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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Rohit Verma Mohamed-Yahia Dabbagh Dept. of Electrical and Computer Engineering University of Waterloo

ISBN: (纸本)9781479900312

Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called local binary patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.

关键词： Facial expression recognition local binary patterns Support vector machine AdaBoost Support Vector Network EXPRESSION ANALYSIS Facial Recognition expression recognition binary binary pattern ADABOOST Face LBP gene LBP computer human interaction

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DOMINANT MULTI-DIMENSIONAL local binary patterns

DOMINANT MULTI-DIMENSIONAL LOCAL BINARY PATTERNS

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IEEE International Conference on Signal Processing, Communications and Computing

作者： Niraj P. Doshi Gerald Schaefer Department of Computer Science Loughborough University U.K.

ISBN: (纸本)9781479910267

local binary patterns (LBP) are known as a simple yet powerful texture descriptor encoding local neighbourhood properties. LBP descriptors can be calculated at different radii, leading to a multi-resolution texture characterisation. Multidimensional LBP (MD-LBP) utilises this concept, while also maintaining the relationships between the different scales by building a multi-dimensional histogram of LBP features. Although this has been shown to give good discriminatory power, the resulting feature vectors are also rather large. In this paper, we show that Dominant MD-LBP (D-MD-LBP), which utilises only dominant texture bins, provides an effective texture descriptor of reduced dimensionality as our experimental results, run on three benchmark datasets of the Outex test suite, confirm.

关键词： texture texture classification local binary patterns multi-dimensional LBP

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Deep Learning and Handcrafted Features for Virus Image Classification

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JOURNAL OF IMAGING 2020年第12期6卷 143-143页

作者： Nanni, Loris De Luca, Eugenio Facin, Marco Ludovico Maguolo, Gianluca Univ Padua Dipartimento Ingn Informaz Via Gradenigo 6 I-35131 Padua Italy

In this work, we present an ensemble of descriptors for the classification of virus images acquired using transmission electron microscopy. We trained multiple support vector machines on different sets of features extracted from the data. We used both handcrafted algorithms and a pretrained deep neural network as feature extractors. The proposed fusion strongly boosts the performance obtained by each stand-alone approach, obtaining state of the art performance.

关键词： virus classification texture descriptors deep learning local binary patterns ensemble of descriptors

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Merge local binary patterns for Face Recognition

Merge Local Binary Patterns for Face Recognition

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2013 International Conference on Advances in Information Technology(ICAIT 2013)

作者： Hua Lu Mingqiang Yang Xianye Ben Jianzhong Miao School of Information Science and Engineering Shandong University

As the local binary pattern operator discards a lot of important texture features, a new texture operator is proposed in this paper, the merge local binary patterns(MLBP)By using the absolute differences between P neighbors of the LBP operator and the central pixel, an subtraction operator(SLBP) like LBP is proposed for the robust of the featureBy combining original LBP operator and SLBP operator into joint distribution, texture classification will improve significantlyExtensive experiments in CMU-PIE and AR face database present the advantages of the MLBP method over other methods.

关键词： local binary patterns Average Merge local binary patterns

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FACE DETECTION IN MOBILE PHONES USING CO-OCCURRENCE OF ADJACENT local binary patterns

FACE DETECTION IN MOBILE PHONES USING CO-OCCURRENCE OF ADJAC...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Jeaff Wang Dohyoung Lee K.N. Plataniotis Multimedia Lab The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto

ISBN: (纸本)9781479903573

This paper investigates a novel combination of Co-occurrence of adjacent local binary patterns histogram and local binary patterns feature extraction methods for face detection in mobile phone applications. In particular, Co-occurrence of adjacent local binary patterns histogram feature extraction provides exceptionally high discriminative power in face/non-face classification and hence is used to ensure the high accuracy of the proposed face detector. local binary patterns feature extraction has low computation complexity and is thus used to reduce the overall processing speed. In the conducted face detection experiments, the proposed face detector yields comparable or better performance as well as faster computation speed than the existing best methods.

关键词： Frontal face detection Feature extraction local binary patterns Mobile Phone Mobile phones Feature extraction Facial Recognition binary binary pattern PROCESSING SPEED

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Breast Density Classification using local Septenary patterns: A Multi-resolution and Multi-Topology Approach 32

Breast Density Classification using Local Septenary Patterns...

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32nd IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)

作者： Rampun, Andrik Scotney, Bryan W. Morrow, Philip J. Wang, Hui Ulster Univ Sch Comp Coleraine BT52 1SA Londonderry North Ireland Ulster Univ Sch Comp Jordanstown BT37 0QB Newtownabbey North Ireland

ISBN: (纸本)9781728122861

We present an extension of our previous work in [1] by investigating the use of local Septenary patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of local binary patterns (LBP) inspired by local Ternary patterns (LTP) and local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.

关键词： Breast Density Classification Mammography local binary patterns local Ternary patterns local Septernary patterns Computer Aided Diagnosis

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A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered local binary Pattern operators

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GIGASCIENCE 2020年第3期9卷 giaa017页

作者： Vi Nguyen Thanh Le Ahderom, Selam Apopei, Beniamin Alameh, Kamal Edith Cowan Univ Elect Sci Res Inst 270 Joondalup Dr Joondalup WA 6027 Australia

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined local binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the "bccr-segset" dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection.

关键词： precision agriculture morphological operators feature extraction local binary patterns contour masks weed detection computer vision

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A Facial Expression Recognition Algorithm based on CNN and LBP Feature 4

A Facial Expression Recognition Algorithm based on CNN and L...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Xu, Qintao Zhao, Najing Heilongjiang Univ Coll Elect Engn Harbin Peoples R China

ISBN: (纸本)9781728143903

In recent years, facial expression recognition technology has been widely used in computer vision, security monitoring and image classification. However, in practical application, it is difficult to solve the rotation problem of facial expression image, which leads to the decrease of expression recognition rate and is difficult to meet the actual demand. Although the convolutional neural network (CNN) can extract the high -dimensional features of the image and has the invariance of gray scale, it does not have the invariance of rotation. local binary model (LBP) is a feature extraction algorithm with rotation invariance, which can solve the rotation problem to some extent. To solve the above problems, this paper proposes a face expression recognition algorithm based on CNN and LBP, and compares this algorithm with other algorithms. The simulation results show that this algorithm can improve the expression recognition rate under rotation to some extent.

关键词： facial expression recognition convolutional neural network local binary patterns feature fusion

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Grape Leaf Disease Classification using LBP-derived Texture Operators and Colour 22

Grape Leaf Disease Classification using LBP-derived Texture ...

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22nd IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)

作者： Barburiceanu, Stefania Terebes, Romulus Meza, Serban Tech Univ Cluj Napoca Commun Dept Cluj Napoca Romania

ISBN: (纸本)9781728171661

This paper proposes new feature extractors for colour texture classification based on local binary patterns (LBP) operators which are invariant to rotation, illumination and change of the observation scale, being also robust to Gaussian noise. Our novel approaches are operating in the RGB colour space and are using a state-of-the-art image denoising algorithm embedded into a multiscale LBP feature extraction process. The results obtained in the experimental section for grape leaf disease classification on a public database, using Support Vector Machines, show that the proposed feature extractors bring a significant improvement in terms of accuracy, precision and recall when compared to recent grayscale LBP-based approaches.

关键词： image classification local binary patterns Gaussian noise texture colour grape leaf

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Faculty Facial Recognition Using Convolutional Neural Network a tool for Smart Academic Monitoring 12

Faculty Facial Recognition Using Convolutional Neural Networ...

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IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)

作者： Jayoma, Jaymer Camba, John Carlo Caraga State Univ Coll Comp & Informat Sci Butuan City Philippines

ISBN: (纸本)9781665419710

Faculty Academic Monitoring is one thing a university has to perform to ensure quality delivery of instructions. That includes proper attendance monitoring of classes, faculty consultations, and others. To address attendance monitoring in CCIS - College of Computing and Information Sciences, a facial detection and recognition was developed to identify a faculty face's important feature from a real-time captured image. The study uses a pre-trained model called facenet but acquires new training data sets to generate the desired classifier for a localized recognition system. The data are cleaned by eliminating duplicates, resizing, cropping into the desired dimension, and labeling each image data. The system used image pre-processing techniques like face alignment algorithm, landmarks, face detection, and local binary pattern. The study achieved a validation rate of 96.45% using the test validation function and an accuracy rate of 90% in the actual testing.

关键词： Embedding Triplet Loss TensorFlow local binary patterns Classifier Anchor image Negative image Positive image

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