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Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

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SIGNAL IMAGE AND VIDEO PROCESSING 2016年第2期10卷 277-284页

作者： Liu, Zhao-Guang Yang, Yang Ji, Xiu-Hua Shandong Univ Finance & Econ Sch Comp Sci & Technol Shandong Prov Key Lab Digital Media Technol Jinan Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm that is based on a saliency detection technique and on the uniform local binary pattern (ULBP). In still images and video sequences, an area that contains an open flame is always noticeable because fire is an exceptional event. Thus, to utilize the color information of flame pixels, the probability density function (pdf) of the flame pixel color can be obtained using Parzen window nonparametric estimation. This a priori pdf is then fused with the saliency detection phase as top-down information so that the flame candidate area can be extracted. To reduce the number of false alarms, the image texture of the candidate area is analyzed by ULBP, and an exponential function with two parameters is utilized to model the texture of the flame area. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while ensuring the accurate classification of positive samples. The classification performance of our proposed method is proven to be better than that of alternative algorithms.

关键词： Flame detection Saliency detection uniform local binary pattern YCbCr

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Finger vein biometric verification using block multi-scale uniform local binary pattern features and block two-directional two-dimension principal component analysis

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OPTIK 2020年 208卷 163664-163664页

作者： Hu, Na Ma, Hui Zhan, Tao Heilongjiang Univ Coll Elect Engn Harbin 150080 Peoples R China

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The focus of this paper is on proposing new schemes based on finger vein patterns by employing block uniform local binary pattern and block two-directional two-dimension principal component analysis method in order to effectively reduce data redundancy. First, a block multi-scale uniform local binary pattern (MULBP) features operator based on improved circular neighborhood is employed to extract the local texture features of finger vein images effectively. Then two-directional two-dimension principal component analysis ((2D)(2)PCA) method is applied to finger vein recognition in this paper, which can effectively reduce the dimension of feature matrix and improve system performance. Furthermore, in order to avoid the disadvantage of (2D)(2)PCA method which cannot preserve some important local features, this paper adopts (2D)(2)PCA method based on block to preserve local information of image. The experimental results revealed that our proposed method achieved superior performance for the FV-USM finger vein database with a recognition rate of 99.32 % and consistent performance for the Tianjin finger vein database with a recognition rate more than 99 %. Above results show that this new algorithm is effective and feasible in finger vein recognition system.

关键词： Biometrics Finger vein recognition uniform local binary pattern Two-directional two-dimension principal component analysis

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An Effective Feature Descriptor with Gabor Filter and uniform local binary pattern Transcoding for Iris Recognition

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pattern RECOGNITION AND IMAGE ANALYSIS 2019年第4期29卷 688-694页

作者： Huo, Guang Guo, Huan Zhang, Yangrui Zhang, Qi Li, Wenyu Li, Bin Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Jilin Peoples R China Northeast Elect Power Univ Sch Foreign Languages Jilin 132012 Jilin Peoples R China

Iris recognition is recognized as one of the most reliable and efficient technique for human identification in the biometric fields. The Gabor filter and local binary pattern (LBP) are widely adopted for feature extraction in face recognition. However, it is difficult to achieve high recognition accuracy when the Gabor filter or LBP is directly applied to iris texture representation. This paper presents an effective iris feature descriptor, which first uses 2D-Gabor filter to extract multi-orientation imaginary (MOI) feature, and then applies uniform LBP for region feature encoding. Thus, the MOI feature-by-point energy is converted into that of the uniform LBP histogram-by-block, during which the distributions of the intra- and inter-class are greatly widened. Such process largely improves distinguishability of MOI features. Finally, the Bhattacharyya distance is adopted for matching. Experimental results on CASIA and JLU iris image databases show that this method performs better for combining MOI features and LBP encoding as compared to their individual function.

关键词： biometric security iris recognition Gabor filter uniform local binary pattern

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Face Recognition Based on Spatially Enhanced uniform local binary pattern Histograms under Uncontrolled Lighting Conditions

Face Recognition Based on Spatially Enhanced Uniform Local B...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Wang Qian Xiao Guoqiang Lin Kuan College of Computer & Information Science,Southwest University Chongqing,China

This paper proposed a robust method for face recognition under uncontrolled lighting ***,the effects of illumination variation are reduced by Gamma Correction,LoG (Laplacian of Gaussian) Filtering and Contrast ***,the novel feature,which is spatially enhanced histograms of uniform local binary pattern,is generated by dividing images into small cells and extracting their corresponding histograms according to the uniform local binary pattern (LBP) in face *** descriptor combines texture information and spatial microstructures of the face ***,the classification is made by SVM (support vector machine).The experimental results show that the average precision of the proposed algorithm is up to 99.68%,which indicates the performance of the proposed algorithm is better than that of the existing methods.

关键词： face recognition uniform local binary pattern histograms support vector machine

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A texture-based rolling bearing fault diagnosis scheme using adaptive optimal kernel time frequency representation and uniform local binary patterns

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MEASUREMENT SCIENCE AND TECHNOLOGY 2017年第3期28卷 035903-035903页

作者： Chen, Haizhou Wang, Jiaxu Li, Junyang Tang, Baoping Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu Peoples R China

This paper presents a new scheme for rolling bearing fault diagnosis using texture features extracted from the time-frequency representations (TFRs) of the signal. To derive the proposed texture features, firstly adaptive optimal kernel time frequency representation (AOK-TFR) is applied to extract TFRs of the signal which essentially describe the energy distribution characteristics of the signal over time and frequency domain. Since the AOK-TFR uses the signal-dependent radially Gaussian kernel that adapts over time, it can exactly track the minor variations in the signal and provide an excellent time-frequency concentration in noisy environment. Simulation experiments are furthermore performed in comparison with common time-frequency analysis methods under different noisy conditions. Secondly, the uniform local binary pattern (uLBP), which is a computationally simple and noise-resistant texture analysis method, is used to calculate the histograms from the TFRs to characterize rolling bearing fault information. Finally, the obtained histogram feature vectors are input into the multi-SVM classifier for pattern recognition. We validate the effectiveness of the proposed scheme by several experiments, and comparative results demonstrate that the new fault diagnosis technique performs better than most state-of-the-art techniques, and yet we find that the proposed algorithm possess the adaptivity and noise resistance qualities that could be very useful in real industrial applications.

关键词： fault diagnosis time-frequency representation texture analysis adaptive optimal kernel uniform local binary pattern noise resistance

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DEEP BELIEF NETWORK BASED FINGER VEIN RECOGNITION USING HISTOGRAMS OF uniform local binary patternS OF CURVATURE GRAY IMAGES

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第5期15卷 1701-1715页

作者： Fang, Zhi-Min Lu, Zhe-Ming Quzhou Univ Coll Elect & Informat Engn 78 Jiuhua North Ave Quzhou 324000 Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China

In the existing biometric recognition area, the Finger Vein Recognition (FVR) technology has strong universality and applicability. Conventional FVR schemes are based on extracting features of the overall image or features of the vein pattern. Because there are a large amount of redundant data in features acquired from the whole Finger Vein Image (FVI), the time complexity is high, and the features extracted from the vein pattern are greatly affected by the image segmentation algorithm. Aiming at the low recognition rate of traditional FVR technology and the high requirement of image preprocessing in the early stage, this paper proposes a finger vein recognition method based on Deep Learning (DL) and features extracted from curvature gray images. Firstly, the curvature of the finger vein image is calculated by using a two-dimensional Gaussian template, and the curvature gray image is obtained. And then the histogram feature is generated by the uniform local binary pattern (ULBP) operator, and input into the Deep Belief Network (DBN) for vein image recognition. Experimental results show that our scheme is effective and better than existing schemes.

关键词： Finger vein recognition Deep learning Deep belief network uniform local binary pattern Curvature gray image

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Automatic polyp detection for wireless capsule endoscopy images

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10952-10958页

作者： Li, Baopu Meng, Max Q. -H. Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

Wireless capsule endoscopy (WCE) opens a new stage for diagnosing gastrointestinal tract diseases since it enables direct visualization of the small intestine for the first time. However, it requires a clinician's long time inspection due to a great number of images produced by the procedure. Therefore, it may be beneficial to devise an automatic detection system to help clinicians identify problematic images. In this work, we attempt to design a computerized scheme aiming for polyp WCE image recognition though polyp in WCE images show great variations in appearance. This scheme utilizes a new texture feature to characterize WCE images, which integrates advantages of wavelet transform and uniform local binary pattern. With support vector machine (SVM) as a classifier, extensive experiments on our present image data, which consists of 600 normal WCE images and 600 polyp WCE images chosen from 10 patients, verify that it is promising to utilize the proposed scheme to detect polyp WCE images. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Wireless capsule endoscopy image Wavelet transform uniform local binary pattern Polyp Support vector machine

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Face recognition algorithm based on discriminative dictionary learning and sparse representation

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NEUROCOMPUTING 2016年第PartB期174卷 749-755页

作者： Lu, Zhenyu Zhang, Linghua Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

In order to overcome the defect that the face recognition (FR) rate is greatly reduced in the existing uncontrolled environments such as the change of illumination, occlusion, and posture, etc, Face recognition algorithm based on discriminative dictionary learning and regularized robust coding was proposed. In this proposed algorithm, the Gabor amplitude images of a face image are obtained via using Gabor filter at first, then we extract the uniform local binary histogram and use Fisher criterion to gain a new dictionary, finally the test image is classified as the existing class via sparse representation Coding. The experimental results obtained from Extended Yale B databases and AR databases show that the proposed algorithm has higher face recognition rate in the existing uncontrolled environments in comparison with K-SVD, LC-K-SVD, FDDL and so on. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Face recognition Regularized sparse representation uniform local binary pattern Gabor filtering Dictionary learning

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UMLBP - A Novel Approach for Face Recognition System using OPENCV 7

UMLBP - A Novel Approach for Face Recognition System using O...

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7th International Conference on Reliability, Infocom Technologies and Optimization - Trends and Future Directions (ICRITO)

作者： Kumar, Manish Gupta, Rahul Kumar, Dinesh Raju, Kota Solomon Kurukshetra Univ Dept Elect Sci Kurukshetra Haryana India YMCA Univ Sci & Technol Dept Elect Engn Faridabad India CSIR CEERI Pilani Cyber Phys Syst Grp Pilani Rajasthan India

ISBN: (纸本)9781538646922

Several face recognition algorithms have been proposedin the last decade which provide a biometric authentication process for various utilities. In the present work, a novel algorithm for face verification considering both lightening and shape information for representing the face images, has been presented. In this approach, the region of interest in face is first divided into 8x8 region from which uniform Mean local binary pattern (UMLBP) histograms are extracted and concatenated into asingle histogram with enhanced feature efficiently representing the face. The matching is done using a K nearest neighbor classifier with minimum error based similarity measure. A number of performed trials reveal that the given approach is better overall considered methods (basic LBP, MLBP and ULBP methods). All experiments have been performed on ORL database which include evaluating the efficacy of the approach over different face angles, illumination and rotation of the query image. Testing was performed using 10 cross validation scheme and average results have been considered. Recognition rate has been recorded as 90 percent on 3 face training of single person and 99 percent on 9 face training of single person. The proposed method also allows for the fast feature extraction and testing for a given platform using OPENCV.

关键词： Face recognition system local binary pattern uniform Mean local binary pattern Mean local binary pattern uniform local binary pattern

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Occlusion Invariant Palmprint Recognition with ULBP Histograms 11

Occlusion Invariant Palmprint Recognition with ULBP Histogra...

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11th International Conference on Image and Signal Processing (ICISP)

作者： Tamrakar, Deepti Khanna, Pritee PDPM Indian Inst Informat Technol Design & Mfg Jabalpur India

This work presents a palmprint recognition approach based on the local distribution of uniform patterns. Preprocessing segments palmprint region from foreground of image as well as aligns the palmprint along the line between tips of middle finger and mid of wrist. Palmprint texture has line and wrinkles like features which are a type of uniform patterns. uniform LBP (ULBP) refers to the pattern of uniform appearance with limited discontinuities. ULBP histograms are used as features to handle occlusion upto 36%. Experiments are performed on 400 classes of the PolyU 2D, 600 classes of CASIA and 150 classes of IIITDMJ palmprint database. Correct identification rates of 99.6% for normal palmprints and 99% for randomly occluded palmprints are obtained on PolyU 2D database. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： uniform local binary pattern Entropy Chi-square distance EER Occlusion

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