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

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Research on Password algorithms based on Chaos and Algebraic Groups

Research on Password Algorithms based on Chaos and Algebraic...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE International conference on

作者： xi He Northfield Mount Hermon School Gill United States

As information technology advances rapidly, the demand for data security continues to grow. Chaos theory and algebraic groups have become a focal point in cryptographic research, providing a foundation for designing more robust block cipher algorithms. This article introduces a password algorithm based on chaos and algebraic groups, proposing the Lorenz chaos-geometric Goppa code composite password algorithm that has demonstrated significant achievements in image processing. In comparison to traditional methods, this algorithm exhibits the shortest key length and reduced error rates in image processing, offering a more reliable data protection solution for practical applications. This research opens up new possibilities for enhancing the security and performance in the field of cryptography.

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Generalized Single-image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer

Generalized Single-Image-Based Morphing Attack Detection Usi...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Haoyu Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Norwegian Univ Sci & Technol Trondheim Norway Darmstadt Univ Appl Sci Darmstadt Germany

ISBN: (纸本)9798350365474

Face morphing attacks have posed severe threats to Face Recognition systems (FRS), which are operated in border control and passport issuance use cases. Correspondingly, morphing attack detection algorithms (MAD) are needed to defend against such attacks. MAD approaches must be robust enough to handle unknown attacks in an open-set scenario where attacks can originate from various morphing generation algorithms, post-processing and the diversity of printers/scanners. The problem of generalization is further pronounced when the detection has to be made on a single suspected image. In this paper, we propose a generalized single-image-based MAD (S-MAD) algorithm by learning the encoding from Vision Transformer (ViT) architecture. Compared to CNN-based architectures, ViT model has the advantage on integrating local and global information and hence can be suitable to detect the morphing traces widely distributed among the face region. Extensive experiments are carried out on face morphing datasets generated using publicly available FRGC face datasets. Several state-of-the-art (SOTA) MAD algorithms, including representative ones that have been publicly evaluated, have been selected and benchmarked with our ViT-based approach. Obtained results demonstrate the improved detection performance of the proposed S-MAD method on inter-dataset testing (when different data is used for training and testing) and comparable performance on intra-dataset testing (when the same data is used for training and testing) experimental protocol.

关键词： Face Recognition Morphing Attack Detection

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Context-dependent tree-structured image classification using the QDA distortion measure and the hidden markov model

Context-dependent tree-structured image classification using...

引用

2004 International conference on image processing, ICIP 2004

作者： Ozonat, Kivanc M. Yoon, SangHo Information Systems Laboratory Department of Electrical Engineering Stanford University Stanford CA 94305 United States

ISBN: (纸本)0780385543

Vector quantization based on the Gauss mixture model (GMM) and the quadratic discriminant analysis (QDA) distortion measure has been shown to perform well in statistical image classification problems. Previous work in this area has concentrated on designing a separate GMM-based vector quantizer using the QDA distortion measure for each class using full search. We design a single vector quantizer for all classes using a tree-structured algorithm based on the (generalized) BFOS algorithm. This reduces the search complexity, while it increases the correct classification rate. Further, the pruning stage of our algorithm takes into account the dependencies between the image blocks assuming a hidden Markov model (HMM). During the test stage, our algorithm aims to iteratively maximize the joint probability of occurrence of all image blocks based on the HMM. Our simulation results indicate that our algorithm performs better (both in terms of computational complexity and classification rate) when compared to the previously published algorithms based on the GMM. © 2004 IEEE.

关键词： image analysis

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image denoising based on learning over-complete dictionary

Image denoising based on learning over-complete dictionary

引用

2012 9th International conference on Fuzzy systems and Knowledge Discovery, FSKD 2012

作者： Su, Kehua Fu, Hongbo Du, Bo Cheng, Hong Wang, Haofeng Zhang, Dengyi Computer School Wuhan University Hubei China

ISBN: (纸本)9781467300223

The sparse and redundant representations of signal theory have aroused extensive and deep research in recent years, and been widely applied to image processing. Aiming to study the performance and suitability of the sparse and redundant representations in image denoising, our paper introduces a sparse and redundant representations algorithm based on over-complete learned dictionary to process different types of images. We use the K-SVD denoising framework and modify its initial dictionary, and then mainly focus on using it to study its denoising performance and suitability for different types of images, and then compare it with some other image denoising algorithms. As to the remote sensing images denoising, the experiment results show that the K-SVD algorithm can leads to the state-of-art denoising performance at low noisy levels, but for high noisy levels, its performance isn't good on PSNR and visual effect, that is it cannot retain the local details of images. © 2012 IEEE.

关键词： image denoising

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Object recognition algorithms implemented on NAO robot for children's visual learning enhancement 2018

Object recognition algorithms implemented on NAO robot for c...

引用

2nd International conference on Mechatronics systems and Control Engineering (ICMSCE)

作者： Karayaneva, Yordanka Hintea, Diana Coventry Univ Gulson Rd Coventry W Midlands England Coventry Univ Gulson Rd Coventry W Midlands England

ISBN: (纸本)9781450363792

Schools in many parts of the world use robots as social peers in order to interact with children and young students for a rich experience. Such use has shown significant enhancement of children's learning. This project uses the humanoid robot NAO which provides object recognition of colours, shapes, typed words, and handwritten digits and operators. The recognition of typed words provides performance of the corresponding movements in the sign language. Five classifiers including neural networks are used for the handwritten recognition of digits and operators. The accuracy of the object recognition algorithms are within the range of 82%-91% when tested on images captured by the robot including the movements which represent words in the sign language. The five classifiers for handwritten recognition produce highly accurate results which are within the range of 87%-98%. This project will serve as a promising provision for an affective touch for children and young students.

关键词： object recognition image processing machine learning robotics visual learning

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Comparative Study of TRANS - GAN Architecture for Bio-Medical image Semantic Segmentation 7

Comparative Study of TRANS - GAN Architecture for Bio-Medica...

引用

7th International conference on Communication and Electronics systems, ICCES 2022

作者： Ashok, Malvika Gupta, Abhishek Shri Mata Vaishno Devi University School of Computer Science and Engineering Jammu and Kashmir Katra India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

In the area of biomedical image processing, medical image segmentation plays a crucial role. Today due to the deep sculptures of deep neural networks and innovative by-passes like the Transformers this field has rejuvenated. This paper analyzes various algorithmic advancements regarding Transformers and Generative Adversarial Networks (GAN) which have paved the anatomy of image segmentation. The GAN network is not only able to regenerate feature spaces but also can transpose the inputs from the transformer. These algorithms are not only at par with the bifurcation of networks like U-net and ResNet but showcase a strong and more intuitive idea of human neural intelligence. Finally, all methods are compared based on accuracy, dice score, architectural evaluation, etc. The ability of these networks are deduced to regenerate and recreate the feature spaces for the given input image and thus can segment high features for prediction. © 2022 IEEE.

关键词： Generative adversarial networks

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INTEGRATING REGION GROWING AND EDGE-DETECTION USING REGULARIZATION

INTEGRATING REGION GROWING AND EDGE-DETECTION USING REGULARI...

引用

conference on image processing - Medical Imaging 1995

作者： CHALANA, V COSTA, W KIM, YM UNIV WASHINGTON CTR BIOENGN SEATTLE WA 98195 USA

ISBN: (纸本)0819417823

We propose a segmentation approach which integrates region growing and edge detection in a regularization framework. Our method is a modified active contour model and uses region statistics in addition to gradient information. We formulate the active contour model using a Bayesian approach. We have implemented this integrated approach and characterized its performance on synthetic images and on 36 short-axis cardiac ultrasound images. The resulting boundaries are compared to true boundaries in the case of the synthetic images and to manually outlined boundaries in the case of ultrasound images. The results are also compared with those obtained using the balloon force to expand the active contour model. We found that our integrated algorithm detects boundaries more accurately than the active contour method using a balloon force. Furthermore, the integrated algorithm is less sensitive to the placement of the initial contour inside the LV cavity than the active contour algorithm using a balloon force.

关键词： image segmentation Data modeling Sensors Edge detection Statistical modeling Echocardiography image processing image processing algorithms and systems Ultrasonography Convolution

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Iris recognition using minimum average correlation energy and principal component 4

Iris recognition using minimum average correlation energy an...

引用

4th IEEE Global conference on Consumer Electronics, GCCE 2015

作者： Klehm, Volnei Da S. Melo, Wheidima C. Farias, Felipe S. Santos, Kenny V. Waldir, S.S. Samsung Institute for Dev. Informatics Manaus AM Brazil State University of Amazonas Manaus AM Brazil Federal University of Amazonas Manaus AM Brazil

ISBN: (纸本)9781479987511

Biometric systems have great importance in modern society. Today, we can find biometric systems using hardware technologies to provide an increasingly fast response. Partly this is possible due to the use of algorithms of digital signal processing which are used in these systems, such as the correlation filters. In this paper, we explore a change in correlation filters, using the principal component analysis, to provide robustness to the filters. Finally, the experiments demonstrate that the modification provides good results for a biometric recognition system human iris. © 2015 IEEE.

关键词： Digital signal processing

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Accuracy Enhancement of a Blind image Steganalysis Approach Using Dynamic Learning Rate-Based CNN on GPUs 10

Accuracy Enhancement of a Blind Image Steganalysis Approach ...

引用

10th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Mustafa, Esiam M. Eishafey, Mohamed A. Fouad, Mohamed M. Mil Tech Coll Dept Comp Engn Cairo Egypt

ISBN: (纸本)9781728140698

Blind image steganalysis is the classification problem of determining whether an image contains any hidden data or not. This blind process doesn't need any prior information about the embedding algorithm which is used to hide data on the examined images. Recently, Convolutional Neural Network (CNN) is presented to deal with the blind image steganalysis classification problem. Most of the CNN-based image steganalysis approaches can't cope with low payloads. Improved Gaussian Convolutional Neural Network (IGNCNN) is presented with a transfer learning method in order to deal with stego-images with low payloads. IGNCNN contains a pre-processing layer which is consisted of a fixed coefficients (data-set independent) high pass filter (HPF). IGNCNN also is a fixed learning rate based-CNN. In this paper, a dynamic learning rate-based CNN approach is proposed, in order to highly minimize the detection error cost. Nevertheless, the proposed approach uses a dataset dependent-based Gaussian HPF instead, as a preprocessing layer, in order to well-choose a cutoff frequency depending on the training dataset. Experiments are performed on graphical processing units (GPUs) with the standard BOSSbase 1.01 dataset exposed to the S-UNIWARD and WOW image steganographic algorithms. Results show that the proposed approach outperforms computing approaches, GNCNN, improved GNCNN, SRM and SRM+EC, by an average increase of 7.4%, 5.3%, 4.1% and 2.8% respectively in terms of accuracy metric.

关键词： image steganalysis convolutional neural network deep learning transfer learning gaussian high pass filter dynamic learning rate

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A preliminary study of intensity-weighted ROI imaging in cone-beam CT

A preliminary study of intensity-weighted ROI imaging in con...

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Medical Imaging 2008 conference

作者： Cho, Seungryong Pearson, Erik xia, Dan Han, xiao Pelizzari, Charles A. Pan, xiaochuan Univ Chicago Dept Radiol Chicago IL 60637 USA Univ Chicago Dept Radiat & Cellular Oncol Chicago IL 60637 USA

ISBN: (纸本)9780819470973

Some of the recently developed image reconstruction algorithms for cone-beam computed tomography (CBCT) involve the. computation of the finite Hilbert transform. We have previously studied noise property of the finite Hilbert transform and observed that it can be used for potentially improving the image noise property within a region of interest (ROI) in IGRT. Imaging radiation dose is one of the critical issues in IGRT, and in addition to existing dose-reduction schemes by use of ROI imaging, it is possible to achieve further patient dose reduction through modulating beam intensity so-that a sub-ROI in the ROI be exposed by high flux of x-ray photons and the rest of the ROI be exposed by low flux of them. In this work, we investigate the technique for obtaining sub-ROI images, which is supposed to include the target under treatment, with high contrast-to-noise ratio (CNR) and the images within the rest of the ROI with low CNR. Numerical studies have been conducted as a preliminary in this work.

关键词： image segmentation Reconstruction algorithms image restoration Sensors Tissues Algorithm development Detection and tracking algorithms image processing algorithms and systems X-ray computed tomography Computed tomography

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