Tracking visual targets in an unconstrained environment is challenging due to variations in illumination, scale, occlusion, and motion blur, for example. Many video applications that utilize particle filter-based visual target trackers require tracking of visual targets under varying illuminations. Similarity measures and likelihood estimation strongly influence the performance of particle filters. In this paper, we propose a novel likelihood estimator that has been combined with other state-of-the-art particle filtering-based tracking techniques to accommodate varying illumination by predicting changes in the illumination intensity and direction of the illumination. Moreover, an enhanced update strategy for the template dictionary is used along with a sparse representation model to solve the problem of drift due to appearance changes during tracking. The proposed algorithm has been evaluated using various particle-filter-based tracking algorithms on scenes from public data sets and using our gesture data set, which includes variations in illumination. Using the proposed model, the algorithms perform up to 20% better on sequences for which variations in illumination are dominant. We carried systematic experiments to evaluate the robustness of the proposed algorithm on video sequences with illumination variations, as well as other variations. Furthermore, in sequences that include variations in illumination, our likelihood model usually performs better than the default tracker likelihood model.

A perceptual image fusion method is proposed that employs explicit luminance and contrast masking models. These models are combined to give the perceptual importance of each coefficient produced by the dual-tree complex wavelet transform of each input image. This combined model of perceptual importance is used to select which coefficients are retained and furthermore to determine how to present the retained information in the most effective way. This paper is the first to give a principled approach to image fusion from a perceptual perspective. Furthermore, the proposed method is shown to give improved quantitative and qualitative results compared with previously developed methods.

A novel adaptation of the two dimensional Homomorphic filter is introduced using the Dual Tree Complex Wavelet Transform for improved illumination invariant processing. The Homomorphic filter is conventionally implemented within the log-Fourier domain using an isotropic high-pass filter based on the assumption that the illumination signal occupies low spatial frequencies. In this case however, low frequency structural reflectance content will be incorrectly attenuated. Our method implements the Homomorphic filter using the DT-CWT and exploits the property of cross scale persistence (for structural content) to generate a filter that retains cross scale content and therefore reduces incorrect attenuation of structural reflectance content.

This work presents a new digital communications receiver that requires only partial knowledge of the channel state information (CSI). Generally speaking, the CSI is composed of real and imaginary components, which can be expressed as h = |h| ejθ, where |h| corresponds to the channel attenuation and θ is the phase shift. In the proposed receiver, only |h| is required to detect the transmitted symbols with low probability of error. The proposed receiver can be used to increase the spectral efficiency of most digital communications receivers and/or reduce their error probability.

Accurate estimation of the contrast sensitivity of the human visual system is crucial for perceptually based image processing in applications such as compression, fusion and denoising. Conventional contrast sensitivity functions (CSFs) have been obtained using fixed-sized Gabor functions. However, the basis functions of multiresolution decompositions such as wavelets often resemble Gabor functions but are of variable size and shape. Therefore to use the conventional CSFs in such cases is not appropriate. We have therefore conducted a set of psychophysical tests in order to obtain the CSF for a range of multiresolution transforms: the discrete wavelet transform, the steerable pyramid, the dual-tree complex wavelet transform, and the curvelet transform. These measures were obtained using contrast variation of each transforms' basis functions in a 2AFC experiment combined with an adapted version of the QUEST psychometric function method. The results enable future image processing applications that exploit these transforms such as signal fusion, superresolution processing, denoising and motion estimation, to be perceptually optimized in a principled fashion. The results are compared with an existing vision model (HDR-VDP2) and are used to show quantitative improvements within a denoising application compared with using conventional CSF values.

This paper presents a hybrid automatic repeat request (HARQ) system using a parity error checking (PEC) technique with low processing power requirements. The proposed technique is applied to extended turbo product codes (TPC) where the parity check bits used for extending the component codes of TPC, are exploited to replace the conventional cyclic redundancy check (CRC) error detection in HARQ systems. Consequently, the required processing power can be reduced substantially while the throughput is almost unchanged for long TPC codes, or increased for short TPC codes. The proposed PEC technique is also compared to the state-of-the-art syndrome error checking (SEC) as well as conventional CRC. Monte Carlo simulation results reveal that PEC- HARQ can provide equivalent throughput to SEC-HARQ and higher throughput than CR-HARQ systems. Moreover, numerical results show that the PEC technique has lower computational complexity than both SEC and CRC error detection. In particular cases, the complexity of the proposed system is reduced by more than 50% as compared to the state- of-the-art, and by more than 80% when compared to the CRC error detection.

In this paper, a motion and appearance saliency combined detection framework for hierarchical representation of targets from groups to individuals in crowded scenes of surveillance videos is proposed. Big data analytic solutions within surveillance often require compact representations for target (s)- of-interest that allows simultaneous micro (individualistic) and macro (holistic) levels of inference on visual information. The target detection method proposed in this paper combines the estimation of motion saliency through dynamic texture (DT) based Gaussian Mixture Model (GMM) and appearance saliency through person detection using combined Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) feature sets. The saliency models are tightly integrated such that initially motion information is used to update and improve detection within an appearance framework, which in turn compliments the motion segmentation for accurate localization of people in groups. The improved people detection thus proposed is capable of eliminating false detections and can accurately delineate individuals within groups. The quantitative and qualitative results of experiments conducted on benchmark datasets have proven the validity and robustness of the proposed technique.

This paper presents an adaptive power control scheme for hybrid automatic repeat request (HARQ) systems with no channel state information feedback. The power control process is developed by noting that the throughput of particular HARQ systems may exhibit a staircase behavior. In such scenarios, the throughput remains fixed for a wide range of signal-to-noise ratios (SNRs). Consequently, the transmit power can be reduced significantly while the throughput remains almost unchanged. The obtained results reveal that invoking power optimization algorithms can achieve a significant power saving of about 80% for particular scenarios. The system considered in this work is a truncated HARQ with turbo product codes (TPC). Chase combining is also used to combine the retransmitted packets with the original transmission. HARQ acknowledgment/negative acknowledgment (ACK/NACK) feedback is used to estimate the packet error rate from which the system throughput is computed.

This paper presents a hybrid automatic repeat request (HARQ) system using turbo product codes (TPC). The inherent word-error detection capability of TPC is exploited to replace the conventional cyclic redundancy check (CRC) used for packet error detection in conventional HARQ systems. Therefore, TPC are used for joint bit error correction and packet error detection. Consequently, the HARQ system efficiency is improved by increasing the system throughput when short packets are transmitted or by reducing the computational complexity/delay when the packets transmitted are long. Monte Carlo simulation results reveal that the CRC-free TPC-HARQ system consistently provides equivalent or higher throughput than CRC-based HARQ systems. Moreover, numerical results show that TPC self-detection has lower computational complexity than CRC detection, particularly for TPCs with high code rates. In particular scenarios, the relative complexity of the self-detection approach with respect to popular CRC techniques is about 0.3%.

Wavelet shrinkage is a standard technique for denoising natural images. Originally proposed for univariate shrinkage in the Discrete Wavelet Transform (DWT) domain, it has since been optimised through the exploitation of translationally invariant wavelet decompositions such as the Dual-Tree Complex Wavelet Transform (DT-CWT) alongside bivariate analysis techniques that condition the shrinkage on spatially related coefficients across neighbouring scales. These more recent techniques have denoised the real and imaginary components of the DT-CWT coefficients separately. Processing real and imaginary components separately has been found to lead to an increase in the phase noise of the transform which in turn affects denoising performance. On this basis, the work presented in this paper offers improved denoising performance through modelling the bivariate distribution of the coefficient magnitudes. The results were compared to the current state of the art non-local means denoising technique BM3D, showing clear subjective improvements, through the retention of high frequency structural and textural information. The paper also compares objective measures, using both PSNR and the more perceptually valid structural similarity measure (SSIM). Whereas PSNR results were slightly below those for BM3D, those for SSIM showed closer correlation with subjective assessment, indicating improvements over BM3D for most noise levels on the images tested.

This paper presents a novel DCT-based watermarking method for image authentication. In this method a watermark in the form of a visually meaningful binary pattern is embedded for the purpose of tamper detection. The method embeds one watermark bit in each DCT block by shifting a randomly selected coefficient to have a mapped value, in a coefficient-binary-mapping function, that is identical to the watermark bit. The method was tested under different attacks and was found to provide good detection performance while maintaining the quality of the watermarked image.

Multiple reference motion compensated prediction achieves significant prediction gains, but at the expense of a significant increase in computational complexity. This paper investigates and compares the performance of a number of fast motion estimation techniques when extended to work for multiple reference prediction. In general, algorithms that are designed to exploit the properties of the long-term block motion field, were found to provide superior performance and better compromise between prediction quality and computational complexity.

Block-matching motion estimation (BMME) can be formulated as a 2-D constrained minimization problem. This problem can, therefore, be solved with reduced complexity using optimization techniques. This paper proposes a novel fast BMME algorithm called the simplex minimization search (SMS). The algorithm is based on the simplex minimization (SM) optimization method. The initialization procedure, termination criterion, and constraints on the independent variables of the search are designed to take advantage of the characteristics of the BMME problem and the properties of the block motion fields of typical video sequences. Simulation results show that the proposed algorithm outperforms other fast BMME algorithms, providing better prediction quality, a smoother motion field, and higher speed-up ratio. This paper also investigates the properties of the multiple-reference (MR) block motion field. Guided by the results of this investigation, the paper extends the SMS algorithm to the MR case. Three MR SMS algorithms are proposed, providing different degrees of compromise between prediction quality and computational complexity. Simulation results using 50 reference frames indicate that the proposed MR algorithms have a computational complexity comparable to that of single-reference full-search while still maintaining the prediction gain of MR motion estimation.

This paper investigates the performance of different temporal concealment techniques when incorporated within a long-term motion compensated video codec. In particular, the paper finds a combination of techniques that best recovers the spatial-temporal components of a damaged long-term motion vector. It is demonstrated also that accurate recovery of the spatial components of the damaged motion vector is, in general, more important and more complex than the recovery of the temporal component. Such findings are explained in view of the properties of the long-term block motion field. The paper also, proposes a novel long-term multihypothesis temporal concealment technique where the damaged block is concealed using the average of a number of candidates from the several reference frames available at the decoder. The superior performance of this technique is demonstrated over a range of block error rates.

This paper investigates the properties of the multiple-reference block motion field. Guided by the results of this investigation, the paper proposes three fast multiple-reference block matching motion estimation algorithms. The proposed algorithms are extensions of the single-reference simplex minimisation search (SMS) algorithm. The algorithms provide different degrees of compromise between prediction quality and computational complexity. Simulation results using a multi-frame memory of 50 frames indicate that the proposed multiple-reference SMS algorithms have a computational complexity comparable to that of single-reference full-search while still maintaining the prediction gain of multiple-reference motion estimation.