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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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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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Application of Fuzzy Neural Network Predictor to Mosaic image coding

Application of Fuzzy Neural Network Predictor to Mosaic Imag...

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4th International conference on Natural Computation (ICNC 2008)

作者： Cheng, Yongqiang Hu, Min Xie, Keming Taiyuan Univ Technol Coll Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9780769533049

In this paper,. we propose an effective sequential lossless compression scheme which performs sequentially pixel by pixel in raster-scan order, also so called one-pass scheme as opposed to multiple-pass scheme. the proposed method has two major components: fuzzy neural network predictor and context based adaptive arithmetic coder. the proposed predictor which is based on T-S fuzzy neural network system effectively exploits structural redundancies between mosaic-like color components, and its residuals are well-modeled by a two-side geometry distribution centered at zero. An advantage of the proposed compression method is the clean separation between prediction and entropy coding of prediction errors. Any entropy coder, be it Huffman or arithmetic, static or adaptive, binary or m-ary, can be easily interfaced with our predictor. Consequently, we can encode mosaic images at low complexity level. Compared with existing methods of lossless compression for Bayer raw data, the performance of proposed method is apparently the best.

关键词： Pixels

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Handwritten Digit Recognition Using encryption Methods 4

Handwritten Digit Recognition Using Encryption Methods

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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 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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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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Concatenated image compression and encryption

Concatenated Image Compression and Encryption

引用

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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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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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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Design and implementation of image compression core based on CCSDS algorithm

Design and implementation of image compression core based on...

引用

International conference on Computer Science & Education (ICCSE)

作者： Xiaodong Gu Huaichao Wang Xuequan Zhang Shijun Xu Chinese Academy of Sciences Center for Space Science and Applied Research Beijing China Department of Mathematics and Physics Xi'an Technological University Xi'an China

image compression is one of the major services in space flight mission and remote sensing system. this paper presents a high speed and high performance image compressor for future spacecrafts and micro-satellites. the proposed compression core is based on a modified CCSDS algorithm and the processing speed is improved 40 times using parallel architecture and pipeline technique. the experimental results indicate that this core has high performance at coding efficiency, data rate and error containment. A data rate of about 12.5 Mpixels/s can be sustained at 50 MHz.

关键词： Algorithm design and analysis image coding Discrete wavelet transforms Hardware Partitioning algorithms Decorrelation Space missions Space vehicles Pipelines Field programmable gate arrays

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Interpretation and Segmentation of Chart images Using h-Means image Clustering Algorithm 4th

Interpretation and Segmentation of Chart Images Using h-Mean...

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4th International conference on data Management, Analytics and Innovation, ICDMAI 2020

作者： Mishra, Prerna Kumar, Santosh Chaube, Mithilesh Kumar Department of Computer Science and Engineering DSPM IIITNR RaipurChhattisgarh India Department of Mathematics DSPM IIITNR RaipurChhattisgarh India

ISBN: (纸本)9789811556159

For chart analysis, it is necessary to segment regions of interest from the background for a better understanding of distinctive pieces of charts. Many different methods, such as thresholding, clustering, compression-based methods, etc., are proposed for image segmentation. For accurate representation, decoding of graphical data or visual contents is needed for a readable and understandable data pattern for a computing machine. the existing works for classification of charts have relied on local and handcrafted features that lack in dealing with a massive amount of visual data. that could include substantial variabilities of complex data from within-class and between-classes of data. therefore, we propose a segmentation method using histogram to segment the charts into a region of interest for feature extraction and classification. H-means algorithm uses histogram to decide the number of clusters by extracting peaks as ROIs for each chart image and segments foreground from the background. An image database of info charts having 5280 images covering ten categories of chart images is introduced to the research community. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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