An improved SVD-based wall clutter mitigation method for through-the-wall radar imaging

This paper presents an improved SVD-based method for wall clutter mitigation in through-the-wall radar imaging. The dominant wall singular components are identified from the singular value spectrum. A subspace projection method is then applied to remove the strong wall clutter, residing in the dominant singular components, and separate the target signal from noise. The remaining wall clutter residual, which is mixed with the target signal, is suppressed by segmenting the range profile of the signal residing in the subspace orthogonal to the wall and noise subspaces. A Gaussian mixture is used to model the range profile, and the optimum segmentation threshold is found by minimizing the Bayes error. Experiments results show that the proposed method is more effective at reducing wall clutter and preserving the targets than some of the existing wall clutter mitigation methods

A subspace projection approach for wall clutter mitigation in through-the-wall radar imaging

One of the main challenges in through-the-wall radar imaging (TWRI) is the strong exterior wall returns, which tend to obscure indoor stationary targets, rendering target detection and classification difficult, if not impossible. In this paper, an effective wall clutter mitigation approach is proposed for TWRI that does not require knowledge of the background scene nor does it rely on accurate modeling and estimation of wall parameters. The proposed approach is based on the relative strength of the exterior wall returns compared to behind-wall targets. It applies singular value decomposition to the data matrix constructed from the space-frequency measurements to identify the wall subspace. Orthogonal subspace projection is performed to remove the wall electromagnetic signature from the radar signals. Furthermore, this paper provides an analysis of the wall and target subspace characteristics, demonstrating that both wall and target subspaces can be multidimensional. While the wall subspace depends on the wall type and building material, the target subspace depends on the location of the target, the number of targets in the scene, and the size of the target. Experimental results using simulated and real data demonstrate the effectiveness of the subspace projection method in mitigating wall clutter while preserving the target image. It is shown that the performance of the proposed approach, in terms of the improvement factor of the target-to-clutter ratio, is better than existing approaches and is comparable to that of background subtraction, which requires knowledge of a reference background scene

Slope stability prediction using the Artificial Neural Network (ANN)

Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by randomly generating material input parameters. Over 80% of generated FOS values were used to train ANNs and the remaining 20% were used to for validation. The one-material models performed better than the two-material models overall. The first sub-model from the one-material models and the third sub-model from the two-material models exhibited the best performance compared to the other sub-models, achieving Mean Square Error (MSE) of 8.35E-04 and 5.10E-3, respectively. The third sub-model from the one-material models and the first sub-model from the two-material models have a MSE of 2.00E-3 and 9.80E-3, respectively. The second sub-models have shown the lowest performance compared to the other models. The minimal errors between LEMs and ANNs have led to the conclusion that ANN can be used as a tool for a quick and first-pass analysis by design engineers without undertaking rigours, complex, time-consuming and tedious computation of FOS using LEMs. An actual field-tested database can be usedto predict real-world slope failures

Feature selection for facial expression recognition

In daily interactions, humans convey their emotions through facial expression and other means. There are several facial expressions that reflect distinctive psychological activities such as happiness, surprise or anger. Accurate recognition of these activities via facial image analysis will play a vital role in natural human-computer interfaces, robotics and mimetic games. This paper focuses on the extraction and selection of salient features for facial expression recognition. We introduce a cascade of fixed filters and trainable non-linear 2-D filters, which are based on the biological mechanism of shunting inhibition. The fixed filters are used to extract primitive features, whereas the adaptive filters are trained to extract more complex facial features for classification by SVMs. This paper investigates a feature selection approach that is based on the reduction of mutual information among the selected features. The proposed approach is evaluated on the JAFFE database with seven types of facial expressions: anger, disgust, fear, happiness, neutral, sadness and surprise. Using only two-thirds of the total features, our approach achieves a classification rate (CR) of 96.7%, which is higher than the CR obtained using all features. Our system also outperforms several existing methods, evaluated on the same JAFFE database

Gender and gaze gesture recognition for human-computer interaction

© 2016 Elsevier Inc. The identification of visual cues in facial images has been widely explored in the broad area of computer vision. However theoretical analyses are often not transformed into widespread assistive Human-Computer Interaction (HCI) systems, due to factors such as inconsistent robustness, low efficiency, large computational expense or strong dependence on complex hardware. We present a novel gender recognition algorithm, a modular eye centre localisation approach and a gaze gesture recognition method, aiming to escalate the intelligence, adaptability and interactivity of HCI systems by combining demographic data (gender) and behavioural data (gaze) to enable development of a range of real-world assistive-technology applications. The gender recognition algorithm utilises Fisher Vectors as facial features which are encoded from low-level local features in facial images. We experimented with four types of low-level features: greyscale values, Local Binary Patterns (LBP), LBP histograms and Scale Invariant Feature Transform (SIFT). The corresponding Fisher Vectors were classified using a linear Support Vector Machine. The algorithm has been tested on the FERET database, the LFW database and the FRGCv2 database, yielding 97.7%, 92.5% and 96.7% accuracy respectively. The eye centre localisation algorithm has a modular approach, following a coarse-to-fine, global-to-regional scheme and utilising isophote and gradient features. A Selective Oriented Gradient filter has been specifically designed to detect and remove strong gradients from eyebrows, eye corners and self-shadows (which sabotage most eye centre localisation methods). The trajectories of the eye centres are then defined as gaze gestures for active HCI. The eye centre localisation algorithm has been compared with 10 other state-of-the-art algorithms with similar functionality and has outperformed them in terms of accuracy while maintaining excellent real-time performance. The above methods have been employed for development of a data recovery system that can be employed for implementation of advanced assistive technology tools. The high accuracy, reliability and real-time performance achieved for attention monitoring, gaze gesture control and recovery of demographic data, can enable the advanced human-robot interaction that is needed for developing systems that can provide assistance with everyday actions, thereby improving the quality of life for the elderly and/or disabled

A new class of convolutional neural networks based on shunting inhibition with applications to visual pattern recognition

In the contemporary era of increased information overload, there is a growinginterest in a new class of computational intelligence systems. These systems have been proven as powerful and versatile computational tools for solving certain types of problems that are too complex to be analyzed with traditional analytical means. Inspired by the computational mechanism of the human brain, many researchers have looked into neurobiology for new inspiration to solve more complex problems than those based on traditional computational techniques. Artificial neural networks, evolving from neuro-biological insights, give computer systems an amazing capability to actually learn from input data to generate solutions for problems that are too abstract to be understood or too resource-intensive to tackle. Although neural networks have been applied with success in many industries, there is a continuing demand for new types of hierarchical artificial neural networks that can overcome some of the drawbacks of the earlier models. This thesis presents a new class of convolutional neural networks based on the physiologically plausible mechanism of shunting inhibition with its various systematic connection schemes. The network has a generic architecture in which shunting inhibitory neurons are used as feature extraction elements. A series of training algorithms, ranging from first-order gradient methods to Quasi-Newton and hybrid methods, have been implemented to adapt the synaptic weights of the developed networks; all of them have been successfully used to train the convolutional neural networks for a classification task. To demonstrate their capability in real life applications, the convolutional networks are employed in a face detection system and a handwritten digit recognition system. The face detector has 383 trainable network parameters and achieves a detection rate of 98%for detecting human faces on a large set of unconstrained and complex images. The handwritten digit recognition system, on the other hand, has 2722 trainable parameters, and its classification rate is 97.3% for recognizing human handwritten numerals. Besides these two applications, the developed network is analyzed for its built-in invariance, and it is implemented as a rotation invariant face classifier. The network achieves a classification rate of 97.3% in the rotation range ±900, and for 360° in-plane rotation, it has a correct detection rate of 93.6% at 5% false detection rate. These classification results demonstrate that the new class of convolutional neural networks has excellent generalization capability and achieves rotation invariance by adapting its connection weight matrices (receptive fields) as invariant feature detectors

A tensor-based subspace wall clutter mitigation method for through-the-wall radar imaging

In through-the-wall radar imaging, targets behind the wall reflect weak electromagnetic signals that are obscured by the strong returns from exterior wall, rendering the detection and classification of indoor stationary targets very difficult. In this paper, a tensor-based subspace method is proposed for wall clutter mitigation. The radar signals received from the antenna array are transformed into a data tensor. Higher-order singular value decomposition is used to segregate the wall reflections from the target returns. Simulation and experimental results show that the proposed method is effective in removing reflections backscattered from both homogeneous and heterogeneous walls