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arXiv 2023年

作者： Karnam, Naveen Kumar Turlapaty, Anish Chand Dubey, Shiv Ram Gokaraju, Balakrishna The Biosignal Analysis Lab The Indian Institute of Information Technology A.P. Sri City India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology U.P. Allahabad Prayagraj211015 India Department of Computational Data Science An Engineering North Carolina A and T State University GreensboroNC United States

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： local binary pattern

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binary pattern BASED EDGE DETECTION FOR MOTION ESTIMATION IN H.264/AVC

BINARY PATTERN BASED EDGE DETECTION FOR MOTION ESTIMATION IN...

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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

Many fast algorithms have been developed to tackle the computational intensive motion estimation in a video encoder. In this paper, a binary pattern based edge (BPE) detection algorithm for motion estimation has been proposed. BPE captures the most representative pixels in a block in order of contribution to the distortion function by evaluating whether the individual pixels belong to the edge or background. Experimental results show that the proposed algorithm outperform other existing pixel decimation techniques, reducing by up to 62% the number of matched pixels per block with respect to the conventional N-Queen algorithm while maintaining comparable performance.

关键词： Block matching Pixel decimation local binary pattern Video compression

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COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

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RESULTS IN PHYSICS 2021年 31卷 105045页

作者： Hasoon, Jamal N. Fadel, Ali Hussein Hameed, Rasha Subhi Mostafa, Salama A. Khalaf, Bashar Ahmed Mohammed, Mazin Abed Nedoma, Jan Mustansiriyah Univ Dept Comp Sci Baghdad 10001 Iraq Univ Diyala Dept Comp Sci Diyala 32001 Iraq Univ Tun Hussein Onn Malaysia Kagawa 86400 Malaysia Bilad Alrafidain Univ Coll Dept Med Instruments Engn Tech Diyala 32001 Iraq Univ Anbar Coll Comp Sci & Informat Technol Anbar 31001 Iraq VSB Tech Univ Ostrava Dept Telecommun Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

关键词： COVID-19 diagnosis X-ray image local binary pattern Haralick Machine learning K-nearest neighbor Support vector machine

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Facial expression classification based on local spatiotemporal edge and texture descriptors 10

Facial expression classification based on local spatiotempor...

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Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research

作者： Yulia Gizatdinova Veikko Surakka Guoying Zhao Erno Mäkinen Roope Raisamo University of Tampere Finland University of Oulu Finland

ISBN: (纸本)9781605589268

Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.

关键词： human behaviour understanding expression classification spatiotemporal descriptor emotion action unit local oriented edge local binary pattern facial expression

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Model Representation for Facial Expression Recognition Based on Shape and Texture

Model Representation for Facial Expression Recognition Based...

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International Conference on Tools with Artificial Intelligence

作者： Adriana Cruz de Gois Victor Oliveria Antonino Tsang Ing Ren George D. C. Cavalcanti Center for Informatics (CIn) - Federal University of Pernambuco (UFPE)

ISBN: (纸本)9781479902279

In this paper, we present an efficient method for facial expression recognition. Three features extraction methods are combined to form a model representation for facial expressions. Once the feature and the model representation are defined a Support Vector Machine (SVM) is used for the classification task. The proposed method is tested using the Yale and Cohn-Kanade databases, which contains 165 images and 1480 images, respectively. The method presented a recognition rate of 98.1% and 93% for the Yale and Cohn-Kanade respectively.

关键词： Facial expression recognition local binary pattern Spatially Maximum occurrence model Elastic shape-texture matching Gabor Filter Support Vector Machine

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SPATIAL MOTION patternS: ACTION MODELS FROM SEMI-DENSE TRAJECTORIES

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INTERNATIONAL JOURNAL OF pattern RECOGNITION AND ARTIFICIAL INTELLIGENCE 2014年第7期28卷

作者： Thanh Phuong Nguyen Manzanera, Antoine Garrigues, Matthieu Ngoc-Son Vu ENSTA ParisTech F-91762 Palaiseau France ETIS ENSEA UCP CNRS F-95014 Cergy France

A new action model is proposed, by revisiting local binary patterns (LBP) for dynamic texture models, applied on trajectory beams calculated on the video. The use of semi-dense trajectory field allows to dramatically reduce the computation support to essential motion information, while maintaining a large amount of data to ensure robustness of statistical bag of features action models. A new binary pattern, called Spatial Motion pattern (SMP) is proposed, which captures self-similarity of velocity around each tracked point (particle), along its trajectory. This operator highlights the geometric shape of rigid parts of moving objects in a video sequence. SMPs are combined with basic velocity information to form the local action primitives. Then, a global representation of a space x time video block is provided by using hierarchical blockwise histograms, which allows to efficiently represent the action as a whole, while preserving a certain level of spatiotemporal relation between the action primitives. Inheriting from the efficiency and the invariance properties of both the semi-dense tracker Video extruder and the LBP-based representations, the method is designed for the fast computation of action descriptors in unconstrained videos. For improving both robustness and computation time in the case of high definition video, we also present an enhanced version of the semi-dense tracker based on the so-called super particles, which reduces the number of trajectories while improving their length, reliability and spatial distribution.

关键词： Action recognition semi dense trajectory beam local binary pattern dynamic texture

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A NEW ALGORITHM OF INCOMPLETE FINGERPRINT SEGMENTATION BASED ON LBP

A NEW ALGORITHM OF INCOMPLETE FINGERPRINT SEGMENTATION BASED...

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2011 IEEE International Conference on Cloud Computing and Intelligence Systems(2011年第一届IEEE云计算与智能系统国际会议 IEEE CCIS2011)

作者： Bo Zhang Jie Zhang Haixia Jiang Xiaojun Jing School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

A novel segmentation method is proposed for incomplete fingerprint based on local binary pattern (LBP). Firstly, the feature vector, based on orientation coherence, orientation entropy and gray, is defined for segmentation with SVM. Secondly, According to the problem of segmentation with SVM, with the texture of non-statistical LBP, fuzzy criterion is used for modifying segmentation. The fingerprint segmentation of the incomplete area is re-computed and measured with correlation and competition of texture, which is based on LBP. The performance of the new method is evaluated on FVC2004 database. The experimental results indicate that the proposed algorithm is effective for incomplete fingerprint.

关键词： incomplete fingerprint segmentation local binary pattern fuzzy criterion

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A NEW MULTICLASS SVM ALGORITHM AND ITS APPLICATION TO CROWD DENSITY ANALYSIS USING LBP FEATURES

A NEW MULTICLASS SVM ALGORITHM AND ITS APPLICATION TO CROWD ...

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IEEE International Conference on Image Processing

作者： Hajer Fradi Jean-Luc Dugelay EURECOM Sophia Antipolis France

ISBN: (纸本)9781479923427

Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional local binary pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.

关键词： Crowd density multiclass SVM local binary pattern dimensionality reduction LBP gene LBP dimensionality reduction binary pattern density determination Support Vector Network safety monitoring Visual surveillance algorithms multi-class

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ADAPTIVE FEATURE EXTRACTION FOR BLURRED FACE IMAGES IN FACIAL EXPRESSION RECOGNITION

ADAPTIVE FEATURE EXTRACTION FOR BLURRED FACE IMAGES IN FACIA...

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IEEE International Conference on Image Processing

作者： Hyung-Il Kim Seung Ho Lee Yong Man Ro Dept. of EE Korea Adv. Inst. of Sci. & Technol. (KAIST) Daejeon South Korea

ISBN: (纸本)9781479957521

In real world facial expression recognition, blurred face images could hamper achieving high performance due to the lack of distinct edges and textures. In this paper, we propose a new feature extraction method that is robust to blurred face images for facial expression recognition. In the proposed method, the facial feature is extracted adaptively depending on the image sharpness, aiming to achieve robustness against blurred face images. Experimental results on blurred face images demonstrate that the proposed method outperforms the exiting feature extraction method.

关键词： emotion recognition face recognition feature extraction image restoration image texture adaptive facial feature extraction method blurred face image sharpness distinct edges distinct textures facial expression recognition Computer vision Face Face recognition Feature extraction Image recognition Robustness Image sharpness measurement kurtosis local binary pattern Facial Recognition Feature extraction images Face Face Image recognition image texture BLURRING emotion recognition image restoration technique kurtosis computer vision expression recognition Robustness

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Optical Flow Feature Based for Fire Detection on Video Data

Optical Flow Feature Based for Fire Detection on Video Data

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Chastine Fatichah Sirria Panah Alam Dini Adni Navastara Departement of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781728130422

A fire detection on video data using optical flow feature is presented to improve the performance of detection when using only texture or color feature. We compare two kinds of optical flow that are dense optical flow using Farneback algorithm and sparse optical flow using the Lucas Kanade algorithm. The fusion of optical flow feature and local binary pattern (LBP) as a texture feature is used to classify the video frame as fire or not fire using Support Vector Machine (SVM). There are three phases for fire detection in our framework. First, segmentation on each video frames based on Hue, Saturation, Value (HSV) color space is done to obtain the candidate of the fire area. Second, feature extraction using optical flow and LBP method is done to achieve the movement and texture features of the fire. Finally, the extracted features are classified to fire or not fire using the SVM method. The model is evaluated using stratified 10-folds cross-validation to be separated into learning process data and validation data. The best result is obtained using the Lucas Kanade optical flow feature and using a linear kernel SVM with 96.21% in accuracy.

关键词： Fire detection Optical flow farneback algorithm Lucas kanade algorithm local binary pattern Support vector machine fire detection Optic Flow Support Vector Network video data VIDEO FRAMES binary pattern Fire

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