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Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database (RMD) with a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. Potentially, the presented database can be used to build surrogate models of well-known processes and to aid in labour-intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, incorporate consistency and credibility into deep learning models. We show the effectiveness of the presented database by surrogating the forward-modelling process, and we urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publicly available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

Airborne time-domain electromagnetic surveys produce extremely large data sets with thousands of line kilometers of data and millions of possible models to explain the data. Inversion of such data sets to obtain the resistivity structures of the subsurface is computationally intensive and involves calculation of a significant number of forward and derivative responses for solving the least-squares inverse problem. The flight altitude of the airborne system needs to be included in the modeling, which adds further complexity. We propose to integrate neural networks in a damped iterative least-squares inversion framework to expedite the inversion process. We train two separate neural networks to predict the forward responses and partial derivatives independently for a broad range of resistivity structures and flight altitudes. Data inversion is not only used for producing the final subsurface models but also used during data processing, or to produce intermediate results during a survey. With these purposes in mind, we provide three inversion schemes with a tunable balance between computational time and modeling accuracy: (1) numerical forward responses used initially in combination with neural network derivatives, and the derivatives switched to a numerical solution in final iterations, (2) numerical forward responses in combination with neural network derivatives used throughout the inversion, and (3) only neural network forward responses and derivatives used in inversion. Experiments on field data find that we improve inversion speed without any loss in modeling accuracy with our first approach, whereas the second scheme gives a significant speedup at the cost of minor and often acceptable deviations in the inversion results from the conventional nonlinear inversion. The last approach is the fastest and captures the overall resistivity structures quite well. Therefore, depending on the modeling accuracy, inversion speedup factors of up to 50 are realized by using the proposed schemes.

Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is computationally expensive and time-consuming. The calculation of partial derivatives for the Jacobian matrix is by far the most computationally intensive task, as this requires calculation of a significant number of forward responses. We propose to accelerate the inversion process by predicting partial derivatives using an artificial neural network. Network training data for resistivity models for a broad range of geological settings are generated by computing partial derivatives as symmetric differences between two forward responses. Given that certain applications have larger tolerances for modeling inaccuracy and varying degrees of flexibility throughout the different phases of interpretation, we present four inversion schemes that provide a tunable balance between computational time and inversion accuracy when modeling TEM datasets. We improve speed and maintain accuracy with a hybrid framework, where the neural network derivatives are used initially and switched to full numerical derivatives in the final iterations. We also present a full neural network solution where neural network forward and derivatives are used throughout the inversion. In a least-squares inversion framework, a speedup factor exceeding 70 is obtained on the calculation of derivatives, and the inversion process is expedited ~36 times when the full neural network solution is used. Field examples show that the full nonlinear inversion and the hybrid approach gives identical results, whereas the full neural network inversion results in higher deviation but provides a reasonable indication about the overall subsurface geology.

The most common noise in geophysical data is probably interference from powerlines. This noise manifests itself as a sinusoidal signal oscillating at the fundamental 50 or 60 Hz frequency of the power grid and as harmonic components oscillating at integer multiples. Many different mitigation strategies, tailored for the specific geophysical method, have been developed to target powerline noise. One method that applies to fully sampled data is model-based subtraction, where a model of the powerline noise is fit to the noisy dataset and subsequently subtracted. In most cases, this leads to significant improvements in the signal-to-noise ratio. However, the determination of the powerline model parameters, in particular the fundamental powerline frequency, is computationally expensive, as it requires repeated solutions of a least-squares problem. We demonstrate that the powerline frequency can be directly predicted with a scientific machine-learning-based approach. We work on both time domain-induced polarization and surface nuclear magnetic resonance data. We use a different network for each method to trade-off prediction accuracy and prediction speed. In both cases, the prediction accuracy is fully on par with standard methods, and we obtain speed-ups by factors of 400 and 10 for the two types of data.

Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We show the effectiveness of the presented database by surrogating the forward modelling process, and urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publically available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

Modern transient electromagnetic (TEM) surveys, either ground-based or airborne, may yield thousands of line kilometers of data. Parts of these data, especially in areas with dense infrastructure, are often disturbed by electromagnetic couplings due to infrastructure, e.g., power cables and fences. In most cases and in particular when working in a hydro-geological context, such coupled data must be culled before inversion. The process of identifying and culling coupled data is a manual task, requiring specialists to examine and process the data in detail. Manual data processing is subjective, difficult to reproduce, and time-consuming. To automate the complex data processing workflows, we propose an expert system based on a deep convolutional auto-encoder to identify couplings in the data. We configure the auto-encoder to learn an encoded representation of TEM data in a latent space. A reconstruction part that decodes the encoded representation is also trained, aiming to reconstruct input data. If the data unaffected by electromagnetic couplings are observed by the auto-encoder, the reconstructed output will have low error to the input. However, when having couplings in the data, the reconstruction error is elevated, indicating a non-geologic anomaly. The size of the anomaly is based on the relative error between the input data and the reconstructed output normalized by the data standard deviation. We show that the proposed approach displays high quality data processing within a fraction of a second for a ground-based and an airborne system, which is either ready for inversion or requires minimal further quality inspection.

Only a few methods in literature are effective for multi-national license plate detection in a multi-lane scenario. These methods are prone to illumination variance, complex background and weak-edged license plates. In this paper, we propose a novel illumination invariant method to handle multi-national vehicle license plates of different colors and styles. Red corona is initially used to detect the tail-lights of vehicles to establish region-of-interest as the license plates are in a vicinity of its tail-lights. The vertical edges within each region-of-interest are obtained using a unique approach that preserve license plate edges for improved performance. Heuristic energy map is then used to distinguish the license plate area. To validate the detected regions, high-level features extracted from AlexNet Convolutional Neural Network are used. Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance, but also outperforms the conventional and deep-learning methods.

Wireless Sensor Network (WSN) is an emerging technology that has attractive intelligent sensor-based applications. In these intelligent sensor-based networks, control-overhead management and elimination of redundant inner-network transmissions are still challenging because the current WSN protocols are not data redundancy-aware. The clustering architecture is an excellent choice for such challenges because it organizes control traffic, improves scalability, and reduces the network energy by reducing inner-network communication. However, the current clustering protocols periodically forward the data and consume more energy due to data redundancy. In this paper, we design a novel cluster-based redundant transmission control clustering framework that checks the redundancy of the data through the statistical tests with an appropriate degree of confidence. After that, the cluster-head separates and deletes the redundant data from the available data sets before sending it to the next level. We also designed a spatiotemporal multi-cast dynamic cluster-head role rotation that is capable of easily adjusting the non-associated cluster member nodes. Moreover, the designed framework carefully selects the forwarders based on the transmission strength and effectively eliminates the back-transmission problem. The proposed framework is compared with the recent schemes using different quality measures and we found that our proposed framework performs favorably against the existing schemes for all of the evaluation metrics.

Inversion of large-scale time-domain electromagnetic surveys are computationally expensive and time consuming. Deterministic or probabilistic inversion schemes usually require calculations of forward responses, and often thousands to millions of forward responses are computed. We propose a machine learning based forward modelling approach as a computationally feasible alternative to approximate numerical forward modelling where a neural network is employed to model the relationship between the resistivity models and corresponding forward responses. For training of the neural network, we generated forward responses using conventional numerical algorithm for 93,500 resistivity models derived from different surveys conducted in Denmark representing typical resistivities of sedimentary geological layers. The input resistivity models and the network target outputs, i.e. forward responses, are scaled using a novel normalization strategy to ensure each gate is equally prioritized. The performance of the network is evaluated on two test datasets consisting of 8942 resistivity models by comparing the forward responses generated by the neural network and the conventional algorithm. We also measure the performance for the time derivatives of forward responses, i.e. dB/dt, by incorporating a system response. The results show that the proposed strategy is at least 13 times faster than commonly used accurate modelling methods and achieves an accuracy of 98% within 3% relative error, which is comparable to data uncertainty. Additional experiments on surveys from two other continents show that the results generalize in similar geological settings. Thus, under certain geological constraints, the proposed methodology may be incorporated into the pre-existing inversion structures, allowing for significantly faster inversion of large datasets.

Geophysical modelling and data inversion are important tools for interpreting the physical properties of Earth's subsurface. Solving the inverse problem involves several computational steps and is generally a time consuming task. Artificial neural networks have the potential to speed up large computations. Such networks provide the means to model the relationship between the inputs and outputs without needing to know the physical model of the underlying problem. There are two main aspects that affect the performance of neural networks: optimization of network architecture and pre-processing of data. In this article, we investigate several traditional pre-processing techniques including the min-max scaling, z-score scaling, and the logarithmic transform scaling, and propose some novel data pre-processing approaches for the 1-D forward modelling of time-domain electromagnetic data based on signal characteristics. We evaluate the performance of the conventional and the proposed pre-processing methods against a 3% relative error metric, which corresponds to the typical data uncertainty, to show that forward data pre-processing has significant effect on the performance of neural networks. The proposed gate-wise min-max scaling achieves the best performance with 96% of gates within a 3% relative error, while the commonly used logarithmic transform results only in 75% of gates within a 3% relative error. We provide insights into how various pre-processing methods affect the performance of these networks and recommend optimal pre-processing strategies that may be used where similar data content is encountered to achieve superior performance. Finally, we show the effect of forward modelling accuracy in inverse modelling.

License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles License plate detection for vehicle identification is one of the key problems for traffic surveillance in urban areas. Most of the existing methods that can handle multiple license plates are country-specific as color information has been typically targeted. In this paper, we propose a real-time multiple license plate detection method feasible for multi-national vehicles having variable colors, sizes and geometrical attributes. A region-of-interest is initially identified for each vehicle using a fuzzy inference system based on the salient feature of its rear lights as license plates generally exist in a vicinity of these lights. Due to the abundance of edges within the license plate region, a local recursive analysis approach is utilized to locate the license plate candidate within each region-of-interest after tilt correction using a rear-light alignment technique. To verify the detected region as a true license plate, a unique combination of local image features is used to achieve high precision. The proposed method has been tested on 2200 images taken during various weather and illumination conditions to detect 5379 license plates out of 5945 available vehicles with 90.5% accuracy. The proposed approach outperforms the conventional and deep learning methods to achieve superior performance with the ability of being applied to multi-national vehicles.

This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of a quadrotor to undergraduate students, using a graphical user interface (GUI) and 3-D animations. The Simulink quadrotor model is controlled by a proportional integral derivative controller and a linear quadratic regulator controller. The GUI layout’s many components can be easily programmed to perform various experiments by considering the simulation of the quadrotor as a plant; it incorporates control systems (CS) fundamentals such as time domain response, transfer function and state-space form, pole-zero location, root locus, frequency domain response, steady-state error, position and disturbance response, controller design and tuning, unity, and the use of a Kalman filter as a feedback sensor. 3-D animations are used to display the quadrotor flying in any given condition selected by the user. For each simulation, users can view the output response in the form of 3-D animations, and can run time plots. The quadrotor educational tool (QET) helps students in the CS laboratory understand basic CS concepts. The QET was evaluated based on student feedback, grades, satisfaction, and interest in CS.

License plate detection in complex backgrounds.Ability to deal with multinational license plates regardless of color, size and content.Ability to handle images with large depth of field.Two novel contributions.Compared with three latest and one classical method. Many methods for multinational License Plate Detection (LPD) have been proposed in recent times but most of them are not sophisticated enough to handle complex backgrounds. Moreover, their ability to handle various environmental and illumination conditions has been limited and still needs improvement. In this paper, we propose a novel technique to detect license plates of vehicles regardless of their color, size, and content. As the rear vehicle lights are an essential part of any vehicle, we reduce the image processing area to eliminate the complex background by detecting the rear-lights as the license plates are in a certain range of these lights. Heuristic Energy Map (HEM) of the vertical edge information in the Region of Interest (ROI) is calculated and area with the dense edges is selected using a unique histogram approach which is considered to be the license plate. The proposed algorithm is tested on 855 images from various countries including China, Pakistan, Serbia, Italy and various states of America. Experimental results show that the proposed method is able to detect license plates 90.4% of times despite of complex backgrounds in 0.25s on average that can achieve real time performance.

Sparse reconstruction based saliency detection methods are gaining popularity for object detection and content-based image recovery due to its simplicity and easy understandability. It helps in extracting concise representations of the stimuli and capture high-level semantics in visual information with a few active coefficients. Different from the conventional sparse representation techniques that highlight the borders of the salient objects only, we propose a novel regularized sparse coding method that preserves the similarity and locality to achieve smoothness in sparse representation to evenly highlight the entire salient object. In this study, we propose a novel Maximum Mean Discrepancy (MMD) regularized sparse representation method for salient region detection. Initially, the resemblance and locality of super pixels are conserved by constructing a graph regularization term to increase the fidelity of the salient coefficient score of the visual part. Secondly, the distributions divergence among the similar regions is alleviated by constructing a MMD regularized term. Furthermore, the re-constructive background dictionary is extracted from background pixels that are enriched with visual and geometrical information. The results computed through this dictionary are more accurate in terms of background suppression. We analyze our model on four largest benchmark datasets using five evaluation metrics that reveal the fact that the performance of the proposed model is satisfactory and favorably against the existing state-of-the-art schemes. HighlightsWe exploited visual and contextual information to precisely extract the salient objects.Our extracted background dictionary is very effective in removing background noises.We included a Laplacian term to preserve similarity and locality among salient regions.Our MMD regularized term transfers sparse coding to an effective representation.

Aphids are agricultural pest insects that transmit viruses and cause feeding damage on a global scale. Current pest control involving the excessive use of synthetic insecticides over decades has led to multiple forms of aphid resistance to most classes of insecticides. In nature, plants produce secondary metabolites during their interaction with insects and these metabolites can act as toxicants, antifeedants, anti-oviposition agents and deterrents towards the insects. In a previous study, we demonstrated that the butanol fraction from a crude methanolic extract of an important plant species, Isodon rugosus showed strong insecticidal activity against the pea aphid, Acyrthosiphon pisum. It was however not known as which compound was responsible for such activity. To further explore this finding, current study aimed to exploit a bioactivity-guided strategy to isolate and identify the active compound in the butanol fraction of I. rugosus. As such, reversed-phase flash chromatography, acidic extraction and different spectroscopic techniques were used to isolate and identify the new compound, rosmarinic acid as the bioactive compound in I. rugosus. Insecticidal activity of rosmarinic acid was carried out using standard protocols on A. pisum. The data was analyzed using qualitative and quantitative statistical approaches. Considering that a very low concentration of this compound (LC90 = 5.4 ppm) causes significant mortality in A. pisum within 24 h, rosmarinic acid could be exploited as a potent insecticide against this important pest insect. Furthermore, I. rugosus is already used for medicinal purposes and rosmarinic acid is known to reduce genotoxic effects induced by chemicals, hence it is expected to be safer compared to the current conventional pesticides. While this study highlights the potential of I. rugosus as a possible biopesticide source against A. pisum, it also provides the basis for further exploration and development of formulations for effective field application.

In this study, the authors propose a real-time multiple licence plate (LP) detection algorithm for dense traffic conditions which is of vital importance in this modern era due to increased traffic congestion. The chromatic component of YDbDr colour space is proposed to detect the blue regions, whereas a simple yet effective colour detection method is used to identify yellow LP regions. The low-intensity pixel values are eliminated as a pre-processing step to enhance the LP regions and Otsu method is used to obtain the binary image. The candidate regions are acquired by using the connected component analysis. The false candidate regions are by large rejected by inspecting the area and aspect ratio of LPs. Additionally, a two-layered false LP detection approach has been introduced to remove fake LP regions. Experimental results in practical scenarios carried out in various weather conditions show that the proposed method is highly effective in coping with various illumination conditions to accurately detect the multiple vehicle LPs with an accuracy of 93.86%. The average processing time per image is 0.33 s that can achieve real-time performance.

A complex domain-based approach of active contour model has been proposed for image segmentation which deforms iteratively to partition an image into various useful regions. A new region-based complex pressure force function has been designed which gives shape to the dynamic active contour using image forces, and efficiently control the propagation of moving interface. This model makes the level set function binary, uses Gaussian smoothing kernel to regulate and avoid re-initialisation procedure. The working scheme of the model is as: the real image data is converted into complex data by iota (i) times and the average iota (i) times of horizontal and vertical components of gradient is inserted in the model to develop complete complex gradient. The proposed model can be implemented by simple finite difference scheme. The efficiency and robustness of the authors model have been verified.

Feature matching is the core stage for object recognition, tracking and several applications of computer vision. Low resolution images have various limitations with respect to spatial, spectral, pixel and temporal information which reduces the performance of image processing approaches. We have combined SURF features with FAST and BRISK features individually in order to provide an optimal solution for feature matching. Furthermore, feature matching has exploited through combined features and compared the performance with state-of-the-art methods. Lastly, RANSAC and MSAC were utilized to eliminate the wrong matches to get optimal feature matches. The experimental results show that the combination of FAST–SURF and BRISK–SURF perform feature matching optimally according to the number of feature matches and estimated time.

In order to achieve superior performance, various projection space pairs (PSPs) in neighbor embedding (NE) algorithms are introduced aiming to satisfy manifold assumption better. However, the comparison of theses PSPs has not been given much importance in previous researches, which could be a guiding factor for choosing better PSPs before executing the whole neighbor embedding process. Besides, evaluation criterions of final results like Peak Signal to Noise Ratio (PSNR) cannot represent the exact performance of non-linear PSPs due to the non-linear back projection process. To overcome these limitations, we compare different PSPs by introducing an efficient technique using cosine similarity and histogram approach. Experimental results demonstrate the effectiveness of the proposed evaluation method. Moreover, we also identify that non-linear PSPs could obtain superior performance only if the non-linear back projection process is well handled.

Region proposals are very important for several perfections of remote sensing applications such as vehicle detection, traffic surveillance and intelligent transport system. In this paper, an efficient region proposal approach has been proposed. The framework is organized into two key steps. The first step is based on extracting region proposals using Cascade system. The second step is based on the classification of extracted region proposals which is performed by transfer learning using Convolutional Neural Networks (CNN) and AlexNet architecture is utilized for transfer learning. The aim of this investigation is to evaluate the proposed method for vehicle detection. The selective search (SS) method is also briefly discussed for comparison. The results regarding vehicle detection are very promising.

The verification of vehicle License Plates (LPs) has not been given much importance in existing LP recognition systems as only a handful of methods deal with this problem explicitly. For an efficient system, it is imperative that a detected LP is validated first before the recognition of characters on it. Majority of the existing methods make use of geometrical constraints for the elimination of false LP regions which is not an effective way as multinational LPs have variable geometrical attributes and diversity in styles. To overcome these limitations, in this paper, we evaluate three kinds of representative local descriptors (SURF, HOG and LBP) and their combinations along with AIexNet CNN for the classification of LP and non-LP regions to provide a unique solution for the validation of multinational LPs. Experiments on 13490 LP and non-LP images show that the HOG feature individually gives the best recognition rate of 96.94% while considering collectively, best of 98.35% is achieved for SURF+HOG; whereas, the fine-tuned AIexNet outperform all others in terms of recognition accuracy of 99.27% but requires extensive processing. Furthermore, the proposed model is incorporated in one of the existing LP detection methods to demonstrate improved performance.

The processing of satellite imagery is dependent upon the quality of imagery. Due to low resolution, it is difficult to extract accurate information according to the requirements of applications. For the purpose of vehicle detection under shadow regions, we have used HOG for feature extraction, SVM is used for classification and HOG is discerned worthwhile tool for complex environments. Shadow images have been scrutinized and found very complex for detection as observed very low detection rates therefore our dedication is towards enhancement of detection rate under shadow regions by implementing appropriate preprocessing. Vehicles are precisely detected under non-shadow regions with high detection rate than shadow regions.

Drone technology is being used for military, agriculture, aerial photography, surveillance, remote sensing and many more purposes. In this paper, drone plane is proposed for monitoring and targeting the street crime criminals based on real time image processing techniques. Operations of proposed plane controlled with two processing units, 1st processing unit is for implementation of real time image processing techniques and 2nd processing unit will handle the rest of controlling, monitoring and targeting operations. Drone plane will monitor circular area of 5 kilometers and it will automatically perform all operations and can be controlled by operator. Shape detection algorithms have been tested to find accuracy in target detection and analysis the processing time before implementing in such environment and results provide optimal accuracy in matching weapons type with name and shape in predefined database.

Aphids are agricultural pest insects that transmit viruses and cause feeding damage on a global scale. Current pest control practices involving the excessive use of synthetic insecticides over many years have resulted in aphid resistance to a number of pesticides. In nature, plants produce secondary metabolites during their interaction with insects and these metabolites can act as toxicants, antifeedants, anti-oviposition agents and deterrents towards the insects. In a previous study, we demonstrated that the butanol fraction from a crude methanolic extract of an important plant species, Isodon rugosus showed strong insecticidal activity against the pea aphid, Acyrthosiphon pisum. To further explore this finding, the current study aimed to exploit a bioactivity-guided strategy to isolate and identify the active compound in the butanol fraction of I. rugosus. As such, reversed-phase flash chromatography, acidic extraction and different spectroscopic techniques were used to isolate and identify the new compound, rosmarinic acid, as the bioactive compound in I. rugosus. Insecticidal potential of rosmarinic acid against A. pisum was evaluated using standard protocols and the data obtained was analyzed using qualitative and quantitative statistical approaches. Considering that a very low concentration of this compound (LC90 = 5.4 ppm) causes significant mortality in A. pisum within 24 h, rosmarinic acid could be exploited as a potent insecticide against this important pest insect. Furthermore, I. rugosus is already used for medicinal purposes and rosmarinic acid is known to reduce genotoxic effects induced by chemicals, hence it is expected to be safer compared to the current conventional pesticides. While this study highlights the potential of I. rugosus as a possible biopesticide source against A. pisum, it also provides the basis for further exploration and development of formulations for effective field application.

Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value.

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes.

A novel region based Active Contour Model (ACM) for image segmentation is presented using image local information for intensity inhomogeneity images. A transcendental (trigonometric) energy functional based on Local Fitted Image (LFI) energy is suggested to extract the image local information. The difference between the original and fitting image is introduced as an angular constraint of the trigonometric function. This new approach has the ability to handle images with severe intensity inhomogeneity, multiple intensity classes and regions with non-homogeneous pixel intensities. To regularize and maintain the level set function, Gaussian filtering procedure is adopted that not only maintains the level set consistency but also eliminates re-initialization procedure to avoid computational complexity. Furthermore, the proposed model holds boundary regularization property and achieves sub-pixel accuracy. Experimental results on several real and synthetic images demonstrate that the proposed model has higher performance and better accuracy in comparison to the state-of-the-art models.

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

Summary Inversion of geophysical data is often challenging and time-consuming, particularly for large scale surveys. The solution of the inverse problem requires numerous calculations of the forward problem, especially when calculating partial derivatives required for most linearized inversion schemes. The forward model is usually calculated numerically using accurate equations, but often less accurate and faster equations are used. In recent years, neural networks have become increasingly popular to replace the numerical forward modelling, as this may lead to a significant speed-up. Data normalization, prior to the training of neural networks, is crucial to obtain good results and faster convergence rate. This is especially true for geophysical data, as numerical data values may span over several orders of magnitude. In this abstract, we investigate several normalization approaches for TEM data, with a special focus on towed TEM data. Through extensive experimentations, we show that data normalization substantially affects the performance of neural networks when surrogating forward models. We also demonstrate the effect of normalized data variation on neural network’s performance and provide insights into which normalization approaches may be better than others. A significant improvement in performance accuracy is achieved when the appropriate data normalization technique is employed.

Transient Electromagnetics (TEM) is a workhorse technology offering robust insights into hydrogeological structures in the subsurface. Modern instruments yield enormous amounts of data. To lower the complication of the method and getting the technology even more widespread, machine learning approaches offer great opportunities for complete automation of the data processing task. A major time consuming task is to sort good data from data contaminated by interference to buried power lines, fences, etc. (called as couplings). This process is mostly an expensive, highly sophisticated and a manual task. The state-of-the-art machine learning approaches uses supervised methods trained on a subset of acquired data within a localized region, but this is not generalizable to data obtained at other sites making it an in-efficient solution.As couplings in the data are generally rare events, we consider it as an anomaly detection problem. Therefore, we deploy an unsupervised learning approach where a deep convolutional autoencoder is used to differentiate between the coupled and uncoupled data. The autoencoder is trained to learn a representation (encoding) of the input data for dimensionality reduction. A reconstruction part is also trained that expands the reduced dimension to result the output as close as possible to the original input. Since the autoencoder aims to replicate the input at its output, we rely on a database of synthetic TEM data that corresponds to a wide range of subsurface models. By learning the original representation from the most salient features encoded in the reduced dimension, the autoencoder learns to reproduce the most frequently observed characteristics. When facing couplings in the data, the reconstruction performance degrades and the reconstruction error between the original data and its reduced dimensional reconstruction becomes higher, which is used as an anomaly score to detect couplings.We test our method on data acquired at several sites by a towed TEM (tTEM) system and show that the proposed approach exhibits high quality data processing within a fraction of a second, which is ready for inversion. The method is generalizable to practically any tTEM data set acquired worldwide. Importantly, the complicated data processing is been eliminated and no highly skilled operator is required.