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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Wasserstein distributional robustness of neural networks 37

Wasserstein distributional robustness of neural networks

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Bai, xingjian Jiang, Yifan He, Guangyi Oblój, Jan Department of Computer Science University of Oxford United Kingdom Mathematical Institute University of Oxford United Kingdom

ISBN: (纸本)9781713899921

Deep neural networks are known to be vulnerable to adversarial attacks (AA).For an image recognition task, this means that a small perturbation of the original can result in the image being *** of such attacks as well as methods of adversarial training against them are subject of intense *** re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions leveraging recent insights from DRO sensitivity *** consider a set of distributional threat *** the traditional pointwise attacks, which assume a uniform bound on perturbation of each input data point, distributional threat models allow attackers to perturb inputs in a nonuniform *** link these more general attacks with questions of out-of-sample performance and Knightian *** evaluate the distributional robustness of neural networks, we propose a first-order AA algorithm and its multistep *** attack algorithms include Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) as special ***, we provide a new asymptotic estimate of the adversarial accuracy against distributional threat *** bound is fast to compute and first-order accurate, offering new insights even for the pointwise *** also naturally yields out-of-sample performance *** conduct numerical experiments on CIFAR-10, CIFAR-100, imageNet datasets using DNNs on RobustBench to illustrate our theoretical *** code is available at https://***/JanObloj/W-DRO-Adversarial-Methods. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： image recognition

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HIGH-SUCCESS-RATE ISAR IMAGING OF MANEUVERING TARGETS WITH A ROBUST OPTIMAL IMAGING TIME INTERVAL SELECTION ALGORITHM

HIGH-SUCCESS-RATE ISAR IMAGING OF MANEUVERING TARGETS WITH A...

引用

IET International Radar conference 2023, IRC 2023

作者： Fan, Jiabo Shao, Shuai Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

ISBN: (纸本)9781839539954

The complex time-variant Doppler frequency of maneuvering targets leads to serious defocusing of inverse synthetic aperture radar (ISAR) imaging results. It indicates the maneuvering motion poses great challenges to traditional ISAR imaging algorithms. To address the problem, a robust optimal ISAR imaging time interval selection algorithm is proposed in this paper to improve the imaging success rate of maneuvering targets. The proposed algorithm evaluates the imaging results of different imaging time intervals by multiple criteria, and uses Dempster-Shafer (DS) evidence theory to fuse the multiple criteria to select the optimal imaging time interval. Moreover, the proposed algorithm is able to balance the contradiction between resolution and image focusing quality, providing a robust imaging time interval selection performance to obtain well-focused ISAR images of maneuvering targets. The experimental results of both simulated and real data illustrate the effectiveness and necessity of the proposed algorithm to improve the ISAR imaging success rate of maneuvering targets. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Application of computer vision methods for automated wooden planks length measurement 18

Application of computer vision methods for automated wooden ...

引用

18th Baltic Electronics conference

作者： Zabulis, Lukas Lipnickas, Arunas Augustauskas, Rytis Kaunas Univ Technol Dept Automat Kaunas Lithuania

ISBN: (数字)9781665462952

ISBN: (纸本)9781665462952

Automated inspection is a principal component in modern manufacturing aiming for Industry 4.0. With the fast development of digital image processing and machine learning algorithms, quality control is often performed by computer vision systems (CV). Following the presented topic, this paper applies the CV system for non-direct length measuring of objects moving through a conveyor. The approach involves image segmentation by a convolutional neural network and segmented object processing principles. Since high-resolution images were used, segmentation was performed in a patch manner. The algorithm also involves a custom contour analysis and the application of the least-squares method for the midline of wooden plank extraction. The proposed approach is robust to the object's non-alignment and merging factors as well as shape and wooden texture variance. The presented algorithm was tested on a custom wooden planks database gathered in a real EURO pallets production environment and confirmed to work as expected ensuring +/- 1mm length measuring accuracy and 0.97s processing timing meeting the real-time requirement of the specified system.

关键词： object segmentation deep learning computer vision digital image processing non-direct measurement

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Exploiting Richness of Learned Compressed Representation of images for Semantic Segmentation

Exploiting Richness of Learned Compressed Representation of ...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kakaiya, Ravi Sathish, Rakshith Sheet, Debdoot Sethuraman, Ramananthan Indian Institute of Technology Kharagpur West Bengal India Intel Technology India Pvt. Ltd. Bengaluru India

ISBN: (纸本)9798350313154

Autonomous vehicles and Advanced Driving Assistance systems (ADAS) have the potential to radically change the way we travel. Many such of such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to 66× while preserving the information required to perform segmentation with a dice coefficient of 0.84 as compared to 0.88 achieved using decompressed images while reducing the overall compute by 11%. © 2023 IEEE.

关键词： image compression

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Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

引用

23rd International conference on Intelligent systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. Visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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Galois Fields for Digital image and Signal processing: Evidence for the Importance of Field Specificity 5

Galois Fields for Digital Image and Signal Processing: Evide...

引用

5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Vitulyova, Yelizaveta S. Bakirov, Akhat S. Suleimenov, Ibragim E. Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev Almaty Kazakhstan National Engineering Academy of the Republic of Kazakhstan Almaty Kazakhstan

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

It is shown that any digital signal and image processing algorithms can be reduced to multivalued logic operations. The basis for this is signal models, in which each discrete level is associated with an element of the Galois field. Therefore, the development of algorithms and computing systems operating directly in Galois fields is topical. To simplify the calculation of products in Galois fields, in turn, it is convenient to pass from an element of the Galois field to the degree of a primitive element that defines this element. This allows the multiplication operation to be reduced to the addition operation. A specific example of an algorithm and a block diagram of a device that implements it, which allows such a reduction, are presented. This example demonstrates that for solving problems of digital signal and image processing, it is very important to take into account the specifics of specific Galois fields. © 2022 IEEE.

关键词： Adders

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image Recognition and Enhancement using Multi Scale Retinex and Histogram Equalization 2

Image Recognition and Enhancement using Multi Scale Retinex ...

引用

2nd International conference for Innovation in Technology, INOCON 2023

作者： Tejas, K.R. Rajalakhmi, B. Geethika, Palle Indrani, C. Kolalapudi, Ramya Swathi CSE NHCE Bangalore India

ISBN: (纸本)9798350320923

nowadays the need for a safe and at ease device is favored using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output © 2023 IEEE.

关键词： image enhancement

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Attention-Based Deep Learning Algorithm in Natural Language processing for Optical Character Recognition

Attention-Based Deep Learning Algorithm in Natural Language ...

引用

2023 International conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Prem Kumar, M. Logesh Babu, R. Sunil, G. Sampathkumar, J. Chakraborty, Subhra Global Technical Marketing Element 14 Bangalore India Kpr Institute of Engineering & Technology Department of Information Technology Coimbatore India School of Computer Science & Artificial Intelligence Sr University Warangal India Mahendra College of Engineering Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

Optical Character Recognition (OCR) is commonly referred as text recognition which poses a substantial issue in the computer vision tasks. Conventional optical character recognition systems frequently suffer in handwritten document recognition. To solve this, Deep Learning (DL) models have emerged as a powerful and advanced solution for character recognition. The present research offers a unique CNN-RNN model with an Attention Mechanism (CNN-RNN-AM) for English image character recognition. The process comprised many important phases, beginning with image collection from a user-defined dataset, then image pre-processesing includes grayscale conversion and noise reduction. For effective character recognition, the proposed approach integrates the segmentation process at multiple levels, including line segmentation, word segmentation, and character segmentation. Finally, the CNN-RNN with an attention mechanism is deployed for character recognition. The experimental findings demonstrated the remarkable efficacy of the suggested CNN-RNN-AM model. It outperformed other compared models by attaining an excellent character recognition accuracy of 99.89%. © 2023 IEEE.

关键词： Attention Mechanism Computer Vision Deep Learning Grayscale Conversion and Optical Character Recognition

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Enhancing Lane Detection for Autonomous Vehicles Using image processing and Real-Time Analysis 11

Enhancing Lane Detection for Autonomous Vehicles Using Image...

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11th International Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Saved, Mohammed S. Elsharkawy, Mostafa Kobasy, Bassam Eltabakh, Habib Egypt-Japan University of Science and Technology Department of Electronics and Communications Engineering New Borg El-Arab City Alexandria21934 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350369328

Lane detection is an essential function for autonomous driving systems since it provides information about the vehicle's position and the road's geometry. However, there are problems with existing lane detection algorithms, such as shadows, uneven lighting, and traffic signs. In this work, a conventional image processing-based lane detection method based on the Canny edge detector and Hough transform is implemented. The algorithm is implemented and tested on two different platforms, the Raspberry Pi 4 B+ and the Nvidia Jetson Nano boards, utilizing both recorded and live-streaming videos. Our evaluation shows that the technique works effectively for lane detection, with Nvidia Jetson Nano outperforming Raspberry Pi 4 B+. The proposed system can be used for many autonomous driving tasks, such as lane changing, lane keeping, and collision avoidance. © 2023 IEEE.

关键词： Hough transforms

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An Improved Blind Deconvolution for Restoration of Blurred images Using Ringing Removal processing 4th

An Improved Blind Deconvolution for Restoration of Blurred I...

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4th International conference on Computer and Communication Technologies, IC3T 2022

作者： Dimlo, U. M. Fernandes Narasimharao, Jonnadula Laxmaiah, Bagam Srinath, E. Rani, D. Sandhya Sandhyarani Kumar, Voruganti Naresh Department of CSE Sreyas Institute of Engineering and Technology Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of CSE Keshav Memorial Institute of Technology UGC Autonomous Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9789811985621

One of the most difficult challenges in image processing is restoring a defocused image by reducing blur and noise. Blurring characterizes image deterioration, and recovery is accomplished by point spread function estimation and ideal image estimation processing was repeated. Ringing, or wavelike artifacts that arise along strong edges, is a difficult challenge in latent image restoration. Therefore, this paper will introduce an improved blind deconvolution for restoring blurry images using ringing removal process. This paper provides an improved deconvolution technique that uses blur kernel prediction based on dark channels before achieving clear image recovery. To hold picture information and beautify the rims of the ringing impact created at some point of the authentic clean picture recuperation process, an easy bilateral clear out is used. The ringing removal method L0 regularization is used with the restoration process, which can estimate a sharper image. By removing the difference map from the final deconvolution result, it is possible to get a clearer picture without ringing. Finally, the results are presented in terms of performance parameters such as signal-to-noise ratio (SNR), mean squared error (MSE), and peak signal-to-noise ratio (PSNR).The results show that the performance parameters of the improved blind deconvolution model are superior compared to existing image blur removal algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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