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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

共 650 条记录，以下是21-30 订阅

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Research on Radar Imaging Method Based on Tensor Neural Network 16

Research on Radar Imaging Method Based on Tensor Neural Netw...

引用

16th International Conference on Signal processing systems, ICSPS 2024

作者： Qiu, Tian Liu, Nan Yang, Minglei National Key Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781510689251

LFM signals, as the most commonly used form of signals in radar systems, are of significant research value. At this stage, compressed sensing (CS) has significant potential to improve the image quality of radar imaging. However, the existing CS theories have limited ability to improve the imaging capability of LFM signals, and always suffer from low imaging efficiency, parameters need to be manually adjusted, and poor suppression of side flaps. In this paper, we propose a neural network imaging algorithm based on tensor compressed perception, called 2D-ISTA-Net. this algorithm accomplishes distance-azimuth 2D radar imaging by combining a model-based iterative threshold shrinkage (ISTA) algorithm and a data-driven deep learning model. First, the sparse recovery model of the LFM signal is obtained analytically, and then, the 2D-ISTA optimization algorithm is unfolded into a deep network structure in which all parameters are optimized based on data-driven auto-learning instead of manual tuning. Subsequently, the network learning is trained on the scattering point images in the training set to obtain a 2D-ISTA-Net network with autonomous parameter tuning capability. Furthermore, we compared this algorithm with some existing CS algorithms. the data show that 2D-ISTA-Net significantly improves the computing power and has a lower side flap level compared with the traditional CS algorithm. Experiments show that 2D-ISTA-Net has good reconstruction performance. © 2025 SPIE.

关键词： Radar imaging

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image Pre-processing Effects on Attention Modules in Facial Emotion Recognition 12

Image Pre-processing Effects on Attention Modules in Facial ...

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12th IFAC symposium on Biological and Medical systems (BMS)

作者： Arabian, H. Wagner-Hartl, V Moeller, K. Furtwangen Univ Inst Tech Med ITeM D-78054 Villingen Schwenningen Germany Furtwangen Univ Campus Tuttlingen D-78532 Tuttlingen Germany

Neural Networks have achieved a reputation for their ability to outperform traditional machine learning algorithms. the robustness of network models to unseen data is important. In this study the implementation of an attention module to an existing network architecture is studied for robustness improvement with extensive image pre-processing for facial emotion recognition, a component in a closed loop feedback system being developed for the emotional training of people with autism spectrum disorder. the squeeze and excitation (SE) attention module was selected and combined with the network architecture of VGG16. the performance of the model, trained with the OULU-CASIA dataset, was based on statistical data generated from validation accuracies with a 10-fold partition, while the robustness analysis was based on data generated from cross database prediction accuracies. the visualization technique of Grad-CAM was used to observe the regions of impact on classification. Validation accuracies of up to 98.39% were achieved and generalization performance was 52.43% on the FACES, and 26.10% on the JAFFE datasets. the improvements of the SE over the base model were observed in the visualizations of the validation datasets. Data analyzed showed that extensive image pre-processing diminished effects of SE in relation to model robustness. Copyright (C) 2024 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Attention modules Autism spectrum disorder Deep learning Model robustness Prediction visualizations

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Prior Case Retrieval for the Court of Cassation of Turkey 16

Prior Case Retrieval for the Court of Cassation of Turkey

引用

16th International Conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Ozturk, Ceyhun E. Koksal, Omer Ozcelik, S. Baris Koc, Aykut Bilkent Univ Dept Elect & Elect Engn Ankara Turkey ASELSAN Res Ctr Ankara Turkey Bilkent Univ Fac Law Ankara Turkey Natl Magnet Resonance Res Ctr UMRAM Ankara Turkey

ISBN: (纸本)9781665464956

this work introduces the first prior case retrieval models for Turkish courts. We investigated the rulings of the Court of Cassation of Turkey. Since law professionals have to find texts they are interested in from enormous legal databases and computers can process large amounts of text swiftly, information retrieval algorithms are helpful for law professionals. the information retrieval algorithms utilized in this work are recurrent neural network autoencoders, and the combinations of recurrent neural network autoencoders with BM25 algorithms. the combination of the long-short term memory autoencoder with ATIRE BM25 achieves the best scores on our dataset.

关键词： natural language processing law deep learning information retrieval NLP in law AI in law prior legal case retrieval

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An image Encryption Algorithm Suitable for Medical images 16

An Image Encryption Algorithm Suitable for Medical Images

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16th IEEE International Scientific Conference on Informatics, Informatics 2022

作者： Oravec, Jakub Ovsenik, Lubos Lapcak, Maros Zdravecky, Norbert Andrejcik, Samuel Technical University of Košice Feei Dept. of Electronics and Multimedia Communications Košice Slovakia

ISBN: (纸本)9798350310344

Security of data becomes more and more important each day. the importance is even more pronounced if the data contain sensitive information, such as those shown in medical images. there are already several solutions that can be used in order to protect medical images during their storage or transmission. the most popular of those is probably encryption by conventional encryption algorithms such as Advanced Encryption Standard. this proposal tries to investigate possibility of using dedicated image encryption algorithms that have applications in fields of steganography and biometric systems for encryption of medical images. the dedicated image encryption algorithms can be easily adjusted for processing medical images, what can be verified by several experiments. Main advantage of this proposal lies in an addition of a substitution block, which should increase the security of whole algorithm. the performance of our solution will be evaluated by several numerical parameters and those will be compared with results reported in similar works. © 2022 IEEE.

关键词： Cryptography

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DOMAIN ADAPTATION IN image DEHAZING: EXPLORING thE USAGE OF imageS FROM VIRTUAL SCENARIOS 16

DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF ...

引用

16th International Conference on Computer Graphics, Visualization, Computer Vision and image processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022

作者： Sappa, Angel D. Suárez, Patricia L. Velesaca, Henry O. Carpio, Darío Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 P.O. Box 09 Vía Perimetral 01-5863 Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789898704429

this work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator;these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. the adaptation process requires just a few images to fine-tune the model parameters. the proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. © MCCSIS *** rights reserved.

关键词： Deep learning

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AINoC: New Interconnect for Future Deep Neural Network Accelerators 16th

AINoC: New Interconnect for Future Deep Neural Network Accel...

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16th International Workshop on Design and Architecture for Signal and image processing (DASIP) held jointly with the 18th HiPEAC Conference

作者： Krichene, Hana Prasad, Rohit Mouhagir, Ayoub Univ Paris Saclay List CEA F-91120 Palaiseau France

ISBN: (纸本)9783031299698;9783031299704

Data exchanges can be significant in the Deep Neural Network (DNN) algorithms. the interconnection between computing resources can quickly have a substantial impact on the overall performance of the architecture and its energy efficiency. Similarly, access to the different memories of the system, with potentially high data sharing, is a critical point. To overcome these problems, in this paper, we propose a new interconnect network, called AINoC, for future DNN accelerators, which require more flexibility and less power consumption to facilitate their integration into artificial intelligence (AI) edge systems. AINoC is based on (1) parallel routing that ensures communication/computation overlap to improve performance and (2) data reuse (filters, image inputs, and partial sums) to reduce multiple memory accesses. In the experiment section, AINoC can speedup LeNet5 convolution layers by up to 71.74x in latency performance w.r.t. a RISC-V-based CPU and also speedup MobileNetV2 convolution layers by up to 2.35x in latency performance w.r.t. a dataflow architecture featuring row-stationary execution style. AINoC provides any-to-any data exchange with wide interfaces (up to 51.2 GB/s) to support long bursts (up to 384 flits/cycle with packed data, i.e., 3 * 8-bit data in a 32-bit wide datapath) while executing LeNet5 and MobileNetV2. AINoC supports flexible communication with many multicast/broadcast requests with non-blocking transfers. Parallel communication in AINoC can provide up to 128x more throughput (flits/cycle) and bandwidth (GB/s), using parallel routing with respect to single-path routing while executing convolution layers of LeNet5 and MobiletNetV2.

关键词： On-Chip Interconnect Network-on-Chip DNN CNN dataflow execution parallel processing

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Hierarchical DNN-Based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions 16

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

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16th International Conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Yu, Cheng-Hsuan Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781665464956

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both largescaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for oversegmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： image segmentation superpixel merging deep learning

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CyberSaver - A Machine Learning Approach to Detection of Cyber Bullying 16

CyberSaver - A Machine Learning Approach to Detection of Cyb...

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16th International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Jia, Hii Lee Hameed, Vazeerudeen Abdul School, Muhammad Ehsan Rana Asia Pacific Univ Sch Comp Technol Pk Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665426787

In this modern era of extensive use of online resources there has been reports of numerous cases of cyberbullying. Although awareness through medical health support systems such as counselling and psychological assistance is available, a system to combat threats is needed to handle the increasing rate of cyber bullying. this paper presents a model that can be used to detect and report cyberbullying with the use of machine learning techniques. A careful selection of the machine learning algorithms has been identified that could enable better accurate detection. the model was transformed into a prototype in python to evaluate the effectiveness of the model in detecting cyber bullying. the proposed model primarily focusses on test based and image-based threats as they are more common than other forms of cyber bullying.

关键词： Convolutional Neural Network Cyber Bullying Machine Learning Support Vector Machine Text processing image processing

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A Review on Designing of Memory Computing Architecture for image Enhancement in AI Applications 1

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16th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Radhika, C. Ganesh, G.V. Babu, P. Ashok Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India

ISBN: (数字)9783031364020

ISBN: (纸本)9783031364013

this paper presents the random spray retinex (RSR) algorithm, which is an efficient image enhancement technique with the potential to improve image quality. However, the computational complexity of the algorithm, as well as the hardware and memory units it requires, may limit its applicability in contexts with constrained resources, such as Internet of things systems. image augmentation is increasingly important for improving the performance of many modern applications, including artificial intelligence (AI). Our research confirms that RSR can enhance segmentation accuracy. To overcome the limitations of the RSR algorithm, we propose a memristor-based in-memory computing architecture, utilizing Resistive RAM (RRAM) technology, which provides low-power solutions at a low cost. We evaluate the performance of various RSR algorithms in terms of processing time on this architecture. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Memristors

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Mechanisms For Using image Properties And Neural Networks In Identification Of Micro-Objects 16

Mechanisms For Using Image Properties And Neural Networks In...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Jumanov, Isroil I. Safarov, Rustam A. Djumanov, Olimjon I. Samarkand State University Applied Mathimatics and Informatics Samarkand Uzbekistan

ISBN: (纸本)9781665451628

the problem of visualization, recognition, classification of images of micro-objects, in particular, pollen grains, unicellular organisms, fingerprints based on the definition of their variety, belonging to a class, the use of information of geometric shapes, morphology, dynamic, specific characteristics, unique features of neural networks has been investigated, in control systems of industrial and technological complexes, environmental monitoring, ecology, and medical diagnoses. Methods, learning algorithms, component computational schemes of neural networks have been developed, which provide the best quality of image identification in conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing. Mathematical expressions are obtained for estimating identification errors associated with information distortions at the measurement, input, and transmission stages due to nonstationarity, the inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of pollen grains has been built and implemented, which includes algorithms for a three-layer, loosely coupled neural network, Hopfield's network, bidirectional associative memory, Kohonen. Results are obtained for correct, incorrect recognition, and rejected pollen samples based on with-teacher and unsupervised learning algorithms, which are synthesized with cubic, biquadratic, and interpolation spline functions. © 2022 IEEE.

关键词： Interpolation

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