P. Keith Kelly
P. Keith Kelly
Institution: Agile RF Systems LLC
Email: info@rnfinity.com
This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The c...
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This article develops the applicability of non-linear processing techniques such as Compressed Sensing (CS), Principal Component Analysis (PCA), Iterative Adaptive Approach (IAA), and Multiple-input-multiple-output (MIMO) for the purpose of enhanced UAV detections using portable radar systems. The combined scheme has many advantages and the potential for better detection and classification accuracy. Some of the benefits are discussed here with a phased array platform in mind, the novel portable phased array Radar (PWR) by Agile RF Systems (ARS), which offers quadrant outputs. CS and IAA both show promising results when applied to micro-Doppler processing of radar returns owing to the sparse nature of the target Doppler frequencies. This shows promise in reducing the dwell time and increases the rate at which a volume can be interrogated. Real-time processing of target information with iterative and non-linear solutions is possible now with the advent of GPU-based graphics processing hardware. Simulations show promising results.
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Posted 10 months ago
Youchen Fan,
Youchen Fan
Institution: School of Space Information, Space Engineering University
Email: love193777@sina.com
Mingyu Qin,
Mingyu Qin
Institution: Graduate School, Space Engineering University
Email: info@rnfinity.com
Huichao Guo,
Huichao Guo
Institution: Department of Electronic and Optical Engineering, Space Engineering University
Email: info@rnfinity.com
Laixian Zhang
Laixian Zhang
Institution: Department of Electronic and Optical Engineering, Space Engineering University
Email: info@rnfinity.com
The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes...
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The range-gated laser imaging instrument can capture face images in a dark environment, which provides a new idea for long-distance face recognition at night. However, the laser image has low contrast, low SNR and no color information, which affects observation and recognition. Therefore, it becomes important to convert laser images into visible images and then identify them. For image translation, we propose a laser-visible face image translation model combined with spectral normalization (SN-CycleGAN). We add spectral normalization layers to the discriminator to solve the problem of low image translation quality caused by the difficulty of training the generative adversarial network. The content reconstruction loss function based on the Y channel is added to reduce the error mapping. The face generated by the improved model on the self-built laser-visible face image dataset has better visual quality, which reduces the error mapping and basically retains the structural features of the target compared with other models. The FID value of evaluation index is 36.845, which is 16.902, 13.781, 10.056, 57.722, 62.598 and 0.761 lower than the CycleGAN, Pix2Pix, UNIT, UGATIT, StarGAN and DCLGAN models, respectively. For the face recognition of translated images, we propose a laser-visible face recognition model based on feature retention. The shallow feature maps with identity information are directly connected to the decoder to solve the problem of identity information loss in network transmission. The domain loss function based on triplet loss is added to constrain the style between domains. We use pre-trained FaceNet to recognize generated visible face images and obtain the recognition accuracy of Rank-1. The recognition accuracy of the images generated by the improved model reaches 76.9%, which is greatly improved compared with the above models and 19.2% higher than that of laser face recognition.
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Posted 10 months ago
Ilias Gialampoukidis,
Ilias Gialampoukidis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: heliasgj@iti.gr
Thomas Papadimos,
Thomas Papadimos
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
Stelios Andreadis,
Stelios Andreadis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
Stefanos Vrochidis,
Stefanos Vrochidis
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
Ioannis Kompatsiaris
Ioannis Kompatsiaris
Institution: Information Technologies Institute, Centre for Research and Technology Hellas
Email: info@rnfinity.com
This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve perfor...
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This paper discusses the importance of detecting breaking events in real time to help emergency response workers, and how social media can be used to process large amounts of data quickly. Most event detection techniques have focused on either images or text, but combining the two can improve performance. The authors present lessons learned from the Flood-related multimedia task in MediaEval2020, provide a dataset for reproducibility, and propose a new multimodal fusion method that uses Graph Neural Networks to combine image, text, and time information. Their method outperforms state-of-the-art approaches and can handle low-sample labelled data.
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Posted 10 months ago
Wenbo Wan,
Wenbo Wan
Institution: School of Information Science and Engineering
Email: wanwenbo@sdnu.edu.cn
Jiande Sun,
Jiande Sun
Institution: School of Information Science and Engineering
Email: jiandesun@sdnu.edu.cn
Javier Del Ser,
Javier Del Ser
Institution: TECNALIA, Basque Research and Technology Alliance (BRTA)
Email: info@rnfinity.com
Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been...
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Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area.
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Posted 10 months ago
Kunhao Yuan,
Kunhao Yuan
Institution: Loughborough University, UK
Email: info@rnfinity.com
Gerald Schaefer,
Gerald Schaefer
Institution: Loughborough University, UK
Email: info@rnfinity.com
Yifan Wang,
Yifan Wang
Institution: Loughborough University, UK
Email: info@rnfinity.com
Xiyao Liu
Xiyao Liu
Institution: Central South University, China
Email: info@rnfinity.com
Weakly supervised semantic segmentation (WSSS) has gained significant popularity as it relies only on weak labels such as image level annotations rather than the pixel level annotations required by supervised semantic segmentation (SSS) methods. Despite drastically reduced annotation costs, typical ...
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Weakly supervised semantic segmentation (WSSS) has gained significant popularity as it relies only on weak labels such as image level annotations rather than the pixel level annotations required by supervised semantic segmentation (SSS) methods. Despite drastically reduced annotation costs, typical feature representations learned from WSSS are only representative of some salient parts of objects and less reliable compared to SSS due to the weak guidance during training. In this paper, we propose a novel Multi-Strategy Contrastive Learning (MuSCLe) framework to obtain enhanced feature representations and improve WSSS performance by exploiting similarity and dissimilarity of contrastive sample pairs at image, region, pixel and object boundary levels. Extensive experiments demonstrate the effectiveness of our method and show that MuSCLe outperforms current state-of-the-art methods on the widely used PASCAL VOC 2012 dataset.
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Posted 10 months ago
Renato Cordeiro de Amorim
Renato Cordeiro de Amorim
Institution: School of Computer Science and Electronic Engineering
Email: r.amorim@essex.ac.uk
DBSCAN is arguably the most popular density-based clustering algorithm, and it is capable of recovering non-spherical clusters. One of its main weaknesses is that it treats all features equally. In this paper, we propose a density-based clustering algorithm capable of calculating feature weights rep...
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DBSCAN is arguably the most popular density-based clustering algorithm, and it is capable of recovering non-spherical clusters. One of its main weaknesses is that it treats all features equally. In this paper, we propose a density-based clustering algorithm capable of calculating feature weights representing the degree of relevance of each feature, which takes the density structure of the data into account. First, we improve DBSCAN and introduce a new algorithm called DBSCANR. DBSCANR reduces the number of parameters of DBSCAN to one. Then, a new step is introduced to the clustering process of DBSCANR to iteratively update feature weights based on the current partition of data. The feature weights produced by the weighted version of the new clustering algorithm, W-DBSCANR, measure the relevance of variables in a clustering and can be used in feature selection in data mining applications where large and complex real-world data are often involved. Experimental results on both artificial and real-world data have shown that the new algorithms outperformed various DBSCAN type algorithms in recovering clusters in data.
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Posted 10 months ago
Rui Zhu,
Rui Zhu
Institution: a Faculty of Actuarial Science and Insurance, Bayes Business School, City
Email: info@rnfinity.com
Fei Zhou,
Fei Zhou
Institution: College of Information Engineering
Email: info@rnfinity.com
Wenming Yang
Wenming Yang
Institution: Department of Electronic Engineering, Graduate School at Shenzhen
Email: info@rnfinity.com
Image quality assessment is usually achieved by pooling local quality scores. However, commonly used pooling strategies, based on simple sample statistics, are not always sensitive to distortions. In this short communication, we propose a novel perspective of pooling: reliable pooling through statis...
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Image quality assessment is usually achieved by pooling local quality scores. However, commonly used pooling strategies, based on simple sample statistics, are not always sensitive to distortions. In this short communication, we propose a novel perspective of pooling: reliable pooling through statistical hypothesis testing, which enables effective detection of subtle changes of population parameters when the underlying distribution of local quality scores is affected by distortions. To illustrate the significance of this novel perspective, we design a new pooling strategy utilising simple one-sided one-sample t -test. The experiments on benchmark databases show the reliability of hypothesis testing-based pooling, compared with state-of-the-art pooling strategies.
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Posted 10 months ago
Jinming Duan,
Jinming Duan
Institution: School of Computer Science
Email: j.duan@bham.ac.uk
Joseph Bartlett,
Joseph Bartlett
Institution: School of Computer Science
Email: info@rnfinity.com
Wenqi Lu
Wenqi Lu
Institution: Tissue Image Analytics Centre, Department of Computer Science
Email: info@rnfinity.com
In this work, we investigate image registration in a variational framework and focus on regularization generality and solver efficiency. We first propose a variational model combining the state-of-the-art sum of absolute differences (SAD) and a new arbitrary order total variation regularization term...
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In this work, we investigate image registration in a variational framework and focus on regularization generality and solver efficiency. We first propose a variational model combining the state-of-the-art sum of absolute differences (SAD) and a new arbitrary order total variation regularization term. The main advantage is that this variational model preserves discontinuities in the resultant deformation while being robust to outlier noise. It is however non-trivial to optimize the model due to its non-convexity, non-differentiabilities, and generality in the derivative order. To tackle these, we propose to first apply linearization to the model to formulate a convex objective function and then break down the resultant convex optimization into several point-wise, closed-form subproblems using a fast, over-relaxed alternating direction method of multipliers (ADMM). With this proposed algorithm, we show that solving higher-order variational formulations is similar to solving their lower-order counterparts. Extensive experiments show that our ADMM is significantly more efficient than both the subgradient and primal-dual algorithms particularly when higher-order derivatives are used, and that our new models outperform state-of-the-art methods based on deep learning and free-form deformation. Our code implemented in both Matlab and Pytorch is publicly available at https://github.com/j-duan/AOTV.
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Posted 10 months ago
Seyed Hossein Amirshahi,
Seyed Hossein Amirshahi
Institution: Amirkabir University of Technology (Tehran Polytechnic), School of Material Engineering and Advanced Processes
Email: hamirsha@aut.ac.ir
Ida Rezaei,
Ida Rezaei
Institution: Amirkabir University of Technology (Tehran Polytechnic), School of Material Engineering and Advanced Processes
Email: info@rnfinity.com
Ali Akbar Mahbadi
Ali Akbar Mahbadi
Institution: Amirkabir University of Technology (Tehran Polytechnic), School of Material Engineering and Advanced Processes
Email: info@rnfinity.com
Two regression methods, namely, Support Vector Regression (SVR) and Kernel Ridge Regression (KRR), are used to reconstruct the spectral reflectance curves of samples of Munsell dataset from the corresponding CIE XYZ tristimulus values. To this end, half of the samples (i.e., the odd ones) were used ...
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Two regression methods, namely, Support Vector Regression (SVR) and Kernel Ridge Regression (KRR), are used to reconstruct the spectral reflectance curves of samples of Munsell dataset from the corresponding CIE XYZ tristimulus values. To this end, half of the samples (i.e., the odd ones) were used as training set while the even samples left out for the evaluation of reconstruction performances. Results were reviewed and compared with those obtained from Principal Component Analysis (PCA) method, as the most common context-based approach. The root mean squared error (RMSE), goodness fit coefficient (GFC), and CIE LAB color difference values between the actual and reconstruct spectra were reported as evaluation metrics. However, while both SVR and KRR methodologies provided better spectral and colorimetric performances than the classical PCA method, the computation costs were considerably longer than PCA method.
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Posted 10 months ago
Current developments in object tracking and detection techniques have directed remarkable improvements in distinguishing attacks and adversaries. Nevertheless, adversarial attacks, intrusions, and manipulation of images/ videos threaten video surveillance systems and other object-tracking applicatio...
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Current developments in object tracking and detection techniques have directed remarkable improvements in distinguishing attacks and adversaries. Nevertheless, adversarial attacks, intrusions, and manipulation of images/ videos threaten video surveillance systems and other object-tracking applications. Generative adversarial neural networks (GANNs) are widely used image processing and object detection techniques because of their flexibility in processing large datasets in real-time. GANN training ensures a tamper-proof system, but the plausibility of attacks persists. Therefore, reviewing object tracking and detection techniques under GANN threats is necessary to reveal the challenges and benefits of efficient defence methods against these attacks. This paper aims to systematically review object tracking and detection techniques under threats to GANN-based applications. The selected studies were based on different factors, such as the year of publication, the method implemented in the article, the reliability of the chosen algorithms, and dataset size. Each study is summarised by assigning it to one of the two predefined tasks: applying a GANN or using traditional machine learning (ML) techniques. First, the paper discusses traditional applied techniques in this field. Second, it addresses the challenges and benefits of object detection and tracking. Finally, different existing GANN architectures are covered to justify the need for tamper-proof object tracking systems that can process efficiently in a real-time environment.
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Posted 10 months ago