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检索条件"主题词=Local binary pattern"

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Adaptive Fuzzy local binary pattern for Texture Classification 2

Adaptive Fuzzy Local Binary Pattern for Texture Classificati...

引用

2nd International Conference on Man and Machine Interfacing (MAMI)

作者： Girish, Gandham Dash, Jatindra Kumar Natl Inst Sci & Technol Dept Elect & Commun Engn Berhampur 761008 Odisha India Natl Inst Sci & Technol Dept Comp Sci & Engn Berhampur 761008 Odisha India

ISBN: (纸本)9781538629895

Many local texture features are proposed for texture classification and retrieval. Fuzzy local binary pattern (FLBP) is one of such promising texture descriptor found in the literature. However, the fuzzification parameter (T) used in FLBP is computed empirically and not adaptive to local content. In addition, one need to perform several experiments over a large range of T to find its optimal value. In this paper a novel technique is proposed to compute fuzzification parameter and the resulting descriptor is named as Adaptive Fuzzy local binary pattern (AFLBP). In the proposed technique, the computation of fuzzyfication parameter is adaptive in nature and thereby capture most representative feature for each pixel in the image. The earlier paper makes use of Support Vector Machine (that uses a non-linear kernel) to test the discriminating ability of the FLBP descriptor. Here, in this paper, the K-Nearest Neighbors (kNN) classifier is used as a classifier to investigate the discriminating strength of the proposed feature descriptor. The discriminating ability of the proposed texture descriptor is compared with that of local binary pattern (LBP) and fuzzy local binary pattern (FLBP) using Brodatz texture database. The result shows that the proposed descriptor outperforms other competing descriptors used in the experiment irrespective of the distance measure used for classification. It is also observed that the k-NN classifier using Manhattan distance outperforms all other combinations.

关键词： local binary pattern Fuzzy local binary pattern Texture Classification

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Identification of Cashmere/wool based on Pairwise Rotation Invariant Co-occurrence local binary pattern

Identification of Cashmere/wool based on Pairwise Rotation I...

引用

International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Lu, Kai Chai, Xinyu Xie, Haoyang Yu, Zhicai Zhong, Yueqi Donghua Univ Coll Text Shanghai Peoples R China Minist Educ Key Lab Text Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781538626528

The identification of cashmere and wool fibers is a challenge of textile field. The two animal fibers are very similar in surface morphology, performance of physics and chemistry. In this paper, we proposed a new method for automatic identification of cashmere and wool fibers with high accuracy. The Pairwise Rotation Invariant Co-occurrence local binary patterns was used to represent the microscopic images of cashmere/wool fiber. Every fiber image was converted to a vector, which is a histogram of LBPs extracted from fiber images. The vectors were fed into Support Vector Machine for a supervised classification. The experimental results indicated that identification accuracy is about 90% and the proposed method is robust under datasets with various blend ratios.

关键词： cashmere wool identification fication local binary pattern rotation invariant co-occurrence

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Android Malware Detection Using local binary pattern and Principal Component Analysis 3rd

Android Malware Detection Using Local Binary Pattern and Pri...

引用

3rd International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wu, Qixin Qin, Zheng Zhang, Jinxin Yin, Hui Yang, Guangyi Hu, Kuangsheng Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Hunan Key Lab Big Data Res & Applicat Changsha Hunan Peoples R China Hunan Inst Metrol & Test Changsha Hunan Peoples R China

ISBN: (纸本)9789811063855;9789811063848

Nowadays, analysis methods based on big data have been widely used in malicious software detection. Since Android has become the dominator of smartphone operating system market, the number of Android malicious applications are increasing rapidly as well, which attracts attention of malware attackers and researchers alike. Due to the endless evolution of the malware, it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information. In this paper, we propose a novel Android malware detection method based on binary texture feature recognition by local binary pattern and Principal Component Analysis, which can visualize malware and detect malware accurately. Also, our method analyzes malware binary directly without any decompiler, sandbox or virtual machines, which avoid time and resource consumption caused by decompiler or monitor in this process. Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

关键词： Android malware detection binary texture feature local binary pattern Principal component analysis

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Leaf Classification Using Completed local binary pattern Of Textures 7

Leaf Classification Using Completed Local Binary Pattern Of ...

引用

7th IEEE International Advance Computing Conference (IACC)

作者： Muthevi, Anilkumar Uppu, Ravi Babu Acharya Nagarjuna Univ Aditya Coll Engn & Technol Dept Comp Sci & Engn Surampalem AP India Narsimha Reddy Engn Coll Dept Comp Sci & Engn Secunderabad TS India

ISBN: (纸本)9781509015603

This paper, introduces utilizing the magnitude component of local binary pattern (LBP) apart from sign component (which is considered as conventional method). We applied this Completed local binary pattern (CLBP) on plant leaf classification by randomly taken divergent blocks of each texture data set. This approach is also useful for the identification of quality leaves for the automation of grading process in commercial crops like Tobacco etc. By combining Center pixel CLBP (CCLBP), Signed component of CLBP (SCLBP) and magnitude part of CLBP (MCLBP) there is a considerable development can be achieved for rotationally invariant texture classification.

关键词： local binary pattern Leaf Recognition Rotation invariance Image processing Feature selection Texture classification

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Application of local binary pattern and Human Visual Fibonacci Texture Features for Classification Different Medical Images

Application of Local Binary Pattern and Human Visual Fibonac...

引用

Conference on Mobile Multimedia/Image Processing, Security, and Applications

作者： Sanghavi, Foram Agaian, Sos Univ Texas San Antonio Div Engn San Antonio TX 78249 USA

ISBN: (数字)9781510609440

ISBN: (纸本)9781510609433;9781510609440

Textural information plays a critical role in performing and understanding the analysis for different types of microscopic images. The local binary patterns (LBP) have emerged among the most efficient texture features because of its easy implementation, rotation invariance, and robustness to monotonic illumination changes. However, the LBP is sensitive to noise and nonmonotonic illumination changes, it is unable to capture macrostructural information, and has large feature vector size. The goal of this paper is to (a) present an extended variant of the LBP, called the Fibonacci -p pattern and (b) analyze the LBP and Fibonacci -p pattern based texture features for different medical images such as histopathology images, MRI images, CT images, and mammograms. The performance of the classification system of 251 prostate histopathology is analyzed using evaluation parameters such as accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. On computer simulation, n, an increase in cancer classification accuracy is observed from 87.42% (LBP features) to 96.69% (Fibonacci -p pattern features) while maintaining the computational efficiency. Finally, on comparing with the traditional LBP, the Fibonacci -p patterns have approximately the same computational cost, lesser feature size, and the Human Visual Fibonacci System has robustness to illumination changes, additional texture information, and enhanced edge information.

关键词： Fibonacci -p patterns prostate cancer human visual Fibonacci system cancer detection Modified Fibonacci pattern local binary pattern

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Automatic License Plate Recognition Using local binary pattern and Histogram Matching 1

引用

13th International Conference on Intelligent Computing (ICIC)

作者： Bachchan, Ashutosh Kumar Gorai, Apurba Gupta, Phalguni Natl Inst Tech Teachers Training & Res Kolkata 700106 India

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

This paper proposes new real time license plate recognition (LPR) system that is capable of motion tracking and recognition of license plate. The best frame taken from the video has been chosen which is found to be about 4 m apart from camera position. For further processing, lower half section of vehicle image has been cropped of sized (450 x 140) while tracking. local binary pattern (LBP) and histogram matching technique are used to detect license plate. Due to the robustness of LBP features, this method can adaptively deal with various changes such as rotation, scaling, and illumination in the license plate. Segmentation of the plate region into disjoint characters has been done with bounding box technique with some modifications. Recognition has been done by calculating histogram features. Minimum distance classifier has been used for features matching. The system is tested on more than 300 images and it gives 96.14% detection and 89.35% of recognition accuracy. This system is designed to recognize license plate of small, medium as well as large vehicles. It is also capable to detect single line and two line license plates format.

关键词： local binary pattern License plate recognition Optical character recognition

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A NOVEL CROWD DENSITY ESTIMATION TECHNIQUE USING local binary pattern AND GABOR FEATURES 14

A NOVEL CROWD DENSITY ESTIMATION TECHNIQUE USING LOCAL BINAR...

引用

14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Pail, Abhilash K. Karunakar, A. K. Raghavendra, U. Manipal Univ Manipal Inst Technol Dept Comp Applicat Manipal Karnataka India Manipal Univ Manipal Inst Technol Dept Instrumentat & Control Engn Manipal Karnataka India

ISBN: (纸本)9781538629390

Crowd density estimation is an effective automated video surveillance technique to ensure crowd safety. In spite of various efforts being taken to estimate crowd density, it remains a challenging task. This paper proposes a new texture feature-based approach for the estimation of crowd density where two efficient texture features namely local binary pattern (LBP) and Gabor Filter are used. The LBP features are computed using an extended version which reduces the dimension of conventional LBP and the Gabor features are extracted after convolving the original image with a bank of Log-Gabor filters computed at different scales and orientations. Finally, the LBP and Gabor features are concatenated to yield the final feature vector which is used to train a multi-class Support Vector Machine (SVM) classifier. The proposed technique is evaluated on the benchmarked PETS 2009 dataset, and a maximum accuracy of 90.3% is obtained for the proposed texture combination. The experimental results show the better performance of the proposed approach as compared to other conventional techniques.

关键词： local binary pattern

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Fingerprint Spoof Detection using Wavelet based local binary pattern 8

Fingerprint Spoof Detection using Wavelet based Local Binary...

引用

8th International Conference on Graphic and Image Processing (ICGIP)

作者： Kumpituck, Supawan Li, Dongju Kunieda, Hiroaki Isshiki, Tsuyoshi Tokyo Inst Technol Dept Commun & Comp Engn Tokyo Japan

ISBN: (纸本)9781510609518;9781510609525

In this work, a fingerprint spoof detection method using an extended feature, namely Wavelet-based local binary pattern (Wavelet-LBP) is introduced. Conventional wavelet-based methods calculate wavelet energy of sub-band images as the feature for discrimination while we propose to use local binary pattern (LBP) operation to capture the local appearance of the sub-band images instead. The fingerprint image is firstly decomposed by two-dimensional discrete wavelet transform (2D-DWT), and then LBP is applied on the derived wavelet sub-band images. Furthermore, the extracted features are used to train Support Vector Machine (SVM) classifier to create the model for classifying the fingerprint images into genuine and spoof. Experiments that has been done on Fingerprint Liveness Detection Competition (LivDet) datasets show the improvement of the fingerprint spoof detection by using the proposed feature.

关键词： Biometrics fingerprint spoof detection feature extraction wavelet transform local binary pattern SVM

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Mental activity detection from EEG records using local binary pattern method

Mental activity detection from EEG records using local binar...

引用

2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017

作者： Türk, Ömer Özerdem, Mehmet Siraç Midyat Meslek Yüksekokulu Mardin Artuklu Üniversitesi Mardin Turkey Elektrik-Elektronik Mühendisliǧi Dicle Üniversitesi Diyarbakir Turkey

ISBN: (纸本)9781538618806

Electroencephalogram signals are widely used in the detection of different activities but not in the desired level. In this study with this motivation, it is aimed to obtain the attributes by using the local Bilinear pattern (LBP) method of EEG records for various mental activities and to classify these features by k-Nearest Neighbor (k-NN) method. The binary classification performance of these EEG records containing 5 mental tasks was evaluated. In addition, in order to evaluate classification performance, confusion matrix was used as model performance criterion. In the study, the average of the classification performance of all participants was found as 87.38%. As a model performance criterion from the participants' classification of mental activity, accuracy was 85.03%, precision was 85.40% and sensitivity was 85.47%. So, as a result the obtained results support the literature and the applicability of the LBP method for EEG markings has been confirmed. © 2017 IEEE.

关键词： local binary pattern

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Content-Independent Face Presentation Attack Detection with Directional local binary pattern 12th

Content-Independent Face Presentation Attack Detection with ...

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Qin, Le Zhang, Le-Bing Peng, Fei Long, Min College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410014 China

ISBN: (纸本)9783319699226

Aiming to counter photo attack and video attack in face recognition (FR) systems, a content-independent face presentation attack detection scheme based on directional local binary pattern (DLBP) is proposed. In order to minimize the influences of the image content, DLBP is proposed to investigate the noise characteristics of the facial image. By using directional difference filtering, the discrepancies between the real face and the facial artefact in terms of the consistency of adjacent pixels are effectively exploited. With the DLBP feature, the detection is accomplished by using a Softmax classifier. Experiments are done with four public benchmark databases, and the results indicate its effectiveness both in intra-database and cross-database testing. © 2017, Springer International Publishing AG.

关键词： local binary pattern

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