期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到48篇文章
< 1 2 3 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Vehicle detection algorithm based on codebook and local binary patterns algorithms 认领 引用 被引量：1
1
作者 许雪梅 周立超 +1 位作者 墨芹 郭巧云 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第2期593-600,共8页
Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establis... Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy. 展开更多
关键词 background modeling Gaussian pyramid Codebook Local binary patterns（LBP） moving vehicle detection
暂未订购 下载PDF
Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold 认领 引用 被引量：1
2
作者 Usman Ali Muhammad Tariq Mahmood 《Computers, Materials & Continua》 SCIE EI 2022年第4期1597-1611,共15页
Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection ... Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods. 展开更多
关键词 Adaptive threshold blur measure defocus blur segmentation local binary pattern support vector machine
暂未订购 下载PDF
Local binary pattern-based reversible data hiding 认领 引用 被引量：5
3
作者 Monalisa Sahu Neelamadhab Padhy +1 位作者 Sasanko Sekhar Gantayat Aditya Kumar Sahu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2022年第4期695-709,共15页
A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided ... A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed. 展开更多
关键词 hiding capacity(HC) local binary pattern(LBP) peak signal-to-noise ratio(PSNR) reversible data hiding
暂未订购 下载PDF
A Novel Tracking-by-Detection Method with Local Binary Pattern and Kalman Filter 认领 引用 被引量：1
4
作者 Zhongli Wang Chunxiao Jia +6 位作者 Baigen Cai Litong Fan Chuanqi Tao Zhiyi Zhang Yinling Wang Min Zhang Guoyan Lyu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2018年第3期74-87,共14页
Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experim... Tracking-Learning-Detection( TLD) is an adaptive tracking algorithm,which tracks by learning the appearance of the object as the video progresses and shows a good performance in long-term tracking task.But our experiments show that under some scenarios,such as non-uniform illumination changing,serious occlusion,or motion-blurred,it may fails to track the object. In this paper,to surmount some of these shortages,especially for the non-uniform illumination changing,and give full play to the performance of the tracking-learning-detection framework, we integrate the local binary pattern( LBP) with the cascade classifiers,and define a new classifier named ULBP( Uniform Local Binary Pattern) classifiers. When the object appearance has rich texture features,the ULBP classifier will work instead of the nearest neighbor classifier in TLD algorithm,and a recognition module is designed to choose the suitable classifier between the original nearest neighbor( NN) classifier and the ULBP classifier. To further decrease the computing load of the proposed tracking approach,Kalman filter is applied to predict the searching range of the tracking object.A comprehensive study has been conducted to confirm the effectiveness of the proposed algorithm (TLD _ULBP),and different multi-property datasets were used. The quantitative evaluations show a significant improvement over the original TLD,especially in various lighting case. 展开更多
关键词 Tracking-Learning-Detection (TLD) local binary pattern (LBP) Kalman filter
暂未订购 下载PDF
Novel similarity measures for face representation based on local binary pattern 认领 引用
5
作者 祝世虎 封举富 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2009年第2期223-226,共4页
The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter ... The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features. 展开更多
关键词 similarity measurement local binary pattern Earth Mover＇s Distance IMage Euclidean Distance
暂未订购 下载PDF
An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE 认领 引用 被引量：3
6
作者 Kamal Chandra Paul Semih Aslan 《Optics and Photonics Journal》 2021年第4期63-78,共16页
This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have ... This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30° on either side from the front face showed an average face recognition precision of 72.25%-81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats. 展开更多
关键词 Face Detection Face Recognition Low Resolution Feature Extraction Security System Access Control System Viola-Jones Algorithm LBPH Local Binary Pattern Histogram
暂未订购 下载PDF
Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain 认领 引用 被引量：1
7
作者 D. R. Arun C. Christopher Columbus K. Meena 《Circuits and Systems》 2016年第10期3142-3149,共8页
Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach... Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier. 展开更多
关键词 Biometrics Finger Knuckle Print Contourlet Transform Local Binary Pattern (LBP) Local Directional Pattern (LDP) Local Derivative Ternary Pattern (LDTP) Local Texture Description Framework Based Modified Local Directional Pattern (LTDF_MLDN) Nearest Neighbor (NN) Classifier Extreme Learning Machine (ELM) Classifier
暂未订购 下载PDF
Classification of Gastric Lesions Using Gabor Block Local Binary Patterns 认领 引用
8
作者 Muhammad Tahir Farhan Riaz +1 位作者 Imran Usman Mohamed Ibrahim Habib 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期4007-4022,共16页
The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors ... The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features. 展开更多
关键词 Texture analysis Gabor filters gastroenterology imaging convolutional neural networks block local binary patterns
暂未订购 下载PDF
A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis 认领 引用
9
作者 Yao Taky Alvarez Kossonou Alain Clément +1 位作者 Bouchta Sahraoui Jérémie Zoueu 《Journal of Signal and Information Processing》 2020年第3期58-73,共16页
Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, seve... Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones. 展开更多
关键词 Multispectral Images Local Binary Patterns (LBP) Texture Analysis Rotation Invariance Illumination Variation Blurring Invariance
暂未订购 下载PDF
Salient Local Binary Pattern for Ground-Based Cloud Classification 认领 引用 被引量：3
10
作者 刘爽 王春恒 +2 位作者 肖柏华 张重 邵允学 《Acta meteorologica Sinica》 2013年第2期211-220,共10页
Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal wi... Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods. 展开更多
关键词 salient local binary pattern local binary pattern ground-based cloud classification
Finger vein recognition using weighted local binary pattern code based on a support vector machine 认领 引用 被引量：17
11
作者 Hyeon Chang LEE Byung Jun KANG +1 位作者 Eui Chul LEE Kang Ryoung PARK 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2010年第7期514-524,共11页
Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible t... Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method. 展开更多
关键词 Finger vein recognition Support vector machine (SVM) Weight Local binary pattern (LBP)
Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern 认领 引用 被引量：1
12
作者 Qicong WANG Binbin WANG +4 位作者 Xinjie HAO Lisheng CHEN Jingmin CUI Rongrong JI Yunqi LEI 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第6期1118-1129,共12页
To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting ... To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms. 展开更多
关键词 face recognition global feature local feature,linear discriminant analysis local binary pattern decision fusion
暂未订购 下载PDF
Foliar fungal disease classification in banana plants using elliptical local binary pattern on multiresolution dual tree complex wavelet transform domain 认领 引用
13
作者 Deepthy Mathew C.Sathish Kumar KAnita Cherian 《Information Processing in Agriculture》 EI CSCD 2021年第4期581-592,共12页
The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf bl... The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage. 展开更多
关键词 Musa Plant disease classification Texture features Local binary pattern DWT Image classifiers
Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System 认领 引用 被引量：3
14
作者 Santosh Kumar Vipparthi Shyam Krishna Nagar 《International Journal of Automation and computing》 CSCD 2016年第5期457-467,共11页
This paper presents a new feature descriptor, namely local extreme complete trio pattern （LECTP） for image retrieval application. The LECTP extracts complete extreme to minimal edge information in all possible direc... This paper presents a new feature descriptor, namely local extreme complete trio pattern （LECTP） for image retrieval application. The LECTP extracts complete extreme to minimal edge information in all possible directions using trio values. The LECTP integrates the local extreme sign trio patterns （LESTP） with magnitude local operator （MLOP） for image retrieval. The performance of the LECTP is tested by conducting three experiments on Corel-5 000, Corel-10 000 and MIT-VisTex color databases, respectively. The results after investigation show a significant improvement in terms of average retrieval precision （ARP） and average retrieval rate （ARR） as compared to the other state-of-the art techniques in content based image retrieval （CBIR）. 展开更多
关键词 Image retrieval （IR） local binary patterns （LBP） average retrieval precision （ARP） average retrieval rate （ARR） database （DB）.
暂未订购 下载PDF
Palmprint Recognition Based on Statistical Local Binary Orientation Code 认领 引用
15
作者 Mei-Ru Mu Qiu-Qi Ruan 《Journal of Electronic Science and Technology》 CAS 2010年第3期230-236,共7页
A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation inf... A novel coding based method named as local binary orientation code （LBOCode） for palmprint recognition is proposed. The palmprint image is firstly convolved with a bank of Gabor filters, and then the orientation information is attained with a winner-take-all rule. Subsequently, the resulting orientation mapping array is operated by uniform local binary pattern. Accordingly, LBOCode image is achieved which contains palmprint orientation information in pixel level. Further we divide the LBOCode image into several equal-size and nonoverlapping regions, and extract the statistical code histogram from each region independently, which builds a global description of palmprint in regional level. In matching stage, the matching score between two palmprints is achieved by calculating the two spatial enhanced histograms＇ dissimilarity, which brings the benefit of computational simplicity. Experimental results demonstrate that the proposed method achieves more promising recognition performance compared with that of several state-of-the-art methods. 展开更多
关键词 Gabor filter local binary pattern orientation code palmprint recognition.
暂未订购 下载PDF
Exploiting global and local features for image retrieval 认领 引用 被引量：3
16
作者 LI Li FENG Lin +2 位作者 WU Jun SUN Mu-xin LIU Sheng-lan 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第2期259-276,共18页
Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color... Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval.However,there are some problems in both of them:1)the methods defining directly texture in color space put more emphasis on color than texture feature;2)the methods extract several features respectively and combine them into a vector,in which bad features may lead to worse performance after combining directly good and bad features.To address the problems above,a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision.The bag-of-visual words(BoW)models and color intensity-based local difference patterns(CILDP)are exploited to capture local and global features of an image.The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method.The performance of our proposed framework in terms of average precision on Corel-1K database is86.26%,and it improves the average precision by approximately6.68%and12.53%over CILDP and BoW,respectively.Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval. 展开更多
关键词 local binary patterns hue, saturation, value (HSV) color space graph fusion image retrieval
暂未订购 下载PDF
Automated detection and identification of white-backed planthoppers in paddy fields using image processing 认领 引用 被引量：14
17
作者 YAO Qing CHEN Guo-te +3 位作者 WANG Zheng ZHANG Chao YANG Bao-jun TANG Jian 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第7期1547-1557,共11页
A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective.... A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields. 展开更多
关键词 white-backed planthopper developmental stage automated detection and identification image processing histogram of oriented gradient features gabor features local binary pattern features
暂未订购 下载PDF
Feature Representation for Facial Expression Recognition Based on FACS and LBP 认领 引用 被引量：15
18
作者 Li Wang Rui-Feng Li +1 位作者 Ke Wang Jian Chen 《International Journal of Automation and computing》 CSCD 2014年第5期459-468,共10页
In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression featu... In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience. 展开更多
关键词 Local binary patterns (LBP) facial expression recognition active shape models (ASM) facial action coding system (FACS) feature representation
暂未订购 下载PDF
Coal rock image recognition method based on improved CLBP and receptive field theory 认领 引用 被引量：6
19
作者 Chuanmeng Sun Ruijia Xu +2 位作者 Chong Wang Tiehua Ma Jiaxin Chen 《Deep Underground Science and Engineering》 2022年第2期165-173,共9页
Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed a... Rapid coal-rock identification is one of the key technologies for intelligent and unmanned coal mining.Currently,the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy.In view of the evident differences between coal and rock in visual attributes such as color,gloss and texture,the complete local binary pattern(CLBP)image feature descriptor is introduced for coal and rock image recognition.Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher-order pixels and the concave and convex areas between adjacent sampling points,this paper proposes a higher-order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median,and replace the binary differential with a second-order differential.Meanwhile,for the high dimensionality of CLBP descriptor histogram and feature redundancy,deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.With relevant experiments conducted,the following conclusion can be drawn:(1)Compared with that of the original CLBP,the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3%under strong noise interference;(2)Compared with that of the original CLBP model,the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s,a reduction of 71.0%;compared with the improved CLBP model(without the fusion of receptive field theory),it can shorten the recognition time by 97.0%,but the accuracy rate still maintains more than 98.5%.The method offers a valuable technical reference for the fields of mineral development and deep mining. 展开更多
关键词 coal-rock identification complete local binary pattern receptive field texture feature
暂未订购 下载PDF
Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features 认领 引用 被引量：3
20
作者 Diyar Qader Zeebaree Adnan Mohsin Abdulazeez +2 位作者 Dilovan Asaad Zebari Habibollah Haron Haza Nuzly Abdull Hamed 《Computers, Materials & Continua》 SCIE EI 2021年第3期3363-3382,共20页
Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve ... Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity. 展开更多
关键词 Breast cancer ultrasound image local binary pattern feature extraction noise reduction filters fusion
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 3 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈