In computer vision, object recognition and image categorization have proven to be difficult chall... more In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.
A huge amount of generated data is regularly exploding into the network by the users through smar... more A huge amount of generated data is regularly exploding into the network by the users through smartphones, laptops, tablets, self-configured Internet-of-things (IoT) devices, and machine-to-machine (M2M) communication. In such a situation, satisfying critical quality-of-service (QoS) requirements (e.g., throughput, latency, bandwidth, and reliability) is a large challenge as a vast amount of data travels into the network. Nowadays, strict QoS requirements must be satisfied efficiently in many networked multimedia applications when intelligent multi-homed devices are used. Such devices support the concept of multi-homing. To be precise, they have multiple network interfaces that aim to connect and communicate concurrently with different networking technologies. Therefore, many multipath transport protocols are provided to multi-homed devices, which aim (1) to take advantage of several network paths at the transport layer (Layer-4) and (2) to meet the strict QoS requirements for provid...
Innovation of technology and having fast Internet makes distribution of information over the worl... more Innovation of technology and having fast Internet makes distribution of information over the worldeasy and economical. This has made people to worry about their privacy and works. We present a techniquethat prevents unauthorized users to have access to important data. Steganography and digital watermarkingprovide methods so that users can hide and mix their information within other information that make it difficult torecognize by attackers. In this paper, wepresent a technique of embedding data using sequences into an Image.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Recent Advances in Computer Science and Communications
Background: The existing methods based on infrared patch image (IPI) model for small target detec... more Background: The existing methods based on infrared patch image (IPI) model for small target detection does suffer from l1 norm sparsity problem where the non-target elements in the background image may sometimes be considered as the target element. Hence using l1 norm may lead to degrade the detection ability of small and dim target in a noisy environment. So a robust method needs to be developed to tackle the above-said problem. Method: In this paper, the Infrared patch model based on non-convex weighted nuclear norm minimization and Robust principal component analysis (IPNCWNNM-RPCA) is presented. Here we improve the existing IPI model by replacing the nuclear norm by weighted nuclear norm, where unlike in nuclear norm minimization we assign different weights to singular values. The proposed method is further solved by the alternating direction method of the multiplier (ADMM). Results: To validate the robustness of the proposed method extensive experiments on the large dataset is ...
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
In the present infrared target detection system, simultaneously achieving both the target detecti... more In the present infrared target detection system, simultaneously achieving both the target detection performance, as well as, computing efficiency is considered as a big task. To, address the above said issue, a non-convex triple tensor factorisation is incorporated into the existing infrared patch tensor model in the proposed work. In the proposed model (triple tensor factorisation-infrared patch tensor), local prior information using linear structure tensor and corner strength is incorporated so that the strong clutters in the background can be easily suppressed and the target can be detected correctly. Finally, the method proposed, was solved by alternating direction method of the multiplier. Large number of experiments were conducted and the results presented in the experiments suggest that the method proposed has shown good performance for background suppression, as well as, for the target detection in the clutter environment, when compared with the other baseline methods.
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Over the last decade, many algorithms have been presented on an infrared target detection problem... more Over the last decade, many algorithms have been presented on an infrared target detection problem. This paper reviews the recent approaches in infrared small target detection algorithms. Firstly, the detailed review of Detection-Before-Track methods which are seen in recent years are discussed and problem associated with these methods are raised. Secondly, to verify the robustness of these methods, large set of experiments are conducted and comparative analysis of different baseline methods is done. In addition to this advantages and disadvantages of baseline methods is discussed. Experimental results from the dataset suggest that the patch based methods are more robust than the other filter-based methods. How to address the issues of small target detection method in complex environment and the effect of varying parameters like patch size, step size and controlling parameters is our future endeavor. Keywords: Track before detection (TBT), detect before track(DBT), infrared search and track, signal to clutter ratio, infrared patch image model
In computer vision, object recognition and image categorization have proven to be difficult chall... more In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attentionaware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F-score of 0.92, indicating that the suggested model outperformed against the baseline models.
A huge amount of generated data is regularly exploding into the network by the users through smar... more A huge amount of generated data is regularly exploding into the network by the users through smartphones, laptops, tablets, self-configured Internet-of-things (IoT) devices, and machine-to-machine (M2M) communication. In such a situation, satisfying critical quality-of-service (QoS) requirements (e.g., throughput, latency, bandwidth, and reliability) is a large challenge as a vast amount of data travels into the network. Nowadays, strict QoS requirements must be satisfied efficiently in many networked multimedia applications when intelligent multi-homed devices are used. Such devices support the concept of multi-homing. To be precise, they have multiple network interfaces that aim to connect and communicate concurrently with different networking technologies. Therefore, many multipath transport protocols are provided to multi-homed devices, which aim (1) to take advantage of several network paths at the transport layer (Layer-4) and (2) to meet the strict QoS requirements for provid...
Innovation of technology and having fast Internet makes distribution of information over the worl... more Innovation of technology and having fast Internet makes distribution of information over the worldeasy and economical. This has made people to worry about their privacy and works. We present a techniquethat prevents unauthorized users to have access to important data. Steganography and digital watermarkingprovide methods so that users can hide and mix their information within other information that make it difficult torecognize by attackers. In this paper, wepresent a technique of embedding data using sequences into an Image.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Recent Advances in Computer Science and Communications
Background: The existing methods based on infrared patch image (IPI) model for small target detec... more Background: The existing methods based on infrared patch image (IPI) model for small target detection does suffer from l1 norm sparsity problem where the non-target elements in the background image may sometimes be considered as the target element. Hence using l1 norm may lead to degrade the detection ability of small and dim target in a noisy environment. So a robust method needs to be developed to tackle the above-said problem. Method: In this paper, the Infrared patch model based on non-convex weighted nuclear norm minimization and Robust principal component analysis (IPNCWNNM-RPCA) is presented. Here we improve the existing IPI model by replacing the nuclear norm by weighted nuclear norm, where unlike in nuclear norm minimization we assign different weights to singular values. The proposed method is further solved by the alternating direction method of the multiplier (ADMM). Results: To validate the robustness of the proposed method extensive experiments on the large dataset is ...
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
In the present infrared target detection system, simultaneously achieving both the target detecti... more In the present infrared target detection system, simultaneously achieving both the target detection performance, as well as, computing efficiency is considered as a big task. To, address the above said issue, a non-convex triple tensor factorisation is incorporated into the existing infrared patch tensor model in the proposed work. In the proposed model (triple tensor factorisation-infrared patch tensor), local prior information using linear structure tensor and corner strength is incorporated so that the strong clutters in the background can be easily suppressed and the target can be detected correctly. Finally, the method proposed, was solved by alternating direction method of the multiplier. Large number of experiments were conducted and the results presented in the experiments suggest that the method proposed has shown good performance for background suppression, as well as, for the target detection in the clutter environment, when compared with the other baseline methods.
Infrared small target detection in a complex background has always been a challenging task in an ... more Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.
Over the last decade, many algorithms have been presented on an infrared target detection problem... more Over the last decade, many algorithms have been presented on an infrared target detection problem. This paper reviews the recent approaches in infrared small target detection algorithms. Firstly, the detailed review of Detection-Before-Track methods which are seen in recent years are discussed and problem associated with these methods are raised. Secondly, to verify the robustness of these methods, large set of experiments are conducted and comparative analysis of different baseline methods is done. In addition to this advantages and disadvantages of baseline methods is discussed. Experimental results from the dataset suggest that the patch based methods are more robust than the other filter-based methods. How to address the issues of small target detection method in complex environment and the effect of varying parameters like patch size, step size and controlling parameters is our future endeavor. Keywords: Track before detection (TBT), detect before track(DBT), infrared search and track, signal to clutter ratio, infrared patch image model
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