuses on the accurate detection of runways by fusing Synthetic Vision System (SVS) and Enhanced Vision System (EVS) images. A novel procedure is developed to accurately detect runways and horizons and also enhance runway surrounding areas by fusing enhanced vision system (EVS) and synthetic vision system (SVS) images of the runway while an aircraft is landing. Because the EVS and SVS frames are not aligned, a registration step is introduced to align the EVS and SVS images prior to fusion. The most notable feature of the registration procedure is that it is guided by the information extracted from the weather-invariant SVS images. Four fusion rules based on combining Discrete Wavelet Transform (DWT) sub-bands are implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and also on image pairs containing simulated EVS images with varying levels of turbulence. The subjective and objective evaluations reveal that runways and horizons can be detected accurately even in poor visibility conditions. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. Another notable feature is that the entire procedure is autonomous throughout the landing sequence irrespective of the weather conditions. Given the excellent fusion results and the autonomous feature, it can be concluded that the fusion procedure developed is quite promising for incorporation into head-up displays (HUDs) to assist pilots in safely landing aircrafts in varying weather conditions. The second problem focuses on the blind detection of hidden messages that are embedded in images using various steganography methods. A new steganalysis strategy is introduced to blindly detect hidden messages that have been embedded in JPEG images using various steganography techniques. The key contribution is the formulation of a multi-domain feature extraction, ranking, and selection strategy to improve the steganalysis performance. The multi-domain features are statistical measures extracted from DWT, muti-wavelet (MWT), and slantlet (SLT) transforms. Feature ranking and selection is based on evaluating the performance of each feature independently and combining the best uncorrelated features. The resulting feature set is used in conjunction with discriminant analysis and support vector classifiers to detect the presence/absence of hidden messages in images. Numerous experiments are conducted to demonstrate the improved performance of the new steganalysis strategy over existing steganalysis methods.




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