This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been accomplished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR sequences, whereas the VIZ video showed the worst tracking performance due to higher levels of clutter. However, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to accurately assess how fusion affects the performance of the tracker.

The purpose of the Applied Multi-dimensional Fusion Project is to investigate the benefits that data fusion and related techniques may bring to future military Intelligence Surveillance Target Acquisition and Reconnaissance systems. In the course of this work, it is intended to show the practical application of some of the best multi-dimensional fusion research in the UK. This paper highlights the work done in the area of multi-spectral synthetic data generation, super-resolution, joint fusion and blind image restoration, multi-resolution target detection and identification and assessment measures for fusion. The paper also delves into the future aspirations of the work to look further at the use of hyper-spectral data and hyper-spectral fusion. The paper presents a wide work base in multi-dimensional fusion that is brought together through the use of common synthetic data, posing real-life problems faced in the theatre of war. Work done to date has produced practical pertinent research products with direct applicability to the problems posed.

In this paper, we present a novel multimodal image fusion algorithm in ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from given regions using the Piella fusion metric to maximise the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and improvement over other state-of-the-art algorithms

In this paper we present a novel fusion method in ICA domain. The method uses nonlinear shrinkage of the coefficients in ICA domain to determine the ICA coefficients to be used in the fused image reconstruction, so that the noise transferred from input images into the fused output is minimized. Experimental results have shown that it has improved performance in the presence of noise, compared to the basic ICA algorithm. Compared to standard multiresolutional fusion methods, the noise in the fused image is visually less annoying, while the important detail is still adequately represented. The proposed method exhibits considerably higher fusion performance, measured by Piella and Petrovic fusion metric, than the basic ICA algorithm and improvement over other state-of-the-art algorithms.

In this paper, we present a novel region-based multimodal image fusion algorithm in the ICA domain. It uses segmentation to determine the most important regions in the input images and consequently fuses the ICA coefficients from the given regions. The proposed method exhibits considerably higher performance than the basic ICA algorithm and shows improvement over other state-of-the-art algorithms

We present a novel scheme that is able to combine digital watermarking and content authentication of digital audio. The embedding of additional data is performed in discrete wavelet domain. Watermark embedding is done by frequency hopping method, while the additional authentication data is hidden using the LSB modulation. The perceptual transparency is achieved using the frequency masking property of the HAS. The scheme obtains high robustness against standard watermark attacks and localizes the accurately tampered parts of the audio clip.

Standard spread spectrum audio watermarking algorithms offer BER unacceptable for reliable transmission of data. Causes of unreliable watermark detection are often attacks that distort the watermarked audio in fading-like manner, disabling the correlation-based detectors to extract data. In this paper, consideration of capacity of the audio watermark channel in the presence of fading-like distortion is performed. It is shown that turbo codes offer convenient trade-off between capacity of the watermark channel and BER due to large coding gain in fading environment. Test results proved a large advantage of the proposed algorithm over standard detection as robustness is significantly increased for a given watermark data rate.