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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zhang, Qilong Han, Tingting Harbin Huade University Harbin China Harbin Institute of Petroleum Harbin China

ISBN: (纸本)9781510691667

Computer vision and machine learning techniques play a pivotal role in advancing the recognition and analysis of dynamic human postures, particularly in sports such as Sanda, a form of Chinese martial arts. This paper explores the application of feature extraction algorithms for the efficient recognition of Sanda postures. By utilizing sensor data, including motion trajectories, joint angles, and body part coordinates, we develop a robust algorithmic framework to identify and classify various stances and movements in real-time. The research integrates traditional image processing methods with modern machine learning approaches, such as deep learning and support vector machines (SVM), to enhance the accuracy and efficiency of posture recognition. Experimental evaluations using video sequences and motion-capture data show that deep learning models, particularly convolutional neural networks (CNNs), outperform traditional techniques in both speed and precision. The findings highlight the potential of feature extraction algorithms in the development of intelligent systems for automating posture recognition in martial arts, offering significant improvements in training, performance monitoring, and real-time feedback. This work paves the way for data-driven sports methodologies, improving the effectiveness of martial arts training through advanced computational techniques. © 2025 SPIE.

关键词： feature extraction algorithms gesture recognition machine learning martial arts technology motion detection posture recognition Sanda sports training

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A COMPARISON OF feature extraction algorithms BASED ON SONAR SENSOR DATA

A COMPARISON OF FEATURE EXTRACTION ALGORITHMS BASED ON SONAR...

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ASME International Mechanical Engineering Congress and Exposition (IMECE2013)

作者： Ismail, Hesham Balachandran, Balakumar Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (纸本)9780791856246

Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.

关键词： simultaneous localization and mapping sonar feature extraction algorithms triangulation-based fusion algorithm Hough Transform triangulation Hough fusion TBF HT THY

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Comparative study of feature extraction algorithms for panorama stitching

Comparative study of feature extraction algorithms for panor...

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作者： Tianxingjian Ding College of Information and Electronic Engineering Zhejiang University

Panorama stitching is a fascinating and rapidly advancing research *** integrating many photographs that were taken from various angles and viewpoints,with various exposure and color settings,a seamless image is primarily the aim of panorama *** paper investigates the performance of three widely used feature extraction algorithms SpeededUp Robust features(SURF),Scale-Invariant feature Transform(SIFT),and Oriented FAST and Rotated BRIEF(ORB) for panorama *** study compares these algorithms in terms of accuracy,robustness,and *** indicate that while SURF and SIFT produce more accurate and robust results than ORB,they require longer processing *** study evaluates the approach on a real-world dataset and demonstrates its effectiveness in creating seamless and visually appealing *** study provides valuable insights into the trade-offs between different feature extraction algorithms and presents a practical solution for panorama stitching applications.

关键词： panorama stitching feature extraction algorithms SURF SIFT ORB

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Photovoltaic cell defect classification using convolutional neural network and support vector machine

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IET RENEWABLE POWER GENERATION 2020年第14期14卷 2693-2702页

作者： Ahmad, Ashfaq Jin, Yi Zhu, Changan Javed, Iqra Maqsood, Asim Akram, Muhammad Waqar Univ Sci & Technol China Dept Precis Machinery & Instrumentat Hefei Peoples R China Univ Lahore Dept Elect & Elect Syst Lahore Pakistan Univ Management & Technol Dept Informat & Syst Lahore Pakistan

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

关键词： support vector machines silicon feature extraction image classification inspection neural nets solar cells pattern classification learning (artificial intelligence) features extraction-based SVM photovoltaic cell defect classification convolutional neural network automatic defect classification photovoltaic modules automatic classification crystalline silicon solar cells cell cracks automatic defects classification PV cells machine learning approaches features extraction-based support vector machine solar cell defect classification solar cell defects six defective classes feature extraction algorithms

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On application of bloom filters to iris biometrics

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IET BIOMETRICS 2014年第4期3卷 207-218页

作者： Rathgeb, Christian Breitinger, Frank Busch, Christoph Baier, Harald Hsch Darmstadt Biometr & Internet Secur Res Grp Darmstadt Germany

In this study, the application of adaptive Bloom filters to binary iris biometric feature vectors, that is, iris-codes, is proposed. Bloom filters, which have been established as a powerful tool in various fields of computer science, are applied in order to transform iris-codes to a rotation-invariant feature representation. Properties of the proposed Bloom filter-based transform concurrently enable (i) biometric template protection, (ii) compression of biometric data and (iii) acceleration of biometric identification, whereas at the same time no significant degradation of biometric performance is observed. According to these fields of application, detailed investigations are presented. Experiments are conducted on the CASIA-v3 iris database for different feature extraction algorithms. Confirming the soundness of the proposed approach, the application of adaptive Bloom filters achieves rotation-invariant cancellable templates maintaining biometric performance, a compression of templates down to 20-40% of original size and a reduction of bit-comparisons to less than 5% leading to a substantial speed-up of the biometric system in identification mode.

关键词： data compression data structures feature extraction iris recognition transforms visual databases CASIA-v3 iris database adaptive Bloom fllter-based transform binary iris biometric feature vectors biometric data compression biometric identiflcation acceleration biometric performance degradation biometric template protection computer science feature extraction algorithms iris-codes rotation-invariant cancellable templates rotation-invariant feature representation feature extraction Transform computer science iris recognition Biometric Template Data compression Filters Data structures visual databases bloom

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RDFx: Audio Effects Utilising Musical Metadata

RDFx: Audio Effects Utilising Musical Metadata

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4th Annual IEEE International Conference on Semantic Computing (ICSC)

作者： Wilmering, Thomas Sandler, Mark Queen Mary Univ London Ctr Digital Mus C4DM London E1 4NS England

ISBN: (纸本)9780769541549

We present a system of audio effects capable of processing and predicting metadata associated with the processed signals. This new class of audio effects has additional inputs and outputs describing features in RDF using the Audio features Ontology. This data is processed in parallel to the audio signals. We show that by deriving the changes in metadata resulting from audio processing directly from the effect parameters, we can increase the accuracy of descriptors and reduce the computational cost by omitting repeated application of feature extraction algorithms on the output signal. Our demonstration uses a modified version of Sonic Visualiser controlled via the Open Sound Control protocol (OSC) to display metadata at various stages of processing.

关键词： RDFx audio effects audio effects audio features ontology audio signal processing audio signals descriptors accuracy feature extraction feature extraction algorithms meta data music ontology musical metadata ontologies (artificial intelligence) open sound control protocol processed signals protocols semantic audio sonic visualiser

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FACE RECOGNITION USING LOCAL BINARY PATTERN AND DISCRETE WAVELET TRANSFORM

FACE RECOGNITION USING LOCAL BINARY PATTERN AND DISCRETE WAV...

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作者： KARZAN RASHAD HAMAD NEAR EAST UNIVERSITY

学位级别：硕士

Face recognition is the practice of recognizing a person based on an image of their face and is now turned into a prominent avenue of research. This technique allows the use of a person's facial images to validate them to a secure system, criminal identification, passport verification, monitoring, and so on. Face recognition is now incorporated into a number of study fields such as machine visibility, pattern recognition, bioinformatics, etc., and is known as a potential research area. The main problem with face detection is how to accurately find the best attribute for face detection. A considerable number of innovative features extraction algorithms exist; they chiefly include three aspects: face geometry, face and statistical characteristics. As the name carries it, face recognition method uses images of people's faces as its primary source of investigation to identify individuals through digital images and video frames. Hence, proposed algorithms can be either one of geometric feature-based or appearance-based. In this thesis, Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT) are used and implemented to obtain the feature of images by using ORL and Yale databases. Face will be characterized as recognized or unrecognized face after matching with the already saved dataset. We apply DWT and LBP on the input face images which provide us high performance and clearer image compared to other algorithms. Simulations results in this work showed that the proposed approaches based on DWT and LBP was achieved very high accuracy performance.

关键词： Biometric Systems face recognition feature extraction algorithms discrete wavelet transform local binary pattern

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Local and nonlocal preserving projection for bearing defect classification and performance assessment

Local and nonlocal preserving projection for bearing defect ...

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IEEE Transactions on Industrial Electronics

作者： Yu, Jianbo School of Mechatronics Engineering and Automation Shanghai University Shanghai 200072 China

The sensitivity of various physical features that are characteristics of bearing performance may vary significantly under different working conditions. Thus, it is critical to extract the most effective information from the original physical features generated from vibration signals for bearing defect classification and performance degradation assessment. This paper proposes a local and nonlocal preserving projection (LNPP)-based feature extraction algorithm, which is different from principal component analysis that aims to discover the global structure of Euclidean space and regular manifold learning algorithms that attempt to preserve local structure in data. LNPP is capable of discovering both local and nonlocal structures of data manifold. This may enable LNPP to find the meaningful low-dimensional information hidden in the high-dimensional feature set and then to serve as a preprocessor for defect classification. Furthermore, an LNPP-based quantification index is proposed for the assessment of bearing performance degradation. An LNPP-based contribution plot for feature selection is developed to improve the degradation detection sensitiveness and to reduce false alarms. A dynamic LNPP for bearing performance assessment is further developed to consider inherent autocorrelation existing in vibration data. Detailed results are very promising and are reported in this paper. This paper will provide guidance for the applications of manifold learning algorithms (e.g., LNPP) in machine fault diagnosis and performance prognostics. © 2006 IEEE.

关键词： Principal component analysis Bearings (structural) Degradation feature extraction Learning algorithms Bearing defect Bearing performance Contribution plots Data manifolds defect classification Euclidean spaces False alarms feature extraction algorithms feature sets Global structure High dimensional Local structure Machine fault diagnosis Manifold learning Manifold learning algorithm Nonlocal Performance assessment Performance degradation assessment Physical features Vibration data Vibration signal Working conditions

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