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An improved real-time object proposals generation method based on local binary pattern

INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第4期14卷

作者： Jiang, Yanting Yan, Jia Fan, Ci'en Shi, Wenxuan Deng, Dexiang Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China

Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.

关键词： Objectness proposal generation local binary pattern computer vision object detection

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Improved local binary pattern for real scene optical character recognition

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pattern RECOGNITION LETTERS 2017年第Dec.1期100卷 14-21页

作者： Yang, Chu-Sing Yang, Yung-Hsuan Natl Cheng Kung Univ Dept Elect Engn 1 Univ Rd Tainan 701 Taiwan

A strong edge descriptor is an important topic in a wide range of applications. local binary pattern (LBP) techniques have been applied to numerous fields and are invariant with respect to luminance and rotation. However, the performance of LBP for optical character recognition is not as good as expected. In this study, we propose a robust edge descriptor called improved LBP (ILBP), which is designed for optical character recognition. ILBP overcomes the noise problems observed in the original LBP by searching over scale space, which is implemented using an integral image with a reduced number of features to achieve recognition speed. In experiments, we evaluated ILBP's performance on the ICDAR03, chars74K, IIIT5K, and Bib digital databases. The results show that ILBP is more robust to blur and noise than LBP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： local binary pattern Optical character recognition Edge descriptor Integral image Recognition speed

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A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed local binary pattern Methodology

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第1期12卷 363-372页

作者： Huang, Wen-zhun Zhang, Shan-wen Xijing Univ Sch Informat Engn Xian Peoples R China

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

关键词： Face recognition Deep convolution neural network Key points detection local binary pattern Feature extraction Optimization

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Real-time ensemble based face recognition system for NAO humanoids using local binary pattern

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2017年第3期92卷 467-475页

作者： Bolotnikova, Anastasia Demirel, Hasan Anbarjafari, Gholamreza Univ Tartu Inst Technol iCV Res Grp EE-50411 Tartu Estonia Eastern Mediterranean Univ Dept Elect & Elect Engn KKTC Gazimagusa Turkey Hasan Kalyoncu Univ Dept Elect & Elect Engn Gaziantep Turkey

NAO humanoid robots are being used in many human-robot interaction applications. One of the important existing challenges is developing an accurate real-time face recognition system which does not require to have high computational cost. In this research work a real-time face recognition system by using block processing of local binary patterns of the face images captured by NAO humanoid is proposed. Majority voting and best score ensemble approaches have been used in order to boost the recognition results obtained in different colour channels of YUV colour space, which is a default colour space provided by the camera of NAO humanoid. The proposed method has been adopted on NAO humanoid and tested under real-world conditions. The recognition results were boosted in the real-time scenario by employing majority voting on the intra-sequence decisions with window size of 5. The experimental results are showing that the proposed face recognition algorithm overcomes the conventional and state-of-the-art techniques.

关键词： Real-time face recognition local binary pattern NAO humanoid Data fusion Kullback-Leibner divergence

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Improved Weber's law based local binary pattern for dynamic texture recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第5期76卷 6623-6640页

作者： Tiwari, Deepshika Tyagi, Vipin Jaypee Univ Engn & Technol Dept Comp Sci & Engn Guna MP India Jaypee Univ Engn & Technol Comp Sci & Engn Guna MP India Jaypee Univ Engn & Technol Deparment CSE Guna MP India

Dynamic texture is the moving sequence of images that shows some form of temporal regularity. Various static texture descriptors have been extended to spatiotemporal domain for dynamic texture classification. local binary pattern (LBP) is a simple descriptor computationally but sensitive to noise and sometimes fails to capture different patterns. In view of this, a novel approach for dynamic texture classification is introduced that maintains the advantageous characteristics of uniform LBP. Inspired by theWeber's law, a simple yet very powerful, robust texture descriptor, i. e., Weber's law based LBP with center pixel (WLBPC) is proposed from the local patches based on the conventional local binary pattern approach. A noise resistant variant of Weber's law based LBP with center pixel (NR-WLBPC) is also proposed. To do this, WLBPC is extended to a 3-valued code based on a new threshold. Proposed noise resistant variant of WLBPC descriptor makes use of the indecisive bit and the uniform pattern to compute the feature vector. Center pixel information is fused with both the dynamic texture descriptors to improve the discriminative power. Extensive experimental evaluations on representative dynamic texture databases (DynTex++ and UCLA) show that the proposed descriptors show better performance in comparison to recent state-of-the-art LBP variants and other methods under both normal and noisy conditions. Noise invariant of the proposed descriptor also performs better in the presence of noise due to its robustness and discriminating capabilities.

关键词： Dynamic texture local binary pattern Uniform pattern Noise resistance Weber law

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Shear wave elastography for characterization of breast lesions: Shearlet transform and local binary pattern histogram techniques

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 91卷 13-20页

作者： Acharya, U. Rajendra Ng, Wei Lin Rahmat, Kartini Sudarshan, Vidya K. Koh, Joel E. W. Tan, Jen Hong Hagiwara, Yuki Gertych, Arkadiusz Fadzli, Farhana Yeong, Chai Hong Ng, Kwan Hoong Ngee Ann Polytech Dept Elect & Comp Engn Singapore 59949 Singapore Singapore Univ Social Sci Sch Sci & Technol Dept Biomed Engn Singapore Singapore Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur Malaysia Univ Malaya Biomed Imaging Dept Res Imaging Ctr Kuala Lumpur Malaysia Cedars Sinai Med Ctr Dept Surg Dept Pathol & Lab Med Los Angeles CA 90048 USA Univ Malaya Fac Med Dept Biomed Imaging Kuala Lumpur Malaysia

Shear wave elastography (SWE) examination using ultrasound elastography (USE) is a popular imaging procedure for obtaining elasticity information of breast lesions. Elasticity parameters obtained through SWE can be used as biomarkers that can distinguish malignant breast lesions from benign ones. Furthermore, the elasticity parameters extracted from SWE can speed up the diagnosis and possibly reduce human errors. In this paper, Shearlet transform and local binary pattern histograms (LBPH) are proposed as an original algorithm to differentiate malignant and benign breast lesions. First, Shearlet transform is applied on the SWE images to acquire low frequency, horizontal and vertical cone coefficients. Next, LBPH features are extracted from the Shearlet transform coefficients and subjected to dimensionality reduction using locality sensitivity discriminating analysis (LSDA). The reduced LSDA components are ranked and then fed to several classifiers for the automated classification of breast lesions. A probabilistic neural network classifier trained only with seven top ranked features performed best, and achieved 98.08% accuracy, 98.63% sensitivity, and 97.59% specificity in distinguishing malignant from benign breast lesions. The high sensitivity and specificity of our system indicates that it can be employed as a primary screening tool for faster diagnosis of breast malignancies, thereby possibly reducing the mortality rate due to breast cancer.

关键词： Breast lesions Benign Malignant Shear wave elastography Shearlet transform local binary pattern

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Finger texture biometric verification exploiting Multi-scale Sobel Angles local binary pattern features and score-based fusion

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DIGITAL SIGNAL PROCESSING 2017年 70卷 178-189页

作者： Al-Nima, R. R. O. Abdullah, M. A. M. Al-Kaltakchi, M. T. S. Dlay, S. S. Woo, W. L. Chambers, J. A. Newcastle Univ Sch Elect & Elect Engn Res Grp ComS2IP Newcastle Upon Tyne Tyne & Wear England Tech Coll Mosul Dept Tech Comp Engn Mosul Iraq Ninevah Univ Dept Comp & Informat Engn Mosul Iraq Al Mustansiriya Univ Dept Elect Engn Coll Engn Baghdad Iraq

In this paper a new feature extraction method called Multi-scale Sobel Angles local binary pattern (MSALBP) is proposed for application in personal verification using biometric Finger Texture (FT) patterns. This method combines Sobel direction angles with the Multi-Scale local binary pattern (MSLBP). The resulting characteristics are formed into non-overlapping blocks and statistical calculations are implemented to form a texture vector as an input to an Artificial Neural Network (ANN). A Probabilistic Neural Network (PNN) is applied as a multi-classifier to perform the verification. In addition, an innovative method for Ft fusion based on individual finger contributions is suggested. This method is considered as a multi-object verification, where a finger fusion method named the Finger Contribution Fusion Neural Network (FCFNN) is employed for the five fingers. Two databases have been employed in this paper: PolyU3D2D and Spectral 460 nm (S460) from CASIA Multi-Spectral (CASIA-MS) images. The MSALBP feature extraction method has been examined and compared with different local binary pattern (LBP) types;in classification it yields the lowest Equal Error Rate (EER) of 0.68% and 2% for PolyU3D2D and CASIA-MS (S460) databases, respectively. Moreover, the experimental results revealed that our proposed finger fusion method achieved superior performance for the PolyU3D2D database with an EER of 0.23% and consistent performance for the CASIA-MS (S460) database with an EER of 2%. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Finger texture Finger fusion local binary pattern Biometric verification Probabilistic neural network

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A Visual Secret Sharing Scheme Based on Improved local binary pattern

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INTERNATIONAL JOURNAL OF pattern RECOGNITION AND ARTIFICIAL INTELLIGENCE 2018年第6期32卷 1850017-1850017页

作者： Zhang, Wenyin Shih, Frank Y. Hu, Shunbo Jian, Muwei Linyi Univ Sch Comp Sci & Technol Linyi 276000 Shandong Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China

A visual secret sharing (VSS) scheme is intended to share secret information in a group to avoid potential treat of interruption and modification. In this paper, we present a novel VSS scheme based on the improved local binary pattern (LBP) operator. It makes full use of local contrast features of LBP for concealing secret image data into different image shares, which can be used to recover the secret easily and exactly. By varying LBP extensions, we can design various kinds of VSS schemes for sharing secret information. Compared to the currently available VSS algorithms, the proposed scheme demonstrates better randomness in shares with less pixel expansion and exact determination in reconstruction with lower computational cost.

关键词： local binary pattern visual cryptography visual secret sharing multiple secret sharing

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Two-bit transform using local binary pattern method for low-complexity block motion estimation

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第6期25卷 4593-4601页

作者： Tasyapi Celebi, Aysun Kocaeli Univ Dept Elect & Telecommun Engn Lab Integrated Syst KUTSAL Umuttepe Campus Kocaeli Turkey

Low bit-depth representation-based motion estimation approaches have been drawing considerable attention recently, mainly because of their small hardware footprint. In this paper, a new two-bit transform using a local binary pattern (LBP) for low-complexity motion estimation is proposed. The proposed approach utilizes the LBP method to obtain two-bit representations of video frames in the binarization process. Video frames are transformed into their low bit-depth representations by the LBP and then a motion estimation process is carried out using these binary frames. A Boolean exclusive-OR operation is used to calculate the number of nonmatching points metric instead of the conventional sum of absolute differences metric in the motion estimation stage. The proposed method reduces the computational complexity, especially in the binarization stage, while improving the motion estimation accuracy compared to existing one-bit and two-bit transform-based low-complexity motion estimation approaches in the literature.

关键词： local binary pattern low-complexity binarization motion estimation

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Applying the Method of Different Cumulative Bin local binary pattern (DCBLBP) to A Small Iris Region for Features in Iris Classification

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2018年第1期31卷 126-137页

作者： Ali, Ahmad N. Univ Sci Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Penang Malaysia

This paper presents an extended variant of the local binary pattern (LBP) method to extract the iris feature for iris classification system. The method is called Different Cumulative Bin local binary pattern (DCBLBP) in which the local iris information will be projected into the binary bit and applies global characteristic for features according to the ratio of bit transition along the horizontal axis. The proposed DCBLBP scheme employs the majority bit decision for assigning the bit to the reference elements where the assigned bit is unaffected by the predetermined index number of the pixel block. The results demonstrate a good classification score and show that the features extracted from the proposed DCB_LBP scheme are reliable for iris classification system.

关键词： local binary pattern Support Vector Machines Iris classification Feature extraction

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