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Fourier Transform of Untransformable Signals Using pattern recognition Technique

Fourier Transform of Untransformable Signals Using Pattern R...

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international conference on Advances in computing, Control, & Telecommunication Technologies (ACT)

作者： Varun Gupta Gavendra Singh Manish Mittal Sharvan Kumar Pahuja Department of Instrumentation & Control Engineering National Institute of Technology Jalandhar Jalandhar India Department of Electronics & Communication Engineering National Institute of Technology Jalandhar Jalandhar India

In this paper we are highlighting the signals that are not Fourier transformable and give its Fourier transform using PCA (Principle Component Analysis), lDA (linear Discriminant Analysis). Such signals are step signal, signum, etc. Basically Fourier transform transforms time domain signal into frequency domain and after transformation describes what frequencies original signal have. Principle Component Analysis is a way of identifying patterns (recognition) in the data and the differences of the data is highlighted. With the help of PCA & lDA we do the dimension reduction of the signal. lDA is used in statistics and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The resulting combination may be used as a linear classifier or, more commonly, for dimensionality reduction before later classification. lDA is closely related to anova (analysis of variance). PCA is used for analyzing. Main advantage of PCA is that once patterns are found and data is compressed that is by reducing the number of dimension without much loss of information. Dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction. Feature selection approaches try to find a subset of the original variables and feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions.

关键词： Principal component analysis Frequency domain analysis Classification algorithms pattern recognition Linear discriminant analysis Transforms Feature extraction

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An efficient method for partial fingerprint recognition based on local binary pattern

An efficient method for partial fingerprint recognition base...

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IEEE international conference on Communication Control and computing Technologies (ICCCCT)

作者： S. Malathi C. Meena Research Scholar Avinashilingam Deemed University for Women India Head Computer Centre Avinashilingam Deemed University for Women India

Matching of partial fingerprints has important applications in both biometrics and forensics. It is well-known that the accuracy of minutiae-based matching algorithms dramatically decreases as the number of available minutiae decreases. When singular structures such as core and delta are unavailable, extended features can be utilized. The current Fingerprint matching technology is quite mature for matching tenprints, matching partial fingerprints still needs lots of improvement. The major challenges faced in partial fingerprint matching are the absence of sufficient level 2 features (minutiae) and other structures such as core and delta. Thus common matching methods based on alignment of singular structures would fail in case of partial prints. Pores (level 3 features) on fingerprints have proven to be discriminative features and have recently been successfully employed in automatic fingerprint recognition systems (AFRS). This paper presents suitable technique for partial fingerprint matching based on pores corresponding their Local Binary pattern (LBP) features. The first step involves extracting the pores from the partial image. These pores act as anchor points and sub window (32*32) is formed surrounding the pores. Then rotation invariant LBP histograms are obtained from the surrounding window. Finally chisquare formula is used to calculate the minimum distance between two histograms to find best matching score. The experimental results are demonstrated on NIST SD30 database and shows high matching score when matching partial with full fingerprint database.

关键词： Fingerprint recognition Histograms Feature extraction Image matching Pixel Databases Biometrics

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Performance evaluation of local appearance based periocular recognition

Performance evaluation of local appearance based periocular ...

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IEEE international conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Philip E. Miller Jamie R. Lyle Shrinivas J. Pundlik Damon L. Woodard Biometrics and Pattern Recognition Laboratory School of Computing Clemson University Clemson SC USA

The human periocular region is known to be one of the most discriminative regions of a face image, and recent studies have indicated its potential as a biometric trait. However, the bulk of the previous work concerning the periocular region consists of feasibility studies that report recognition results on controlled data, and lacks rigorous performance evaluation, thus leaving various open questions regarding the effectiveness of periocular region as a biométrie modality. In this paper we present a performance evaluation of a local periocular texture based recognition approach. Specifically, the paper investigates the effect of input image quality on recognition performance, the uniqueness of texture between different color channels, and texture information present in different color channels. recognition results of periocular texture features are compared to those of full face texture features and suggest that periocular texture features are robust to varying image quality.

关键词： Face Lighting Image recognition Iris recognition Image color analysis Face recognition

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Wi-Fi Positioning Based on Fourier Descriptors

Wi-Fi Positioning Based on Fourier Descriptors

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WRI international conference on Communications and Mobile computing, CMC

作者： Eddie C.L. Chan George Baciu S.C. Mak Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424463275

Location Fingerprinting (LF) is a common Wi-Fi positioning method, which locates a device by accessing a pre-recorded database containing the location fingerprint (i.e., the received signal strengths and coordinates). Most LF methods use the absolute received signal strength (RSS) to estimate the location. There are two drawbacks for using the absolute RSS. First, the absolute RSS in a time interval may not be representable of the IEEE 802.11 signal, as the signal may fluctuate. Second, a manual error-pone calibration is needed across different mobile platform. In this paper, we proposed to use Fourier descriptors in LF. We first transform the IEEE 802.11b Wi-Fi signal into a Fourier domain. Then, the Fourier descriptors are used to estimate the location by applying the K-Nearest Neighbor algorithm. Our experimental results show that the effectiveness of LF methods based on Fourier descriptors lead to substantially more accurate and robust localization.

关键词： Fingerprint recognition Databases Calibration Fourier transforms RAKE receivers Shape measurement Organisms Frequency pattern recognition Mobile communication

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Efficient Implementation of Image Registration Based on Feature Tracking

Efficient Implementation of Image Registration Based on Feat...

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international conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Syed Aamir Ali Shah M. Asif Manzoor M. Farooq Arif Ali Shah Abdul Bais K. M. Yahya G. M. Hassan Department of Computer Systems Engineering NWFP University of Engineering and Technology Peshawar Khyber Pakhtunkhwa Pakistan

Image registration is a process of overlaying various images of a scene. This process has four basic steps. In this work we have replaced a feature matching step with efficient and fast feature tracking. Several problems, arose due to this technique, are discussed and their solutions are proposed. The proposed algorithm is then tested on various sequences of images.

关键词： Feature extraction Image registration Transforms Detectors pattern recognition Computer vision Remote sensing

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Cognitive reasoning UBIAS & E-UBIAS systems in medical informatics

Cognitive reasoning UBIAS & E-UBIAS systems in medical infor...

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international conference on Networked computing (INC)

作者： Marek R. Ogiela Lidia Ogiela Ryszard Tadeusiewicz Institute of Automatics Faculty of Management AGH University of Science and Technology Krakow Krakow Poland

ISBN: (纸本)9781424469864;9788988678206

This paper presents the new classes of dedicated, new generation information systems called UBIAS and E-UBIAS. Such systems belong to the wider group of cognitive information systems and are designed to perform semantic interpretation of complex visual patterns in the form of medical visualization. These systems are predecessors of a new generation of intelligent systems developed for understanding medical images, and allowing to create special biometrie characteristics for particular person.

关键词： Biomedical informatics Data analysis Image analysis Resonance Biomedical imaging Character generation Intelligent systems pattern analysis Management information systems Image recognition

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Automatic Detection of Defects in Solar Modules: Image Processing in Detecting

Automatic Detection of Defects in Solar Modules: Image Proce...

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international conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Bei Nian Zhizhong Fu Li Wang Xiaoxuan Cao Pattern Recognition and Intelligent Systems University of Shanghai for Science and Technology Shanghai China University of Shanghai for Science and Technology Shanghai China

Image acquisition devices which can get infrared image of solar modules is designed by using the principles of the semiconductor's electroluminescence, and image processing is applied to the detection system which can detect the defects automatically including black pieces, fragmentation, broken grid, crack and so on. At first the defects of the infrared image are classified and then the defects' types and locations are marked out after filtering, single-chip division, gray-scale transformation, binary, feature description and extraction, finally the results are feeded back to the database. This method increases the defects' types (such as invisible crack) which the manual testing is difficult to identify, it also can eliminate human errors which manual testing may produce possibly and can reduce labor costs, defects' rates, further it can improve the detection's efficiency and productivity of production line.

关键词： Photovoltaic cells Feature extraction Computers Image recognition conferences Cameras

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Application of ANN in forecast of surface runoff

Application of ANN in forecast of surface runoff

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international conference on Networked computing (INC)

作者： L. H. Feng J. Z. Zhang Department of Geography Zhejiang Normal University ZJNU Jinhua China

ISBN: (纸本)9781424469864;9788988678206

Artificial Neural Networks (ANN) have the advantages of self-learning, self-organizing, and self-adapting. It is practical to use ANN technology to carry out forecast of surface runoff. Wet-and-low water changes of surface runoff have always been the focus of various researches. Regional water bodies are already being challenged by economic development. It shows that ANN technology is a relatively effective way of solving problems in forecast of surface runoff.

关键词： Artificial neural networks Economic forecasting Neurons Neural networks Water resources pattern recognition Multi-layer neural network Technology forecasting Feedforward systems Mathematical model

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Unified Approach to Detection and Identification of Commercial Films by Temporal Occurrence pattern

Unified Approach to Detection and Identification of Commerci...

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international conference on pattern recognition

作者： Narongsak Putpuek Nagul Cooharojananone Chidchanok Lursinsap Shin'ichi Satoh Advanced Virtual and Intelligent Computing Center Department of Mathematics Faculty of Science Chulalongkom University Bangkok Thailand National Institute of Information Chiyoda Tokyo Japan

ISBN: (纸本)9781424475421

In this paper, we propose a method to detect and identify commercial films from broadcast videos by using Temporal Occurrence pattern (TOP). Our method uses the characteristic of broadcast videos in Japan that each individual commercial film appears multiple times in broadcast stream and typically has the same duration (e.g., 15 seconds). Using this characteristic, the method can detect as well as identify individual commercial films within given video archive. Based on simple signature (global feature) for each frame image, the method first puts all frames into numbers of buckets where each bucket contains frames having the same signature, and thus they appear the same. For each bucket, TOP as a binary sequence representing the occurrence time within video archive is then generated. All buckets are then clustered using simple hierarchical clustering with similarity between TOPs allowing possible temporal offset. This clustering stage can stitch up all frames for each commercial film and identify multiple occurrence of the same commercial film at the same time. We tested our method using actual broadcast video archive and confirmed good performance in detecting and identifying commercial films.

关键词： Videos Noise reduction Films Binary sequences Clustering algorithms Streaming media Classification algorithms

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Scaling Up Feature Selection by Means of Democratization

Scaling Up Feature Selection by Means of Democratization

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23rd international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： de Haro-Garcia, Aida Garcia-Pedrajas, Nicolas Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9783642130243

The overwhelming amount of data that is available nowadays makes many of the existing machine laming algorithms inapplicable to many real-world problems Two approaches have been used to deal with this problem scaling up data mining algorithms [1] and data reduction Nevertheless. scaling up a certain algorithm is not always feasible. One of the most common methods for data reduction is feature selection. but when we face large problems, the scalability becomes an issue This paper presents a way of removing this difficulty using several rounds of feature selection on subsets of the original dataset, combined using a voting scheme The performance is very good in terms of testing error and storage reduction, while the execution time of the process is decreased very significantly The method is especially efficient when we use feature selection algorithms that are of a high computational cost An extensive comparison in 27 datasets of medium and large sizes front the UCI Machine Learning Repository and using different classifiers shows the usefulness of our method.

关键词： Feature Selection Instance-based Learning Classification Huge Problems Scale-up Divide & Conquer Data Mining Dimensionality Reduction pattern Discovery Ensembles Genetic Algorithms Relief Algorithms

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