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检索条件"任意字段=4th Conference on Mathematics of Image and Data Coding, Compression, and Encryption"

共 249 条记录，以下是161-170 订阅

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Handwritten Digit Recognition Using encryption Methods 4

Handwritten Digit Recognition Using Encryption Methods

引用

4th International conference on Pattern Analysis and Intelligent Systems, PAIS 2022

作者： Abdelmoumene, Zebdi Lakhdar, Laimeche Abdeldjamil, Gattal Larbi Tebessi Univesity Mathematics and Computer Science Tebessa12002 Algeria Tebessi Tebessa12002 Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Over the past years, deep learning techniques has had a strong impact on many areas of technical intelligence, including handwriting recognition. Handwriting recognition it is defined as the domain which allow a computer to interpret intelligible handwritten inputs from different sources. It is plays an important role in many areas, such as authenticating bank checking, exchanging remote computer files, and document duration. In this paper, we present a novel handwritten digit recognition method based on deep learning. In order to improve the performance of recognizing handwritten digits, we have proposed a new data augmentation method by using different encrypted digit images and image fusion techniques. the experimental results based on the CVL database shown that the proposed method achieved higher accuracy rates that were compared to the state of the art. © 2022 IEEE.

关键词： Cryptography

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A lossless compression method for halftone images using evolvable hardware 4th

A lossless compression method for halftone images using evol...

引用

4th International conference on Evolvable Systems: From Biology to Hardware, ICES 2001

作者： Sakanashi, Hidenori Iwata, Masaya Higuchi, Tetsuya AIST Tsukuba Central 2 1-1-1 Umezono TsukubaIbaraki305-8568 Japan

ISBN: (纸本)354042671X

this paper proposes a lossless (reversible) data compression method for bi-level images, particularly printing images. In this method, called Dispersed Reference compression (DRC), the coding scheme is changed according to the characteristics of the images to be compressed by Evolvable Hardware. Computational simulations demonstrate that DRC provides compression ratios that are up to 30% better than the current international standard for bi-level image compression. this paper also reports on the progress in discussions to incorporate a DRC-based compression method as an improvement to the international standard. © Springer-Verlag Berlin Heidelberg 2001.

关键词： image compression

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Orientation scanning to improve lossless compression of fingerprint images

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4th International conference on Audio- and Video-Based Biometric Person Authentication

作者： thärnå, J Nilsson, K Bigun, J Halmstad Univ Sch Informat Sci Comp & Elect Engn IDE S-30118 Halmstad Sweden

ISBN: (纸本)3540403027

While standard compression methods available include complex source encoding schemes, the scanning of the image is often performed by a horizontal (row-by-row) or vertical scanning. In this work a new scanning method, called ridge scanning, for lossless compression of fingerprint images is presented. By using ridge scanning our goal is to increase the redundancy in data and thereby increase the compression rate. By using orientations, estimated from the linear symmetry property of local neighbourhoods in the fingerprint, a scanning algorithm which follows the ridges and valleys is developed. the properties of linear symmetry axe also used for a segmentation of the fingerprint into two parts, one part which lacks orientation and one that has it. We demonstrate that ridge scanning increases the compression ratio for Lempel-Ziv coding as well as recursive Huffman coding with approximately 3% in average. Compared to JPEG-LS, using ridge scanning and recursive Huffman the gain is 10% in average.

关键词： Scanning

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PCR-tree: a compression-based index structure for similarity searching in high-dimensional image databases

PCR-tree: a compression-based index structure for similarity...

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4th International conference on Fuzzy Systems and Knowledge Discovery

作者： Cui, Jiangtao Zhou, Shuisheng Zhao, Shan Xidian Univ Dept Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China

ISBN: (纸本)0769528740

the vector approximation file (VA-file) approach is an efficient high-dimensional indexing method using compression technique to overcome the difficulty of 'curse of dimensionality'. the VA-file method combined with tree-based index structure can improve the querying efficiency, but it still succumbs to the 'curse of dimensionality'. In this paper, a new high-dimensional indexing structure called PCR-tree for non-uniform distributed data sets was presented, which employs R-tree to manage the approximate vectors in the reduced-dimensionality space. the approximate vectors can be built in the K-L transform domain, and low dimensional MBRs (minimum bounding rectangles) can be used to manage the approximations on the first few principal components. When performing k -nearest neighbor search, a lower-bound filtering algorithm is used to reject the improper nodes of PCR-tree, which can reduce the computational complexity and I/O cost without any dismissals. the experiment results on large image databases show that the new approach provides a faster search speed than other tree-structured vector approximation approaches.

关键词： image coding Indexes image databases Indexing Karhunen-Loeve transforms Computer science Filtering algorithms Computational complexity Costs Acceleration

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A fast and accurate moving object extraction scheme in the MPEG compressed domain

A fast and accurate moving object extraction scheme in the M...

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4th International conference on image and Graphics

作者： Zhou Qiya Yang Gaobo Chen Weiwei Zhang Zhaoyang Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Shanghai Univ Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China

ISBN: (纸本)9780769529295

the hot topic of moving object extraction has been transferred from the pixel domain to directly from the compressed stream. the existed work of compressed-domain moving object extraction in the literature usually can meet the requirement of real-time performance. However, the accuracy of extracted objects is too coarse. the reason is that the information utilized in the compressed-domain extraction process is not abundant and noisy. In order to solve this problem, a novel moving object extraction scheme with high accuracy for MPEG stream is proposed. Firstly, the contour-feature and motion vector based projection are fused to extract the coarse object in an efficient way. Second, the blocks in I frame are partially decoded to improve the segmentation accuracy. the innovation lies in a threshold method of image segmentation with automatic seeding is applied on the DC+2AC image of I frame to extract the contour-feature, which is combined with the projection of motion vector field of P frame to complete the coarse segmentation. then tradeoff is made between the executive time and results' accuracy. High accuracy is acquired at the cost of partially decoding time and region-growing processing time, which is worthwhile at the expense of such a little more time to refine the result. Experiments reveal that the proposed algorithm has the attributes of both real-time and high accuracy at the same time.

关键词： Transform coding data mining Pixel image segmentation Video compression image coding Decoding Computer displays Streaming media Costs

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An End-to-End Mutual Enhancement Network Toward image compression and Semantic Segmentation 4th

An End-to-End Mutual Enhancement Network Toward Image Compre...

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4th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Chen, Junru Yao, Chao Liu, Meiqin Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783030880071;9783030880064

image compression is to compress image data without compromising human vision feeling. However, the information loss through the image compression process may influence the following machine vision tasks, such as object detection and semantic segmentation. How to jointly consider the human vision and the machine vision to compress images for human and machine vision tasks is still an open problem. In this paper, we provide a multi-task framework for image compression and semantic segmentation. More specifically, an end-to-end mutual enhancement network is designed to efficiently compress the given image, and simultaneously segment the semantic information. Firstly, a uniform feature learning strategy is adopted to jointly learn the features for image compression and semantic segmentation in the encoder. Moreover, a multi-scale aggregation module in the encoder is employed to enhance the semantic features. then, by transmitting the quantified features, both the decompressed image features and the learned semantic features can be reconstructed. Finally, we decode this information for the image compression task and the semantic segmentation task. On one hand, we can utilize the decompressed semantic features to implement semantic segmentation in the decoder. On the other hand, the quality of the decompressed image can be further improved depending on the obtained semantic segmentation map. Experimental results prove that our framework is effective to simultaneously support image compression and semantic segmentation, both in the subjective and objective evaluation.

关键词： Learning-based compression Video coding for Machine Semantic segmentation

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Concatenated image compression and encryption

Concatenated Image Compression and Encryption

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Abdulaziz Alshaya Saleh Komies National centre for Electronic and photonics King Abdulaziz City for Science and Technology (KACST) Riyadh Saudi Arabia Electrical and Electronic Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781665437745

In recent years, there have been increasing demands for applications of image storage, sharing and transmission across data networks. For these data-intensive and sensitive applications, it is imperative to perform both image compression and encryption. In this paper, two different approaches of image compression and encryption were studied and compared. In the first approach, image compression was followed by encryption; while, in the second approach, image encryption was followed by compression. Both approaches were implemented based on standard schemes, i.e., JPEG 2000 lossless compression and DES encryption. Experiments were conducted for two different types of testing images: a natural image and a medical image. the efficiency performance of the two approaches was compared based on key parameters such as MSE, PSNR, entropy, image file size, compression ratio and time consumption. We found that the first approach outperformed the second for both testing images. the results are presented and analysed along with recommendations for related future work.

关键词： image coding Transform coding Receivers Entropy Communications technology encryption image storage

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the Analysis and Improvement of Huffman Algorithm

The Analysis and Improvement of Huffman Algorithm

引用

2011 4th IEEE International conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Ting HE Jian LI Yingjia LI Yang LI College of Computer Science and Technology Beijing University of Technology

ISBN: (纸本)9781612848334

As the most common long-lossless compression algorithm, Huffman coding plays an important role in the data compression field. Huffman coding, for the code elements of uneven distribution, with a higher compression ratio, But for the even distribution, the compression rate is lower. In this paper, according to cryptography encryption and decryption methods, a new strategy is referred to alter the distribution of its code elements to uneven distribution and improve the Huffman tree method, in order to raise the compression ratio.

关键词： Huffman coding data compression binary tree

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Modelling the visual cortex using artificial neural networks for visual image reconstruction

Modelling the visual cortex using artificial neural networks...

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Proceedings of the 4th International conference on Artificial Neural Networks

作者： Qiu, G. Univ of Derby United Kingdom

In this paper, we present an artificial neural network model with some special neurons that are designed to function as the feature detectors found in the visual cortex and apply it to the reconstruction of visual images. the model is a multilayer feedforward neural network. the neurons in the first hidden layer of the network are feature detectors of various scales and orientations. the connection strengths between the input and the first hidden layer are pre-set (and fixed) such that the outputs of this layer are some visually important features of various scales. the rest of the connection strengths in the network are decided through learning via the backpropagation algorithm in a self-supervising manner. Computer simulations were conducted, and the results seem to show that the artificial neural network model proposed in this paper is consistent with its biological counterpart (the visual cortex) in terms of the visual features detected by its feature detectors and its ability to reconstruct near perfect input images from the output of these feature detectors. It is also shown that the system can be used for effective image data compression. Simulation results are presented which show promising potential of the new system for image coding applications.

关键词： Feedforward neural networks

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DC Coefficients Recovery from AC Coefficients in the JPEG compression Scenario 4th

DC Coefficients Recovery from AC Coefficients in the JPEG Co...

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4th International conference on Smart Computing and Communications, SmartCom 2019

作者： Qiu, Han Zheng, Qinkai Qiu, Meikang Memmi, Gerard LTCI Telecom Paris Paris75013 France Department of Computer Science Texas A&M University-Commerce CommerceTX United States

ISBN: (纸本)9783030341381

With the deployment of multimedia compression techniques, contents such as images or videos are transmitted through resource-constrained networks such as Internet of things (IoT) scenarios. Traditional multimedia compression methods based on spatial-frequency transforms and coding techniques have been progressively improving and approaching the theoretical limit of Shannon. therefore some new approaches are proposing to transmit only a part of the compressed data and recover the missing part at the receiver’s end. In this paper, we propose to follow this idea to highly enhance the JPEG compression by transmitting all AC coefficients and only four DC coefficients of one image. On the receiver’s end, we propose two methods to rebuild the missing DC coefficients based on the remaining DCT coefficient relationships. the first method considers the pixel relationship compared with the adjacent blocks on multiple directions. the second method considers not only the pixel relationship between adjacent blocks but also the existing relationship inside the adjacent blocks. As a result, our proposed method recovers the transmitted JPEG image with more than 25 dB considering PSNR while transmitting only 40–60% of the size of the JPEG images. © 2019, Springer Nature Switzerland AG.

关键词： image compression

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