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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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An Analysis of Haar Wavelet Transformation for Androgenic Hair pattern recognition 1

An Analysis of Haar Wavelet Transformation for Androgenic Ha...

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1st international conference on Informatics and Computing (ICIC)

作者： Lionnie, Regina Alaydrus, Mudrik Univ Mercu Buana Jakarta Dept Elect Engn West Jakarta Indonesia

ISBN: (纸本)9781509016488

identify criminal or pedophile in online child pornography images and video is a challenging task when the faces and other distinguish features are not shown. To address these kind of problems, the system of recognition using androgenic hair pattern is being developed. The system of recognition presented in this paper used three main parts of methods, pre-processing methods, feature extraction with Haar Wavelet Transform level 1 decomposition and classification using Nearest Neighbor. Using 400 images of lower right legs with controlled condition, the system was analyzed. The Haar Wavelet Transformation for level 1 decomposition gave 83.48 % of average recognition precision when using 10-fold cross validation with nearest neighbor classification.

关键词： biometric identification pattern recognition Haar wavelet transform andorgenic hair image processing

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Automatic Attendance System by Visual Programming Language LabVIEW 1

Automatic Attendance System by Visual Programming Language L...

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1st IEEE international conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Kumar, Amit Verma, P. K. Perala, Srinivas Chadha, P. R. Delhi Technol Univ Dept Elect & Commun Engn New Delhi India Digil Syst Pvt Ltd Hyderabad Andhra Prades India

ISBN: (纸本)9781467385879

This paper presents an application of LabVIEW and camera interface which is used for taking attendance in classroom. The system use a camera which is connected to PC on USB interface and attached to each and every classroom for taking snap of students presented. LabVIEW graphical programming environment is a tool for realizing the image acquisition and processing. This software has several advantages: simple implementation, modularity, flexibility, attractive user interface and possibility to develop very easy new features. Face recognition and machine vision applications in automatic inspection of various electronic modules can also be implemented using given algorithm.

关键词： pattern recognition image processing image Acquisition Virtual Instuments(vi) LabVIEW Digital image

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image Pre-processing Detection: Evaluation of Benford's Law, Spatial and Frequency Domain Feature Performance 1

Image Pre-Processing Detection: Evaluation of Benford's Law,...

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1st international Workshop on Sensing, processing and learning for Intelligent machines (SPLINE)

作者： Neubert, Tom Hildebrandt, Mario Dittmann, Jana Otto von Guericke Univ Dept Comp Sci Univ Pl 2 D-39106 Magdeburg Germany Otto von Guericke Univ Dept Comp Sci Res Grp Multimedia & Secur POB 4120 D-39016 Magdeburg Germany

ISBN: (纸本)9781467389174

This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).

关键词： Frequency domain analysis

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Analysis of Spoofing Detection using Video Subsection processing 16

Analysis of Spoofing Detection using Video Subsection Proces...

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1st international conference on Informatics and Analytics (ICIA)

作者： Arathy, P. J. Nair, Vrinda V. Coll Engn Dept Elect & Commun Trivandrum Kerala India

ISBN: (纸本)9781450347563

Imposters gain unauthorized access to biometric recognition systems using fake biometric data of the legitimate user termed as spoofing. Spoofing of face recognition systems is done by photographs, 3D models and videos of the user. Attack video contains noise from the acquisition process. In this work, we use noise residual content of the video in order to detect spoofed videos. We take advantage of wavelet transform for representing the noise video. Samples of the noise video, termed as visual rhythm image is created for each video. Local Binary pattern (LBP) and uniform Local Binary pattern (LBPu2) are extracted from the visual rhythm image followed by classification using Support Vector machine (SVM). Large size of video from which a number of frames are used for analysis results in huge execution timing. In this work the spoof detection algorithm is applied on various levels of subsections of the video frames resulting in reduced execution timing with reasonable detection accuracies.

关键词： Face anti-spoofing visual rhythm liveness detection wavelet transform

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Score Thresholding for Accurate Instance Classification in Multiple Instance learning 6

Score Thresholding for Accurate Instance Classification in M...

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6th international conference on image processing Theory, Tools and Applications (IPTA)

作者： Carbonneau, Marc-Andre Granger, Eric Gagnon, Ghyslain Univ Quebec Ecole Technol Super Montreal PQ Canada

ISBN: (纸本)9781467389105

Multiple instance learning (MIL) is a form of weakly supervised learning for problems in which training instances are arranged into bags, and a label is provided for whole bags but not for individual instances. Most proposed MIL algorithms focus on bag classification, but more recently, the classification of individual instances has attracted the attention of the pattern recognition community. While these two tasks are similar, there are important differences in the consequences of instance misclassification. In this paper, the scoring function learned by MIL classifiers for the bag classification task is exploited for instance classification by adjusting the decision threshold. A new criterion for the threshold adjustment is proposed and validated using 7 reference MIL algorithms on 3 real-world data sets from different application domains. Experiments show considerable improvements in accuracy over these algorithms for instance classification. In some applications, the unweighted average recall increases by as much as 18%, while the F-1-score increases by 12%.

关键词： Multiple instance learning Weakly-supervised learning Instance classification set classification

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Emotion recognition Using Sigma-Pi Neural Network 1

Emotion Recognition Using Sigma-Pi Neural Network

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1st IEEE international conference on Data stream Mining and processing (DSMP)

作者： Kulishova, Nonna Kharkiv Natl Univ Radio Elect 14 Nauky Ave UA-61166 Kharkov Ukraine

ISBN: (纸本)9781509037360

The problem of emotion state recognition using the sigma-pi artificial neural network is considered. The specific feature of the considered network is the presence of two different types of activation functions: sigmoid and bell-shaped. A learning algorithm for the sigma-pi network is proposed. This algorithm is characterized by high approximation accuracy especially for nonlinear processes in real time. Training and testing network on image dataset was made.

关键词： emotion recognition neural network supervised learning

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Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis 1

Tongue's substance and coating recognition analysis using HS...

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1st international Workshop on pattern recognition (IWPR)

作者： Kamarudin, Nur Diyana Ooi, Chia Yee Kawanabe, Tadaaki Mi, Xiaoyu Malaysia Japan Int Inst Technol Embedded Syst Lab Kuala Lumpur Malaysia Kitasato Univ Oriental Med Res Ctr Tokyo Japan Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

In ISO TC249 conference, tongue diagnosis has been one of the most active research and their objectifications has become significant with the help of numerous statistical and machine learning algorithm. Color information of substance or tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. In order to produce high reproducibility of color measurement analysis, tongue images have to undergo several procedures such as color correction, segmentation and tongue's substance-coating separation. This paper presents a novel method to recognize substance and coating from tongue images and eliminate the tongue coating for accurate substance color measurement for diagnosis. By utilizing Hue, Saturation, Value (HSV) color space, new color-brightness threshold parameters have been devised to improve the efficiency of tongue's substance and coating separation procedures and eliminate shadows. The algorithm offers fast processing time around 0.98 seconds for 60,000 pixels tongue image. The successful tongue's substance and coating separation rate reported is 90% compared to the labelled data verified by the practitioners. Using 300 tongue images, the substance Lab color measurement with small standard deviation had revealed the effectiveness of this proposed method in computerized tongue diagnosis system.

关键词： Computerized tongue image analysis device substance and coating separation coating recognition substance color analysis HSV color threshold tongue diagnosis integrative medicine

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Comparison of standard Edge Detection Techniques Along with Morphological processing and Pseudo Coloring in Sonar image 1

Comparison of Standard Edge Detection Techniques Along with ...

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1st international conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Anitha, U. Malarkkan, S. Premalatha, J. Manonmani, V. Sathyabama Univ Fac Elect Dept Elect & Control Madras Tamil Nadu India Manakula Vinayagar Inst Technol Pondicherry India

ISBN: (纸本)9781467367257

Object recognition and texture identification are the two main application of sonar image processing. It consists of 3 main steps such as image segmentation, object recognition and pattern identification. Before all these steps, some of the preprocessing procedure are included such as filtering of noise, edge detection etc. Many edge detection operators are available and also a mathematical modeling called morphological processing is also very useful for edge detection. Color images generally have more visual perception over gray scale and monochrome images. Therefore in this paper the standard edge detection techniques along with the morphological processing are carried out. Comparison of the result along with the pseudo colored image proves that the morphological processing along with edge detection techniques gives more information than another one. Subjective analysis tells that, this approach mainly useful for the sonar image objects consist of larger shadow regions.

关键词： sonar image edge detection morphological processing pseudo coloring

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1st international conference on Smart Trends in Information Technology and Computer Communications, SmartCom 2016

1st International Conference on Smart Trends in Information ...

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1st international conference on Smart Trends in Information Technology and Computer Communications, SmartCom 2016

ISBN: (纸本)9789811034329

The proceedings contain 108 papers. The special focus in this conference is on Smart Trends in Information Technology and Computer Communications. The topics include: Real time sign language processing system;nano scale dual material gate silicon on nothing junctionless MOSFET for improving short channel effect and analog performance;an improved image compression technique using Huffman coding and FFT;comparison and analysis of cuckoo search and firefly algorithm for image enhancement;an exploration of miscellaneous palm print recognition modalities;plugin for instantaneous web page rejuvenation and translation;issues and requirements for successful integration of semantic knowledge in web usage mining for effective personalization;image fusion based on the modified curvelet transform;a case study of Indian hospitality and tourism sector;a comparative study of various algorithms in wireless networking;research and analysis of open security issues in communication for wireless sensor network;empirical analysis of image segmentation techniques;sentiment analysis at document level;an approach to sentiment analysis on unstructured data in big data environment;lung cancer diagnosis by hybrid support vector machine;multi chromatic balls with relaxed criterion to detect larger communities in social networks;image segmentation and object recognition using machine learning;proposed algorithms to the state explosion problem;predicting software maintainability using object oriented dynamic complexity measures;security enhancement of blowfish block cipher and comparative analytical study for news text classification techniques applied for stock market price extrapolation.

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Remote QR Code recognition based on HOG and SVM Classifiers 1

Remote QR Code Recognition based on HOG and SVM Classifiers

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1st international conference on Informatics and Computing (ICIC)

作者： Tribak, Hicham Moughyt, Salah Zaz, Youssef Schaefer, Gerald Abdelmalek Essaadi Univ Fac Sci Tetouan Morocco Loughborough Univ Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781509016488

QR codes have become useful and efficient data storage tools which are exploited in many commercial applications including product tracking, website redirection, etc. A QR code is a 2-dimensional barcode localised through three finder patterns (three squares characterised by a series of alternative black and white modules at ratios 1: 1: 3: 1: 1) placed in its three corners. QR codes are generally placed in different environments with complex backgrounds (overlapping text, pictures, etc.), and are often captured under unfavourable conditions such as poor lighting. These factors can significantly affect the recognition ability and thus may hinder correct QR code localisation and identification. In order to appropriately address these issues, in this paper, we present a QR code recognition algorithm based on histogram of oriented gradients (HOG) features combined with support vector machine (SVM) classifiers. Using HOG, we extract gradient features of each extracted pattern. Subsequently, the obtained features are passed to two linear SVM classifiers, one trained with finder patterns and one trained with alignment patterns, to remove irrelevant patterns. QR codes are then conveniently localised according to a pattern closeness constraint. In the last stage, the captured code is enhanced by applying a perspective correction followed by image binarisation and morphological processing. Finally, the patterns are decoded using an accurate 2-d barcode decoder. Our proposed approach is designed for an embedded systems using a Raspberry Pi equipped with a HD camera and a small robot carrying the equipment.

关键词： QR codes pattern recognition histogram of oriented gradients support vector machine

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