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2nd international conference on Computer Science, Engineering and applications, ICCSEA 2012

作者： Panda, Sandeep Sahu, Sanat Jena, Pradeep Chattopadhyay, Subhagata Dept. of Computer Science and Engineering National Institute of Science and Technology Palur Hills Berhampur Odisha 761008 India

ISBN: (纸本)9783642301568

Clustering techniques are unsupervised learning methods of grouping similar from dissimilar data types. Therefore, these are popular for various data mining and pattern recognition purposes. However, their performances are data dependent. Thus, choosing right clustering technique for a given dataset is a research challenge. In this paper, we have tested the performances of a Soft clustering (e.g., Fuzzy C means or FCM) and a Hard clustering technique (e.g., K-means or KM) on Iris (150 x 4);Wine (178 x 13) and Lens (24 x 4) datasets. Distance measure is the heart of any clustering algorithm to compute the similarity between any two data. Two distance measures such as Manhattan (MH) and Euclidean (ED) are used to note how these influence the overall clustering performance. The performance has been compared based on seven parameters: (i) sensitivity, (ii) specificity, (iii) precision, (iv) accuracy, (v) run time, (vi) average intra cluster distance (i.e. compactness of the clusters) and (vii) inter cluster distance (i.e. distinctiveness of the clusters). Based on the experimental results, the paper concludes that both KM and FCM have performed well. However, KM outperforms FCM in terms of speed. FCM-MH combination produces most compact clusters, while KM-ED yields most distinct clusters. © 2012 Springer-Verlag GmbH.

关键词： pattern recognition

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IMPROVING EIGENSPACE-BASED FUZZY LOGIC SYSTEM USING A LINEAR INTERPOLATION SCHEME FOR SPEECH pattern recognition

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2013年第3期37卷 611-620页

作者： Ding, Ing-Jr Yen, Chih-Ta Natl Formosa Univ Dept Elect Engn Huwei Taiwan

The Eigen-FLS approach using an eigenspace-based scheme for fast fuzzy logic system (FLS) establishments has been attempted successfully in speech pattern recognition. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace is scarce. To tackle this issue, this paper proposes two improved-versioned Eigen-FLS methods, incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS, both of which use a linear interpolation scheme for properly adjusting the target speaker's Eigen-FLS model derived from an FLS eigenspace. Developed incremental MLED Eigen-FLS and EigenMLLR-like Eigen-FLS are superior to conventional Eigen-FLS especially in the situation of insufficient data from the target speaker.

关键词： speech pattern recognition Eigen-FLS EigenMLLR-like Eigen-FLS incremental MLED Eigen-FLS

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methods of Analysing Images based on Kansei Engineering

Methods of Analysing Images based on Kansei Engineering

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作者： Nazlina.Shaari Universiti Malaysia Sarawak

Kansei Engineering (KE) develops methods of translating and embedding perceptual and emotional qualities in product design features. In design, product emotion plays an important role in encouraging product quality and sustainability. Emotions are complex conscious states that play an essential role in our daily lives. Several methods and tools exist and used to assist the process of evaluating users 'emotional experience with the products. This paper aims to develop the process of evaluating users' emotional experience using the semantic differential method in classifying the Kansei attributes. Image feature extraction is a key issue for concept recognition in images, and particularly emotions. In evaluating the product images, KJ methods based on KE Model used to develop the full range of emotional

关键词： and their affinity cluster. The result shows that this method will be a good reference to design studies that involved with users’ subjective emotional experience with product design Keywords—Kansei, visual images. Semantic differential, KJ methods

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Edge Guided High Order Image Smoothing

Edge Guided High Order Image Smoothing

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Asian conference on pattern recognition (ACPR)

作者： Haoxing Wang Longquan Dai Xiaopeng Zhang Research Center of Precision Sensing and Control Chinese Academy of Sciences National Laboratory of Pattern Recognition Chinese Academy of Sciences

Edge-preserving smoothing has recently emerged as a valuable tool for a variety of applications in computer graphics and image processing. Edge-preserving smoothing using first order smoothness prior in the regularization term under optimization framework tends to bias the smoothing result forward the constant image. Although using high order smoothness prior can alleviate this problem, it tends to obtain the over-smoothed result. In this paper, we present an effective and practical image editing method which can sharply preserve the salient edges and at the same time smooths the continuous regions using high order smoothness prior to achieve the smoothing results different from the first order smoothness prior. Finally, we demonstrate the effectiveness of our method in the context of image denoising, image abstraction and image enhancement.

关键词： Image edge detection Smoothing methods Image color analysis Optimization Noise reduction Image coding

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pattern recognition and Modelling in Electrocardiogram Signals: Early Detection of Myocardial Ischemia and Infraction

Pattern Recognition and Modelling in Electrocardiogram Signa...

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international conference on Edge Computing and applications (ICECAA)

作者： R. Muthalagu Ramachandran R Anuradha. T Anupama PH Jose Anand A Department of Electronics and Communication Engineering Agni College of Technology Tamil Nadu Department of Bio Medical Engineering Rajalakshmi Engineering College Tamil Nadu Department of Electrical and Electronics Engineering KCG College of Technology Tamil Nadu

pattern recognition and modeling in electrocardiogram (ECG) signals play an important role in the early detection of myocardial ischemia and infarction, which are serious cardiovascular diseases that require immediate medical attention. This study provides an overview of techniques used for pattern recognition and modeling in ECG signals to aid in the early detection of myocardial ischemia and infarction. First, the article discusses the importance of ECG signals in the diagnosis and monitoring of heart diseases. It highlights specific ECG changes associated with myocardial ischemia. Understanding these ECG patterns is critical for accurate diagnosis and timely intervention. Next, the study explores various techniques used for pattern recognition and modeling in ECG signals. These techniques include classical signal processing methods, filtering, feature extraction and classification algorithms, and advanced approaches such as machine learning and deep learning. The study discusses the strengths and limitations of each technique and their applications in the diagnosis of myocardial ischemia and myocardial infarction. Also, the study addresses challenges in analyzing ECG signals such as noise, artifact interference, and the need for real-time processing. It also discusses the importance of a comprehensive database of annotated ECG signals for training and testing pattern recognition models. Finally, the potential benefits of early detection of myocardial ischemia and myocardial infarction include improved patient outcomes, reduced morbidity and mortality, and efficient use of healthcare resources. This emphasizes the need for further research and development in pattern recognition and modeling techniques to improve the accuracy and efficiency of early detection methods.

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An ICA Based Approach for Complex Color Scene Text Binarization

An ICA Based Approach for Complex Color Scene Text Binarizat...

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Asian conference on pattern recognition (ACPR)

作者： Siddharth Kherada Anoop M. Namboodiri IIIT Hyderabad India

Binarization of text in natural scene images is a challenging task due to the variations in color, size, and font of the text and the results are often affected by complex backgrounds, different lighting conditions, shadows and reflections. A robust solution to this problem can significantly enhance the accuracy of scene text recognition algorithms leading to a variety of applications such as scene understanding, automatic localization and navigation, and image retrieval. In this paper, we propose a method to extract and binarize text from images that contains complex background. We use an Independent Component Analysis (ICA) based technique to map out the text region, which is inherently uniform in nature, while removing shadows, specularity and reflections, which are included in the background. The technique identifies the text regions from the components extracted by ICA using a global thresholding method to isolate the foreground text. We show the results of our algorithm on some of the most complex word images from the ICDAR 2003 Robust Word recognition Dataset and compare with previously reported methods.

关键词： Image color analysis Lighting Robustness pattern recognition Accuracy Independent component analysis Image segmentation

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Binary particle swarm optimization for feature Selection on uterine electrohysterogram signal

Binary particle swarm optimization for feature Selection on ...

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international conference on Advances in Biomedical Engineering (ICABME)

作者： Dima Alamedine Catherine Marque Mohamad Khalil AZM Research in Biotechnology and its Applications Lebanese University (UL) Tripoli CNRS UMR 7338 Univcrsite de Technologie de Cornpiegne (UTC) Compiegne France

The Selection of pertinent features is a very important problem in pattern recognition. Therefore, we need reliable feature selection methods to reduce the number of features, through the elimination of irrelevant and noisy features. In our study we try to detect the pertinent features extracted from uterine electrohysterography that permit to classify at best labor and pregnancy contractions. The global aim of this work is to detect the preterm deliveries. In this paper we present a feature selection method based on binary particle swarm optimization. The performance of this method was tested by using three types of classifiers. The results show that this method gives common features whatever the type of classifiers.

关键词： Feature extraction Pregnancy Particle swarm optimization Entropy Classification algorithms Electromyography Frequency conversion

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A Comparative Review on Image Analysis with Machine Learning for Extended Reality (XR) applications 2nd

A Comparative Review on Image Analysis with Machine Learning...

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2nd international conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Vijayakumar, P. Dilliraj, E. SRM Inst Sci & Technol Chengalpattu Tamil Nadu India

ISBN: (纸本)9789811925412;9789811925405

Progressions in Medical, Industrial 4.0, and training require more client communication with the information in reality. Extended reality (XR) development could be conceptualized as a wise advancement and a capable data variety device fitting for far off trial and error in image processing. This innovation includes utilizing the Head Mounted Devices (HMD) built-in with a functionalities like data collection, portability and reproducibility. This article will help to understand the different methodologies that are used for 3Dimensional (3D) mode of interaction with the particular data set in industries to refine the system and uncovers the bugs in the machineries. To identify the critical medical issues, the future technology can give a comfort for the medic to diagnose it quickly. Educators currently utilizing video animation are an up-rising pattern. Important methods used for various applications like, Improved Scale Invariant Feature Transform (SIFT), Block Orthogonal Matching Pursuit (BOMP), Oriented fast and Rotated Brief (ORB) feature descriptor and Kanade-Lucas-Tomasi (KLT), Semi-Global Block Matching (SGBM) algorithm etc., In high-speed real time camera, the position recognition accuracy of an object is less than 65.2% as an average in motion due to more noise interferences in depth consistency. So, more optimization is needed in the algorithm of depth estimation. Processing time of a target tracking system is high (10.670 s), that must be reduced to provide increased performance in motion tracking of an object in real-time. XR is a key innovation that is going to work with a change in perspective in the manner in which clients collaborate with information and has just barely as of late been perceived as a feasible answer for tackling numerous basic requirements.

关键词： 3D reconstruction Augmented reality (AR) Extended reality (XR) Image depth analysis Machine learning algorithms Mixed reality (MR) Motion tracking Virtual reality (VR)

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Local Mean Directional Intensity pattern: An Efficient Descriptor for Hand Gesture recognition Using SVM Classification 2nd

Local Mean Directional Intensity Pattern: An Efficient Descr...

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2nd international conference on Intelligent Computing Systems and applications, ICICSA 2023

作者： Bahuguna, Arti Bhaumik, Gopa Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India

ISBN: (纸本)9789819754113

Hand Gesture recognition (HGR) promotes the most efficient and natural communication between users and machines. However, the performance of the HGR system is deteriorating due to challenging conditions such as illumination variation, complex backgrounds, inter-class similarities. In this paper, a handcrafted feature descriptor named as Local Mean Directional Intensity patterns (LMDIP) is proposed for effective hand gesture recognition in the aforesaid challenging environment. The proposed LMDIP descriptor aims to maintain the most relevant directional information. It assists in the detection of grey level variations that move in different directions. LMDIP explores differential excitation by calculating the mean intensity of pixels across each angular neighboring pixel. The most significant effect of employing the mutually beneficial connections between adjacent neighbors in different angular directions is that it no longer relies solely on the sign of the intensity differences between the center pixel and one of its neighbors, but also on the sign of the gap across its adjoined neighbors. The performance of the proposed LMDIP is evaluated in terms of accuracy and F1-score on five benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, ASL Static Dataset, NUS Dataset, and OUhand Datasets. The result of the experimental show that the proposed LMDIP achieves an accuracy of 92% (MUGD Set1), 98% (MUGD Set2), 75%(MUGD Set3), 61%(MUGD Set4), 71%(MUGD Set5),99%(ASL Digit), 51%(ASL Static), 83%(NUS-I Dataset), 39% (OUhands Dataset) and respectively which is better compared to the state-of-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gesture recognition

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Traffic classification based on visualization

Traffic classification based on visualization

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IEEE international conference on Networked Embedded Systems for Enterprise applications

作者： Yu Zhibin Gi-Beom Kil Yong-do Choi Sung-ho Kim Electrical Engineering and Computer Science Kyungpook National University Daegu South Korea Mobile Communication Engineering Kyungpook National University Daegu South Korea School of Computer Science and Engineering Kyungpook National University Daegu South Korea

Nowadays application based on encryption flows are increasing. Such applications are beneficial to protect the privacy but also offer convenience to hackers to avoid detection. This paper discusses the feasibility of applying a visualization technique to recognize traffic flows without reading payloads. We proposed a method to extract features from packet size and intervals and then change them to a 2-D image. Unlike most of machine learning methods which use features directly, we enhance the images with a modified mountain function to explore the potential of flow features. Finally principle component analysis is used to classify the traffic flows based on pattern recognition. The result shows that the images generated from flows can be recognized more easily after image enhancement.

关键词： pattern recognition Mice Internet Educational institutions Protocols Computer science Machine learning

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