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3rd International Symposium on Intelligent Information Technology and Security Informatics

作者： Yao, Xiaoming Du, Wencai Huang, Mei Wu, Weihua Fu, Jundong Hainan Univ Coll Informat Sci & Technol Haikou Peoples R China East China Jiaotong Univ Sch Elect Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9780769540207

In this paper, we propose an original pattern-based blind spatial image watermarking scheme. The pattern is generated via a key-vaulted operation on the original watermark image block, and then modified with a secret position key before it is inserted into the designated position. The strength of its robustness depends on two factors: one is the scale factor when added with the host pixels, and the other is the number of patterns to be inserted. Part of the position key can be public for detection, while the other keys must be kept secret for ownership proof or resistance to removal attack. Theoretical analysis on tradeoff between the capacity and the robustness is also presented. Experimental results have shown that this scheme bear stronger robustness than traditional spread spectrum scheme under the same conditions, while simultaneously with extra security features.

关键词： image pattern spatial watermarking robustness blind detection

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Machine learning method using camera image patterns for predictions of particulate matter concentrations

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ATMOSPHERIC POLLUTION RESEARCH 2022年第3期13卷

作者： Suh, Kyung-Suk Min, Byung-Il Yang, Byung-Mo Kim, Sora Park, Kihyun Kim, Jiyoon Korea Atom Energy Res Inst Environm & Disaster Assessment Res Div Daedeok Daero 989-111 Daejeon 305353 South Korea

A machine learning technique using image patterns has been proposed to predict the concentrations of particulate matter with an aerodynamic diameter of <10 mu m (PM10) originating from on-road vehicles. The analysis process included measurements of PM10 concentrations, image data collection on vehicle transportation patterns and convolutional neural network (CNN). Preprocessing procedures involving high-pass filtering were used to remove the outliers from the measured concentrations of PM10, and multi-neuron components were modeled by connecting the estimated neuron functions. Finally, PM10 concentrations generated from vehicles were predicted through decisions from neural network modeling made in correspondence with the imagery data on estimated vehicles. Machine learning was performed by the classification of four types of images and the results showed good agreement with measurements for the cases of usage in the image patterns of vehicles. Machine learning applied to images of camera photography in this study was designed specifically for the prediction of PM10 concentrations. The findings showed that the proposed method can be used to estimate the real-time contributions of PM10 by vehicles near a road through connection with traffic information from a closed circuit television (CCTV).

关键词： Machine learning PM10 Vehicle transportation Camera photography image pattern

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New performance evaluation of parallel thinning algorithms based on PRAM and MPRAM models

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1998年第12期E81D卷 1494-1506页

作者： Rhee, PK La, CW Inha Univ Dept Comp Sci & Engn Nam Ku Incheon South Korea

The objective of thinning is to reduce the amount of information in image patterns to the minimum needed for recognition. Thinned image helps the extraction of important features such as end points. junction points, and connections from image patterns. The ultimate goal of parallel algorithms is to minimize the execution time while producing high quality thinned image. Though much research has been performed for parallel thinning algorithms, there has been no systematical approach for comparing the execution speed of parallel thinning algorithms. Several rough comparisons have been done in terms of iteration numbers. But, such comparisons may lead to wrong guides since the time required for iterations varies from one algorithm to the other algorithm. This paper proposes a formal method to analyze the performance of parallel thinning algorithms based on PRAM (Parallel Random Access Machine) model. Besides, the quality of skeletons, robustness to boundary noise sensitivity, and execution speed are considered. Six parallel algorithms, which shows relatively high performance, are selected, and analyzed based on the proposed analysis method. Experiments show that the proposed analysis method is sufficiently accurate to evaluate the performance of parallel thinning algorithms.

关键词： parallel thinning algorithm performance evaluation PRAM image pattern

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A computational algebraic geometry approach to analyze pseudo-random sequences based on Latin squares

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ADVANCES IN COMPUTATIONAL MATHEMATICS 2019年第4期45卷 1769-1792页

作者： Falcon, Raul M. Alvarez, Victor Gudiel, Felix Univ Seville Dept Appl Math 1 Seville Spain

Latin squares are used as scramblers on symmetric-key algorithms that generate pseudo-random sequences of the same length. The robustness and effectiveness of these algorithms are respectively based on the extremely large key space and the appropriate choice of the Latin square under consideration. It is also known the importance that isomorphism classes of Latin squares have to design an effective algorithm. In order to delve into this last aspect, we improve in this paper the efficiency of the known methods on computational algebraic geometry to enumerate and classify partial Latin squares. Particularly, we introduce the notion of affine algebraic set of a partial Latin square L = (l(ij)) of order n over a field K as the set of zeros of the binomial ideal < x(i)x(j) - x(lij) : (i, j) is a non-empty cell inL > subset of K[x(1), ... , x(n)]. Since isomorphic partial Latin squares give rise to isomorphic affine algebraic sets, every isomorphism invariant of the latter constitutes an isomorphism invariant of the former. In particular, we deal computationally with the problem of deciding whether two given partial Latin squares have either the same or isomorphic affine algebraic sets. To this end, we introduce a new pair of equivalence relations among partial Latin squares: being partial transpose and being partial isotopic.

关键词： Symmetric-key algorithm image pattern Partial Latin square Affine algebraic set Isomorphism

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Automatic image analysis for gene expression patterns of fly embryos

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BMC CELL BIOLOGY 2007年第s1期8卷 S7-S7页

作者： Peng, Hanchuan Long, Fuhui Zhou, Jie Leung, Garmay Eisen, Michael B. Myers, Eugene W. Howard Hughes Med Inst Ashburn VA 20147 USA No Illinois Univ Dept Comp Sci De Kalb IL 60115 USA Univ Calif Berkeley Dept Cell & Mol Biol Berkeley CA 94720 USA Univ Calif Berkeley Lawrence Berkeley Lab Genom Div Berkeley CA 94720 USA

Background: Staining the mRNA of a gene via in situ hybridization ( ISH) during the development of a D. melanogaster embryo delivers the detailed spatio-temporal pattern of expression of the gene. Many biological problems such as the detection of co-expressed genes, co-regulated genes, and transcription factor binding motifs rely heavily on the analyses of these image patterns. The increasing availability of ISH image data motivates the development of automated computational approaches to the analysis of gene expression patterns. Results: We have developed algorithms and associated software that extracts a feature representation of a gene expression pattern from an ISH image, that clusters genes sharing the same spatio-temporal pattern of expression, that suggests transcription factor binding (TFB) site motifs for genes that appear to be co-regulated (based on the clustering), and that automatically identifies the anatomical regions that express a gene given a training set of annotations. In fact, we developed three different feature representations, based on Gaussian Mixture Models (GMM), Principal Component Analysis (PCA), and wavelet functions, each having different merits with respect to the tasks above. For clustering image patterns, we developed a minimum spanning tree method (MSTCUT), and for proposing TFB sites we used standard motif finders on clustered/co-expressed genes with the added twist of requiring conservation across the genomes of 8 related fly species. Lastly, we trained a suite of binary-classifiers, one for each anatomical annotation term in a controlled vocabulary or ontology that operate on the wavelet feature representation. We report the results of applying these methods to the Berkeley Drosophila Genome Project (BDGP) gene expression database. Conclusion: Our automatic image analysis methods recapitulate known co-regulated genes and give correct developmental-stage classifications with 99+% accuracy, despite variations in morphology, orient

关键词： Gene Expression pattern Linear Discriminant Analysis Gaussian Mixture Model image pattern Gene Ontology Annotation

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A technique to identify some typical radio frequency interference using support vector machine 9

A technique to identify some typical radio frequency interfe...

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9th International Conference on Digital image Processing (ICDIP)

作者： Wang, Yuanchao Li, Mingtao Li, Dawei Zheng, Jianhua Chinese Acad Sci Natl Space Sci Ctr 1 Nanertiao Beijing 100190 Peoples R China Univ Chinese Acad Sci 19 A Yuquan Rd Beijing 10004 Peoples R China

ISBN: (纸本)9781510613058;9781510613041

In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%;while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

关键词： image pattern support vector machine principal component analysis mathematical morphology

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Deep Video Demoireing via Compact Invertible Dyadic Decomposition

Deep Video Demoireing via Compact Invertible Dyadic Decompos...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Quan, Yuhui Huang, Haoran He, Shengfeng Xu, Ruotao South China University of Technology School of Computer Science and Engineering Guangzhou 510006 China Pazhou Lab Guangzhou 510335 China Singapore Management University School of Computing and Information Systems 188065 Singapore

ISBN: (纸本)9798350307184

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.

关键词： Deep Video Computational Efficiency image Features Model Size Video Sequences image pattern Latent Features Compressed Format Complex Texture Multiscale Process Latent image Deep Neural Network Attention Mechanism Video Frames Peak Signal-to-noise Ratio Multiscale Analysis Feature Alignment Adjacent Frames Supplement For Details Discrete Cosine Transform Frame Alignment U-shaped Structure Alignment Blocks Multi-scale Structure Reversible Loss Color Shift Information Fidelity Latent Code Spatial Pyramid Pooling Spatial Pyramid

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Design of a composite video signal for DVD recorder measurement system based on virtual instrument technology

Design of a composite video signal for DVD recorder measurem...

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8th International Conference on Electronic Measurement and Instruments

作者： Wang Xiaotao Liu Hongbo Wang Jizhou Xu Zhixiang China Hualu Panason AVC Networks Co Ltd Dalian 116023 Peoples R China

ISBN: (纸本)1424411351

In this paper, a new source video signal is designed for online DVD evaluation and then its composite video signal waveform is produced through a video waveform generator board PXI-5431. The composite video field, no matter NTSC or PAL system it is, includes both visible image and invisible lines. The visible image has been set to different patterns, and the invisible lines include three lines such as Copy Once, Auto Clock Setting and Electronic Program Guide. Detailed procedures to produce both parts are well illustrated and the virtual instrument blocks in G-language are also presented.

关键词： DVD recorder composite video signal image pattern PXI-5431

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Modelling of interplant competition

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Annual Review in Automatic Programming 1985年第PART 2期12卷 213-215页

作者： Grabarnik, P.Ya Institute of Soil Science and Photosynthesis Pushchino 142292 Russian Federation

A pure plant community with a simple structure of the arrangement of individuals is considered. The stochastic model represented by a Markov random field (MRF), describes a productivity property of the plant community. The parameters of the model may be interpreted as a strength of the competition between plants. The problem of the interplant competition modelling is to choose the probability structure and parameters of the model and to simulate a Markov random field. The synthesized image patterns may be applied to a study of real data. © 1985.

关键词： image pattern Markov random field plant community simulation algorithm

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Stochastic texture recognition for image segmentation

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TM-TECHNISCHES MESSEN 2019年第7-8期86卷 384-398页

作者： Mueller, Thomas Erdnuess, Bastian Fraunhofer IOSB Inst Optron Syst Tech & Bildauswertung Fraunhoferstr 1 D-76131 Karlsruhe Germany KI Inst Photogrammetrie & Fernerkundung Englerstr 7 D-76131 Karlsruhe Germany

Aerial images often show regions that may offer high local color dynamics but nevertheless appear globally regular in a stochastical way so that humans easily recognize them as associated structures like a forest, a field, a pathway, or a waterbody. This work intends to algorithmically recognize and segment such regions. To do this, first, texture models are generated for different image regions, their structural similarities are assessed and, then, sufficiently different texture models are determined. Afterwards, each pixel in the image is assigned to one of those texture models with a structural metric and is then colored according to the assignment. Three different methods of texture modelling are examined and compared in view of the goal to combine as many regions as possible that are seen to be similar by a human observer (e. g., treetops, fields) and to insert segment margins where there are apparent transitions (e. g., boundary of a forest, vegetation changes).

关键词： Stochastic texture image pattern image segmentation pattern recognition land use

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