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15th IEEE International conference on Industrial and information systems, ICIIS 2020

作者： Misra, Indranil Manthira Moorthi, S. Dhar, Debajyoti Signal and Image Processing Group Space Applications Centre Indian Space Research Organisation Ahmedabad India

ISBN: (纸本)9781728185248

optical remote sensing images contain cloud pixels, which need to be detected and masked for different earth observation studies and applications. In literature, various algorithms are reported which can detect cloud in multi-spectral remote sensing images. In this paper, we have developed an approach based on spectral angle computation that measures the radiometric gap between a reference pixel and input image pixels. Reference spectra is determined using mode seeking technique by taking multiple cloud pixels from multi-temporal images covering different land terrain. The processing chain is developed and tested in Resourcesat LISS-3 surface reflectance data. Result section shows that technique developed can determine thick cloud pixels with high confidence interval and compared with other novel cloud detection methods. © 2020 IEEE.

关键词： Pixels

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Analysis of the Matching Pursuit Reconstruction Algorithm Based on Compression Sensing 8th

Analysis of the Matching Pursuit Reconstruction Algorithm Ba...

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8th International conference on Communications, Signal processing, and systems, CSPS 2019

作者： Wang, Zhihong Wang, Hai Sun, Guiling Li, Yangyang Electronic Information Laboratorial Teaching Center College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China

ISBN: (纸本)9789811394089

We introduce the concepts of compression sensing and signal reconstruction, and then explained the minimum 0 norm and minimum l1 norm reconstruction algorithms. We extensively study existing reconstruction algorithms and take the advantages of existing algorithms to propose a new reconstruction algorithm. The normalized random Gaussian matrix is used as the measurement matrix. We chose three different sparse signals for comparisons, namely the three classic CS inputs including the time domain sparse signal, the frequency domain compressible signal and the sparsity unknown frequency domain compressible signal. Finally, a series of simulation results show that the proposed algorithm can achieve signal reconstruction with high probability and high precision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Signal reconstruction

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OLIVES dataset: ophthalmic labels for investigating visual eye semantics 22

OLIVES dataset: ophthalmic labels for investigating visual e...

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Proceedings of the 36th International conference on Neural information processing systems

作者： Mohit Prabhushankar Kiran Kokilepersaud Yash-yee Logan Stephanie Trejo Corona Ghassan AlRegib Charles Wykoff OLIVES at the Centre for Signal and Info. Processing Georgia Tech Atlanta GA Retina Consultants Texas Retina Consultants of America Houston Texas

ISBN: (纸本)9781713871088

Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional optical Coherence Tomography (OCT) scans. Clinical practitioners use all available data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between all relevant data over a treatment period. Existing datasets are limited in that they neither provide data nor consider the explicit relationship modeling between the data modalities. In this paper, we introduce the Ophthalmic Labels for Investigating visual Eye Semantics (OLIVES) dataset that addresses the above limitation. This is the first OCT and near-IR fundus dataset that includes clinical labels, biomarker labels, disease labels, and time-series patient treatment information from associated clinical trials. The dataset consists of 1268 near-IR fundus images each with at least 49 OCT scans, and 16 biomarkers, along with 4 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. We benchmark the utility of OLIVES dataset for ophthalmic data as well as provide benchmarks and concrete research directions for core and emerging machine learning paradigms within medical image analysis.

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Revolutionizing waste management: harnessing the power of IoT

Revolutionizing waste management: harnessing the power of Io...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Ankita Karale Anand Singh Rajawat S. B. Goyal Computer Engineering Sandip Foundation's Sandip Institute of Research Center Nashik India School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non- recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management bas

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Kurtosis-based connected components method for detecting small and transparent foreign objects in semiopaque bottles

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JOURNAL OF ELECTRONIC IMAGING 2020年第4期29卷 041009-041009页

作者： Belhadj, Djallel Nuzillard, Danielle Vrabie, Valeriu Haddad, Madjid Univ Reims CReSTIC EA 3804 Reims France Segula Engn Rueil Malmaison France

In recent years, driven by new standards and image brand, product quality has become highly critical in the food industry. Particularly concerning the beverage industry, the potential presence of very small foreign objects, especially glass fragments, has to be checked. The visible optical domain offers an advantageous alternative solution to the expensive and intrusive x-ray systems. However, such a solution requires the development of robust processing algorithms satisfying real-time industrial constraints, which is a very challenging task. A detection method based on kurtosis and local information that emphasizes connected components is proposed, as well as two robust criteria that give a greater robustness to the detector. A specific kurtosis is calculated using an inclined frame definition that is based on both time and spatial dimensions. Such kurtosis increases the detection capacity of the moving objects and allows the estimation of their direction without adding more costs. The kurtosis being very sensitive to outliers, noisy objects that are very small are filtered, allowing efficient detection of foreign objects, such as glass fragments, as long as they are bigger than the noisy objects. In case of big foreign objects, the low sensitivity of the kurtosis is compensated for by the large detected surface. The proposed detection method can be directly applied on video sequences acquired by a standard RGB camera in industrial environments. The experimental results show the effectiveness of the method in detecting real random foreign objects, regardless of their size or their transparency, in various semiopaque bottles. (C) 2020 SPIE and IS&T

关键词： object detection kurtosis image processing foreign body detection semiopaque bottles RGB camera

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Demonstrating Advanced Machine Learning and Neuromorphic Computing Using IBM’s NS16e 1

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Science and information conference, SAI 2020

作者： Barnell, Mark Raymond, Courtney Wilson, Matthew Isereau, Darrek Cote, Eric Brown, Dan Cicotta, Chris Air Force Research Laboratory Information Directorate RomeNY13441 United States SRC Inc. 6225 Running Ridge Road North SyracuseNY13212 United States

ISBN: (数字)9783030522490

ISBN: (纸本)9783030522483

The human brain can be viewed as an extremely power-efficient biological computer. As such, there have been many efforts to create brain-inspired processing systems to enable advances in low-power data processing. An example of brain-inspired processing architecture is the IBM TrueNorth Neurosynaptic System, a Spiking Neural Network architecture for deploying ultra-low power machine learning (ML) models and algorithms. For the first time ever, an advanced scalable computing architecture was demonstrated using 16 TrueNorth neuromorphic processors containing in aggregate over 16 million neurons. This system, called the NS16e, was used to demonstrate new ML techniques including the exploitation of optical and radar sensor data simultaneously, while consuming a fraction of the power compared to traditional Von Neumann computing architectures. The number of applications that have requirements for computing architectures that can operate in size, weight and power-constrained environments continues to grow at an outstanding pace. These applications include processors for vehicles, homes, and real-time data exploitation needs for intelligence, surveillance, and reconnaissance missions. This research included the successful exploitation of optical and radar data using the NS16e system. processing performance was assessed, and the power utilization was analyzed. The NS16e system never used more than 15 W, with the contribution from the 16 TrueNorth processors utilizing less than 5 W. The image processing throughput was 16,000 image chips per second, corresponding to 1,066 image chips per second for each watt of power consumed. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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Image denoising with bank filters using the maximum likelihood estimation 5

Image denoising with bank filters using the maximum likeliho...

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conference on Computational Imaging V

作者： Bezuglov, D. Voronin, V. Russian Customs Acad Rostov Branch 20 Budyonnovsky Ave Rostov Na Donu 344002 Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Vadkovsky 1 Moscow 127055 Russia Don State Tech Univ Lab Math Methods Image Proc & Intelligent Comp Vi Rostov Na Donu Russia

ISBN: (纸本)9781510635692

Recently, systems of intelligent image processing have been intensively developing. When solving problems of high complexity, modern methods of technical vision are required to increase the efficiency of the digital image processing process with the variability of the working scene, heterogeneity of objects, and interference. One of the trends in the development of modern information technologies is the development of highly efficient methods and algorithms for analyzing signals and images with background noises. When constructing highly-effective techniques and algorithms for image denoising, an a priori knowledge of the characteristics of distorting interference is required. In practice, in most cases, such information is missing. In this paper, we develop a new image denoising method with bank filters using the maximum likelihood estimation. We propose a new approach to using a set of heterogeneous digital image filters, such as a median filter, a Gabor filter, a non-local average filter, a spline filter, a wavelet filter, and others. The feasibility of this approach is determined by the fact that, as a rule, when considering the filtering process, a Gaussian character of the noise distribution density is assumed. Moreover, the effectiveness of various filtering methods on real images recorded against the background of noise will be different. This is due to the fact that under real observation conditions, the noise distribution density may differ from the Gaussian one. This explains the difference in the qualitative filtering characteristics of the same image by different filters. Experimental studies have shown the operability and high efficiency of the developed method, which allows improving the quality of image filtering.

关键词： image denoising median filter Gabor filter wavelet filter filter bank maximum likelihood estimation

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Dynamic regret of convex and smooth functions 34

Dynamic regret of convex and smooth functions

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34th conference on Neural information processing systems, NeurIPS 2020

作者： Zhao, Peng Zhang, Yu-Jie Zhang, Lijun Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss incurred by the online algorithm and that of any feasible comparator sequence. Let T be the time horizon and PT be the path-length that essentially reflects the non-stationarity of environments, the state-of-the-art dynamic regret is O(pT(1 + PT)). Although this bound is proved to be minimax optimal for convex functions, in this paper, we demonstrate that it is possible to further enhance the dynamic regret by exploiting the smoothness condition. Specifically, we propose novel online algorithms that are capable of leveraging smoothness and replace the dependence on T in the dynamic regret by problem-dependent quantities: the variation in gradients of loss functions, the cumulative loss of the comparator sequence, and the minimum of the previous two terms. These quantities are at most O(T) while could be much smaller in benign environments. Therefore, our results are adaptive to the intrinsic difficulty of the problem, since the bounds are tighter than existing results for easy problems and meanwhile guarantee the same rate in the worst case. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Comparators (optical)

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Evaluating Killer Drone Defense: NATO SPS Project "Anti-Drones" Field Trials 16th

Evaluating Killer Drone Defense: NATO SPS Project "Anti-Dron...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Lupidi, Alberto Mancuso, Francesco Meucci, Giulio Jajaga, Edmond Rushiti, Veton Cantelli-Forti, Alessandro Radar and Surveillance Systems National Laboratory CNIT Pisa56124 Italy Department of Information Engineering University of Pisa Pisa56122 Italy Mother Teresa University Skopje1000 Macedonia

ISBN: (纸本)9783031861611

The growing number of Unmanned Aerial Vehicles (UAVs) in both military and civilian sectors necessitates advanced tracking and detection systems. This paper presents the development and field trial results of a high-resolution radar and optical system for UAV detection conducted within the NATO SPS project "Anti-Drones". By using a multichannel linear frequency modulated continuous wave (FMCW) radar architecture, the system demonstrates robust capabilities in identifying and tracking various UAV types, including "mini" and "micro" drones. Field trials evaluated detection accuracy, tracking reliability, and payload recognition. Advanced signal processing and machine learning (ML) algorithms enhanced real-time recognition and reduced false alarms. Integrating radar data with optical camera tracking further improved performance in multi-target scenarios. This study highlights the system’s potential for military and civilian applications in addressing UAV threats. Future research will focus on refining data fusion algorithms and extending operational range based on trial insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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Shift, rotation and scale invariant optical fingerprint verification system with double random phase encoding

Shift, rotation and scale invariant optical fingerprint veri...

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3rd Asian conference on Artificial Intelligence Technology, ACAIT 2019

作者： Zhuo, Shengkai Jiao, Shuming Zou, Wenbin Wang, Na Li, Xia Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Nanophotonics Research Centre College of Optoelectronic Engineering Shenzhen University Shenzhen China

Biometric features such as fingerprint images can be authenticated by not only computer algorithms but also optical systems. In previous works, double random phase encoding (DRPE) optical lens system is employed for fingerprint verification. However, such a system can yield authentication failure when the key is generated from a slightly distorted fingerprint image. In this work, a fingerprint image pre-processing scheme for DRPE fingerprint optical authentication system is proposed. Simulated experimental results demonstrate the proposed method has significantly better robustness to shift, rotation and scale variance than some of the conventional DRPE-based approaches. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Authentication

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