Road safety remains a critical challenge, with human errors, such as drowsiness and distraction as contributing factors. Physiological indicators, such as heart rate (HR) and respiratory rate (RR), provide insights into understanding the driver state but are difficult to acquire in real-world driving scenarios due to the constantly changing driver pose and complex ambient environment. To tackle this challenge, a noncontact cardiorespiratory monitoring system using frequency modulated continuous wave (FMCW) radar is proposed. Unlike optical or acoustic systems, the use of radar can minimize interference from the ambient environment. However, its relatively low spatial resolution, driver motion, and variability in human radar cross section (RCS) present significant signal processing challenges, especially for target tracking. To address these issues, a novel target tracking algorithm and a reference-free signal quality index (SQI) based on phase continuity are proposed, aimed at significantly improving the consistency and reliability of target tracking and overall signal quality. A performance evaluation using a driving simulator and synchronized electrocardiography (ECG) recordings for ground truth demonstrates that our proposed method achieves a mean absolute error (MAE) of 1.65 beats per minute (BPM) for HR, 0.79 respirations per minute (RPM) for RR, and a relative accuracy of 97.83% and 95.06%, respectively. These results highlight the potential of the proposed system to enhance driver monitoring by providing accurate and continuous HR and RR measurement, contributing to the early detection of adverse driver states.

The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction algorithm based on the surface normals of Gabor-wavelet filtered depth maps is utilised and, then, a set of spherical patches and curves are localised over the nasal region to provide the feature descriptors. The last step applies a genetic algorithm-based feature selector to detect the most stable patches and curves over different facial expressions. The algorithm provides the highest reported nasal region-based recognition ranks on the FRGC, Bosphorus and BU-3DFE datasets. The results are comparable with, and in many cases better than, many state-of-the-art 3D face recognition algorithms, which use the whole facial domain. The proposed method does not rely on sophisticated alignment or denoising steps, is very robust when only one sample per subject is used in the gallery, and does not require a training step for the landmarking algorithm.

This study proposes an algorithm is proposed to quantitatively evaluate the performance of three‐dimensional (3D) holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (i) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (ii) shows which parts of the face are more vulnerable to noise and require further post‐processing after data acquisition; and (iii) compares the performance of three different categories of recognition algorithms: training‐free matching‐based, subspace projection‐based and training‐based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Moreover, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training‐based (including the subspace projection) methods.

In the past decade, expression variations have been one of the most challenging sources of variability in 3D face recognition, especially for scenarios where there are a large number of face samples to discriminate between. In this paper, an expression robust reject or is proposed that first robustly locates landmarks on the relatively stable structure of the nose and its environs, termed the cheek/nose region. Then, by defining curves connecting the landmarks, a small set of features (4 curves with only 15 points each) on the cheek/nose surface are selected using the Bosphorus database. The resulting reject or, which can quickly eliminate a large number of candidates at an early stage, is further evaluated on the FRGC database for both the identification and verification scenarios. The classification performance using only 60 points from 4 curves shows the effectiveness of this efficient expression robust rejector.

In this letter, a new approach to tracking the ionospheric-storm enhancements known as tongues of ionization (TOIs) is presented. In contrast to conventional model-based methods, the technique works by applying block-based motion estimation methods directly to ionospheric total electron content image sequences. Solutions to the particular problems posed by the low image resolution and the nonrigid motion of the TOIs are proposed. Experimental results for an image sequence covering the 2003 ldquoHalloween stormrdquo establish that the approach produces estimated motion fields that show a good correspondence with the observable motion when the interframe differences are not extreme.

Connected operators are an important tool for the analysis of greyscale images. In extending them to colour and other vector images there are a number of issues that must be addressed, including the definition of extrema, region merging criterion and the preservation of idempotence. This paper reviews the recently proposed approaches to these problems and considers some of the choices which must be made in the design of effective colour connective operators. An evaluation of the noise reduction performance resulting from these choices is presented. The use of the colour connected sieves in conjunction with the watershed transform for image segmentation is also investigated.

Interpolation methods are widely used in geoscience applications to reconstruct multivariate data from irregular samples. This paper describes a quantitative methodology for assessing the performance of various state-of-the-art interpolation methods. The methodology consists of simulation-validation and cross-validation using simulated and real data respectively, and has recently been applied to study the reconstruction of total electron content maps of the ionosphere. These two approaches are described and a study of the various artefacts associated with different interpolation methods also presented, including their origins and typical locations. Finally, the use of the statistical moments of error histograms as a method of evaluating techniques for biases and skew is described, as well as providing confidence bounds on error values. The methodology and artefact analysis should be of use to anyone who uses multivariate interpolation methods.

The identification and tracking of atmospheric dust is important to many disciplines due to its impact on the climate and ecological System. Sensors on-board existing imaging satellites such as MSG and CALIPSO provide good horizontal and vertical resolution, respectively, and therefore the resolution of any dust products derived solely from one sensor will be limited in the same manner as the sensor. We propose a new method of identifying dust distributions in the atmosphere using data from two separate satellite sources, the SEVIRI on-board MSG and the CALIPSO lidar. The approach employs a supervised classification method using texture data derived from a bank of Gabor filters. Once the dust has been identified, the SEVIRI data is augmented with vertical CALIPSO data to produce a 2½D dust cloud top height distribution.

Maps of the total electron content (TEC) of the ionosphere can be reconstructed using data extracted from global positioning system (GPS) signals. For historic and other sparse data sets, the reconstruction of TEC images is often performed using a multivariate interpolation technique. Although there are many interpolation methods available, only a limited number, for example kriging, have been applied to TEC data. This paper presents a quantitative comparison of various commonly used algorithms for scattered-data interpolation over a range of sparsi- ties. Techniques evaluated include a relatively new approach called Adaptive Normalized Convolution (ANC) that has not previously been applied to ionospheric reconstruction. The proposed evaluation scheme employs a quantitative methodology applied to both simulated and real TEC data. Results show that, although the performance of kriging is good in many cases, it is several times worse than the best performing techniques at some sparsities. Natural-neighbor interpolation has a better overall performance than kriging for both simulated and TEC data. Although its performance is a few percent worse than other methods for the simulated data, ANC produces the best performance for the TEC reconstructions.

Improved coding of atoms in image compression by matching pursuits (MP) after a wavelet decomposition is achieved. The positions of atoms in the wavelet sub-bands are communicated by run length coding. The MERGE coding scheme replaces individual coding of any alphabet of symbols by dividing the symbols into groups and run length coding their positions. It is shown theoretically that the use of MERGE with an efficient run length coder approaches the theoretical entropy of an alphabet of symbols. The embedded nature of MERGE makes it a near-ideal coding scheme for the amplitudes of atoms. An additional layer of MERGE coding for the basis indices of atoms approaches the theoretical optimum and is superior to either separable or combined Huffman coding.

Distributed source coding (DSC) has recently been considered as an efficient approach to data compression in wireless sensor networks (WSN). Using this coding method multiple sensor nodes compress their correlated observations without inter-node communications. Therefore energy and bandwidth can be efficiently saved. In this paper, we investigate a random-binning based DSC scheme for remote source estimation in WSN and its performance of estimated signal to distortion ratio (SDR). With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption of the DSC scheme. We further propose a novel energy-aware transmission protocol for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption, by adapting the source coding and transmission parameters to the network conditions. Simulations validate the energy efficiency of the proposed adaptive transmission protocol.

Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach

A new color edge detector based on vector differences is proposed. The basic technique gives as its output the maximum distance between the vectors within a mask. When applied to scalar-valued images, the method reduces to the classic morphological gradient. The technique is relatively computationally efficient and can also be readily applied to other vector-valued images. To improve the performance in the presence of noise, a novel pairwise outlier rejection scheme is employed. A quantitative evaluation using Pratt's figure of merit shows the new technique to outperform other recently proposed color edge detectors. In addition, application to real images demonstrates the approach to be highly effective despite its low complexity.

Morphological scale-spaces have become an important tool for analysing greyscale images. However, their extension to colour images has proven elusive until recently. In this paper an original evaluation of two recently proposed colour sieves is presented, both algorithmically and in terms of their computational and segmentation performance. A new colour sieve structure is also proposed, motivated by the relative advantages of the two sieves previously studied. A quantitative evaluation of the segmentation performance using a set of images with human ground truth from the Berkeley dataset shows the new method to produce the best segmentation performance. 1

Methods of reducing the complexity of the matching pursuits algorithm with minimal loss of fidelity when coding displaced frame difference (DFD) images in video compression are investigated. A full search using 2D basis functions is used as a benchmark. The use of separable 1D bases greatly reduces the complexity, and significant further reductions are achieved by using only a 1D inner product search to locate the atom position, followed by a further 1D inner product search in the opposite direction to identify the second 1D basis function. To avoid ignoring significant structures orthogonal to the search direction, it is proposed to alternate the initial search direction between horizontal and vertical scanning. This produces a modest increase in distortion compared to the full 2D search, with a complexity reduction in excess of an order of magnitude.

A new morphological gradient operator for colour images is introduced that can be viewed as a direct extension of the well known morphological gradient. In this approach, each pixel is considered as multivariate data and its output is the maximum distance between any two points within a structuring element, determined by a norm. In contrast with other nonlinear schemes, this approach reduces to the morphological gradient for single channel images. It is also computationally efficient and responds well to step edges. To overcome, any sensitivity to noise a robust colour morphological gradient operator is proposed that rejects outlying vector pairs before determining the maximum distance. Results show the effectiveness of the techniques.

In video compression image noise degrades the overall compression performance. Reducing the noise content can produce a more compressible sequence, ideally with minimal effect on the visual quality. This paper presents a new approach to noise reduction using sliding window spatio-temporal attribute morphology filters with both area and power attributes. A combined spatial and spatio-temporal algorithm is proposed that produces a better overall peak signal to noise ratio (PSNR) performance than either spatial or spatio-temporal filtering individually. The compression benefits of such an approach are also demonstrated.

A novel technique for motion description encoding is proposed. The method is based on a vector list and a mapping, each of which is losslessly encoded using variety of algorithms. Results show that in comparison to H.263, the best performing algorithms offer a significant reduction in the number of motion bits required, for motion fields with relatively few unique vectors. The bits saving are particularly important for low bit rate coding applications.

A multi-scale texture segmentation algorithm for SAR images based on the discrete wavelet transform is presented. Responses from different sub-bands are used to form a feature vector for each pixel position that is the input to the classification scheme. To further improve the classification results, the tree-structured wavelet packet transform is used to automatically identify a suitable sub-set of elements from the feature vector. Results show that this approach is effective for both the redundant and non-redundant wavelet transforms.

A technique for computing the field of short-term glacier surface motion on a local scale is presented. Time-lapsed image negatives, digitized to a high resolution, provide the raw data for the three-stage technique. First, cross-correlation is used to establish a number of candidate displacement vectors for a series of regularly spaced templates. A relaxation-labeling routine is then applied to select the most appropriate candidate vectors, according to the local flow. Novel aspects of the relaxation algorithm include a new, efficient form of the support function and the absence of a null-match category. A new development is the application of a post filter to the resultant flow field, providing suitable displacement vectors for templates that were originally unmatched and correcting vectors that are still inconsistent with the local flow. Results from an image sequence from New Zealand's Mount Cook National Park show the superiority of the technique over the maximum cross-correlation method and demonstrate the effectiveness of the post filter in improving correlation-relaxation labeling.

Describes a new fully motion-adaptive spatio-temporal filtering technique to reduce the speckle in ultrasound images. The advantages of this approach are demonstrated in echocardiographic boundary detection and in comparison with other techniques. The first stage of many automated echocardiographic image interpretation schemes is filtering to reduce the amount of speckle noise. The authors show how the two-dimensional least mean squares (TDLMS) filter can be configured as a motion-compensated filter for a time sequence of ultrasound images that eliminates the blurring associated with direct averaging. For an image corrupted by multiplicative speckle noise, the mode of the intensity distribution approximates the maximum likelihood estimator. In consequence, the temporal filter's output is biased towards the mode from the mean, using information contained within the speckle itself. A new adaptive algorithm for controlling the filter's convergence is also included. To evaluate performance, application to simulated, phantom, and an in vivo test sequence of the carotid artery are considered in comparison with other techniques. The effect of filtering on edges is of great importance, as these are used by subsequent image interpretation schemes. Quantitative measurements demonstrate the effectiveness of the Biased TDLMS filter, for both noise reduction and edge preservation. Echocardiographic images have a high noise content and suffer from poor contrast. Despite this challenging environment, the Biased TDLMS filter is shown to produce images that are better inputs for subsequent feature extraction. The benefits for echocardiographic images are highlighted by considering the problems of mitral valve analysis and extraction of the left atrium boundary.

This paper describes a symbolic approach to relational matching. The novelty of the method lies in its Bayesian modelling of relational consistency which leads to a global matching criterion with a unique mathematical structure and robustness to error. Unlike many alternatives in the literature, the method is not limited to the use of binary constraints; it can accommodate N-ary relations of varying order. In consequence of this assumed model, the consistency of match is gauged by a compound exponential function of a higherorder Hamming distance between symbolic relations; there is a single exponential associated with each potential relational mapping. These exponential functions naturally soften the symbolic constraints represented by the relational mappings, This compound exponential structure also bestows a number of tangible benefits over the use of quadratic alternatives. In the first instance, it both renders the method more robust to errors and allows it to operate effectively in a large space of relational mappings. Moreover, this robustness to inconsistency means that the method may be operated without the need for an explicit null matching process. Unmatchable entities are identified by a constraint filtering operation once the relaxation scheme has converged. The utility of the method is illustrated on the matching of hedge structures in SAR images against their cartographic representation in a digital map

The aim is to present a model capable of capturing the statistical correlations introduced by multiplicative noise effects in SAR image data. The motivation behind the study is the need to model noise statistics so that a Bayesian relaxation scheme may be applied to the detection of features in SAR images. The authors present a model which predicts the edge or line gradient distributions to be a product of Rayleigh and Bessel function components; this factorisation separates the correlated and uncorrelated components of the noise.>