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An effective iris recognition system for identification of humans 8

An effective iris recognition system for identification of h...

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8th International Multitopic conference, INMIC 2004

作者： Khan, Muhammad Khurram Zhang, Jiashu Horng, Shi-Jinn Sichuan Province Key Lab. of Signal and Information Processing Southwest Jiaotong University Chengdu610031 China Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)0780386809

In this paper, identification and verification approach based on human iris pattern is presented. The system is based on several steps from capturing the iris pattern, determining and localizing the iris boundaries, transformation of localized iris into rectangular and polar components, extracting the features from The image, based on wavelet transformation and at the end the matching of the iris. Matching is based on calculating the hamming codes of different irises. The algorithms used in this research are computationally effective, reliable, robust, and meet the industry standards in efficient and effective maimer. The accuracy of the presented work shows that our system is ideal for human recognition at high security places. © 2004 ieee.

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Structural analysis of paintings based on brush strokes

Structural analysis of paintings based on brush strokes

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conference on Scientific Detection of Fakery in Art

作者： Sablatnig, R Kammerer, P Zolda, E Vienna Univ Technol Pattern Recognit & Image Proc Grp A-1040 Vienna Austria

ISBN: (纸本)0819427551

The origin of works of art can often not be attributed to a certain artist. Likewise it is difficult to say whether paintings or drawings are originals or forgeries. In various fields of art new technical methods are used to examine the age, the state of preservation and the origin of the materials used. For the examination of paintings, radiological methods like X-ray and infra-red diagnosis, digital radiography, computer-tomography, etc. and color analyzes are employed to authenticate art. But all these methods do not relate certain characteristics in art work to a specific artist - the artist's personal style. In order to study this personal style of a painter, experts in art history and image processing.try to examine the "structural signature" based on brush strokes within paintings, in particular in portrait miniatures. A computer-aided classification and recognition system for portrait miniatures is developed, which enables a semi-automatic classification and forgery detection based on content, color, and brush strokes. A hierarchically structured classification scheme is introduced which separates the classification into three different levels of information: color, shape of region, and structure of brush strokes.

关键词： classification art history brush strokes feature detection face detection segmentation

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ANT colony optimization algorithm for feature selection and classification of multispectral remote sensing image

ANT colony optimization algorithm for feature selection and ...

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2008 ieee International Geoscience and Remote Sensing Symposium - proceedings

作者： Wen, Lintao Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424428083

In classification of multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if only consider image's spectral feature or texture feature alone. In this paper ,we presents a new approach by applying Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to multispectral remote sense image's classification. Using the combination of the spectral and texture features obtained by ACO in classification always get a better accuracy. ©2008 ieee.

关键词： Ant colony optimization

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Traffic classification based on visualization

Traffic classification based on visualization

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2011 ieee 2nd International conference on Networked Embedded Systems for Enterprise Applications, NESEA 2011

作者： Zhibin, Yu Choi, Yong-Do Kil, Gi-Beom Kim, Sung-Ho Electrical Engineering and Computer Science Kyungpook National University Daegu Korea Republic of Mobile Communication Engineering Kyungpook National University Daegu Korea Republic of School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Nowadays application based on encryption flows are increasing. Such applications are beneficial to protect the privacy but also offer convenience to hackers to avoid detection. This paper discusses the feasibility of applying a visualization technique to recognize traffic flows without reading payloads. We proposed a method to extract features from packet size and intervals and then change them to a 2-D image. Unlike most of machine learning methods which use features directly, we enhance the images with a modified mountain function to explore the potential of flow features. Finally principle component analysis is used to classify the traffic flows based on pattern recognition. The result shows that the images generated from flows can be recognized more easily after image enhancement. © 2011 ieee.

关键词： Visualization

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A Self-Supervised Purification Mechanism for Adversarial Samples 15

A Self-Supervised Purification Mechanism for Adversarial Sam...

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2022 ieee Congress on Cybermatics: 15th ieee International conferences on Internet of Things, iThings 2022, 18th ieee International conferences on Green Computing and Communications, GreenCom 2022, 2022 ieee International conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th ieee International conference on Smart Data, SmartData 2022

作者： Xie, Bingyi Xu, Honghui Xiong, Zuobin Li, Yingshu Cai, Zhipeng Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Deep learning based techniques are broadly used in a variety of applications such as image recognition, natural language processing. etc., which express leading performance than traditional methods. However, adversarial samples can cause severe problems for deep learning models, especially for vision-based deep neural networks. Adversarial training is a commonly adopted strategy to defend adversarial samples, but it lacks generalization capability due to the transferability of adversarial samples to work on cross-task predictions. In addition, the input processing.method of deep learning pipeline can improve generalization capability for cross-task predictions, whereas processing.newly generated adversarial samples is the major bottleneck that limits its performance. To deal with these issues, this paper proposes a purification mechanism that combines both adversarial training-based and input processing.based methods to defend the threats of adversarial samples. The proposed mechanism has a strong generalization capability for various vision-based deep learning tasks by leveraging the advantage of input processing.based methods and maintaining the defense efficiency of adversarial training-based methods. Comprehensive experiments are conducted on multiple datasets that demonstrate the superiority of the proposed approach against the state-of-the-art methods on defending adversarial samples. © 2022 ieee.

关键词： image recognition

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English Handwriting image Edge Detection Based on Fuzzy Logic 3

English Handwriting Image Edge Detection Based on Fuzzy Logi...

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3rd International conference on computer Vision and pattern Analysis, ICCPA 2023

作者： Wang, Xueying Liu, Hongwei Wang, Yuanshen School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9781510667563

This paper mainly applies fuzzy logic to the edge detection of handwritten images in English. image edge detection is an important part of image processing. Through image edge detection, the amount of data can be greatly reduced, and the information that can be considered irrelevant can be eliminated, and the important structural attributes of the image are retained. It uses six traditional operators to detect the edge of English handwritten images: canny, log, sobel, sprewitt, zerocross, and roberts. By comparing the edge detection results of the two kinds of experiments, we can find that the canny operator has the best edge detection effect among the traditional operators. In the fuzzy logic experiment, with the growth of Sx and Sy, the edge detection effect gradually weakened. In this paper, the experiment with Sx = 0. 05 and Sy = 0. 05 has the best edge detection results. In conclusion, the edge detection effect of English handwritten images based on fuzzy logic is better than that of English handwritten images based on traditional methods. © 2023 SPIE.

关键词： Fuzzy logic

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Adaptive distances on sets of vectors

Adaptive distances on sets of vectors

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10th ieee International conference on Data Mining, ICDM 2010

作者： Wóznica, Adam Kalousis, Alexandros University of Geneva Computer Science Department 7 Route de Drize Battelle Batiment A 1227 Carouge Switzerland

ISBN: (纸本)9780769542560

Recently, there has been a growing interest in learning distances directly from training data. While the previous works focused mainly on adapting distance measures over vectorial data, it is a well-known fact that many real-world data could not be easily represented as fixed length tuples of constants. In this paper we address this limitation and propose a novel class of distance learning techniques for learning problems in which instances are set of vectors;examples of such problems include, among others, automatic image annotation and graph classification. We investigate the behavior of the adaptive set distances on a number of artificial and real-world problems and demonstrate that they improve over the standard set distances. © 2010 ieee.

关键词： pattern recognition

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Beyond Principal Components: Deep Boltzmann Machines for face modeling

Beyond Principal Components: Deep Boltzmann Machines for fac...

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ieee conference on computer Vision and pattern recognition, CVPR 2015

作者： Duong, Chi Nhan Luu, Khoa Quach, Kha Gia Bui, Tien D. Concordia University Computer Science and Software Engineering MontréalQC Canada Carnegie Mellon University CyLab Biometrics Center PittsburghPA United States

ISBN: (纸本)9781467369640

The 'interpretation through synthesis', i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at 'explaining' face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial poses, occlusions, lighting, low resolution, etc. Since these variations are mostly non-linear, it is impossible to represent them in a linear model, such as Principal Component Analysis (PCA). This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferring a representation for new face images under various challenging conditions. In addition, DAMs have ability to generate a compact set of parameters in higher level representation that can be used for classification, e.g. face recognition and facial age estimation. The proposed approach is evaluated in facial image reconstruction, facial super-resolution on two databases, i.e. LFPW and Helen. It is also evaluated on FG-NET database for the problem of age estimation. © 2015 ieee.

关键词： Face recognition

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Hardware based artificial neural networks for basic pattern recognition application 3

Hardware based artificial neural networks for basic pattern ...

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3rd International conference on Informatics and Computing, ICIC 2018

作者： Sudiro, Sunny Arief Wardijono, Bheta Agus Arianto, Adi Putra Widianti, Linda Wahyu Wardhani, Ire Puspa STMIK Jakarta STI and K Jakarta Indonesia Computer Science Departement STMIK Jakarta STI and K Jakarta Indonesia Electrical Engineering Department Gunadarma University Jakarta Indonesia Information System Departement STMIK Jakarta STI and K Jakarta Indonesia

ISBN: (纸本)9781538669204

Nowadays, rapidly evolved image processing.technology has negative effect. Criminals often use image processing.technology to manipulate important data based on the image, such as bio data, signature, and stamp. Some anti-manipulate method is needed to reduce the further effect. A combination of computer vision and an artificial neural network can be used to build an application to be as anti-manipulate image method. This article proposed an approach by developing an application that can recognize the image pattern using K-Nearest Neighbor algorithm and calculate Euclidean distance between image features vector. Application use Sobel edge detection (3x3 matrix convolution) to obtain image features vector. Application use CogniStixTM as an artificial neural network hardware and CogniSoft SDK Express as a CogniStixTM driver. Application is released in three version, which is v1.0 with RoI size =24. Vl.1 with RoI size= image resolution, and v1.2 with RoI size =96. From the evaluation result, the best version is application version 1.1, which have accuracy rate of 100% (or approximately 100% in long term sample). All application version has a drawback, they not robust with deformation of input pattern. The application can be developed further to be integrated with FPGA board for more advanced image recognition in embedded system. © 2018 ieee.

关键词： Neural networks

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CP-mtML: Coupled projection multi-task metric learning for large scale face retrieval

CP-mtML: Coupled projection multi-task metric learning for l...

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2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Bhattarai, Binod Sharma, Gaurav Jurie, Frederic University of Caen France MPI for Informatics Germany IIT Kanpur India CSE Indian Institute of Technology Kanpur India

ISBN: (纸本)9781467388511

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.

关键词： image retrieval

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