Journal articles: 'Computer-Assisted Pattern Recognition Software Support'

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Chen, Guangyi, Tien D. Bui, and Adam Krzyżak. "Sparse support vector machine for pattern recognition." Concurrency and Computation: Practice and Experience 28, no. 7 (2015): 2261–73. http://dx.doi.org/10.1002/cpe.3492.

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Nabat, Zahraa Modher, Mushtaq Talib Mahdi, and Shaymaa Abdul Hussein Shnain. "Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)." Webology 19, no. 1 (2022): 2170–81. http://dx.doi.org/10.14704/web/v19i1/web19147.

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One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector machine. Here in the research, the novel Rain optimization algorithm and support vector machine algorithm (ROA-SVM)-based method of face recognition is provided. In ROA-SVM, Rain optimization algorithm is applied for optimizing SVM parameters at the same time. The average classification accuracy for the YALE dataset in the proposed method was 86% and for the base paper was 81%. Furthermore, in order to find optimal parameters in support vector machine, proposed ROA-SVM method efficiency has been improved by 5 percent in comparison with PSO-SVM basic research.

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Lin, Xu. "Pattern Recognition of Movements in Wushu Based on Image Processing Technology." Applied Mechanics and Materials 602-605 (August 2014): 2070–74. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2070.

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In this paper, the genetic algorithm for the key point of image capture is improved, and the joint programming of VC and Matlab is used to achieve data transmission, the FAB body identification system signal that collected with VC was processed by Matlab software, eventually Tai Chi chuan movement design computer system has got. In order to verify the validity and reliability of the platform, taking the movements development of Wu style Tai Chi chuan and 24 style Tai Chi chuan for example, this article has performed three-dimensional simulation design on Tai Chi movements. The time curve of the knee joint displacement and the average angle table of knee movement have been got by the computer design platform of Tai Chi movements, which provides digital technology support for the study of tai chi movements design.

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Di Martino, Beniamino, and Antonio Esposito. "Automatic Dynamic Data Structures Recognition to Support the Migration of Applications to the Cloud." International Journal of Grid and High Performance Computing 7, no. 3 (2015): 1–22. http://dx.doi.org/10.4018/ijghpc.2015070101.

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The work presented in this manuscript describes a methodology for the recognition of Dynamic Data structures, with a focus on Queues, Pipes and Lists. The recognition of such structures is used as a basis for the mapping of sequential code to Cloud Services, in order to support the semi-automatic restructuring of source software. The goal is to develop a complete methodology and a framework based on it to ease the efforts needed to port native applications to a Cloud Platform and simplify the relative complex processes. In order to achieve such an objective, the proposed technique exploits an intermediate representation of the code, consisting in parallel Skeletons and Cloud Patterns. Logical inference rules act on a knowledge base, built during the analysis of the source code, to guide the recognition and mapping processes. Both the inference rules and knowledge base are expressed in Prolog. A prototype tool for the automatic analysis of sequential source code and its mapping to a Cloud Pattern is also presented.

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Yang, Jie, Chenzhou Ye, and Nianyi Chen. "DMiner-I: A software tool of data mining and its applications." Robotica 20, no. 5 (2002): 499–508. http://dx.doi.org/10.1017/s0263574702004307.

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SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

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XIAN, GUANG-MING, and BI-QING ZENG. "A NEW FAULT PATTERN RECOGNITION METHOD BASED ON WPT AND DAGSVM." International Journal of Computational Intelligence and Applications 08, no. 03 (2009): 345–53. http://dx.doi.org/10.1142/s1469026809002631.

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A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.

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IFANTIS, A., and S. PAPADIMITRIOU. "SUPPORT VECTOR IDENTIFICATION OF SEISMIC ELECTRIC SIGNALS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 04 (2003): 545–65. http://dx.doi.org/10.1142/s0218001403002484.

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Traditional pattern recognition approaches usually generalize poorly on difficult tasks as the problem of identification of the Seismic Electric Signals (SES) electrotelluric precursors for earthquake prediction. This work demonstrates that the Support Vector Machine (SVM) can perform well on this application. The a priori knowledge consists of a set of VAN rules for SES signal detection. The SVM extracts implicitly these rules from properly preprocessed features and obtains generalization performance founded upon a robust mathematical basis. The potentiality of obtaining generalization potential even in feature spaces of high dimensionality bypasses the problems due to overtraining of the conventional machine learning architectures. The paper considers the optimization of the generalization performance of the SVM. The results indicate that the SVM outperforms many alternative computational intelligence models for the task of SES pattern recognition.

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Azam, Samiul, and Marina L. Gavrilova. "Biometric Pattern Recognition from Social Media Aesthetics." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 3 (2017): 1–16. http://dx.doi.org/10.4018/ijcini.2017070101.

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Online social media (OSN) has witnessed a significant growth over past decade. Millions of people now share their thoughts, emotions, preferences, opinions and aesthetic information in the form of images, videos, music, texts, blogs and emoticons. Recently, due to existence of person specific traits in media data, researchers started to investigate such traits with the goal of biometric pattern analysis and recognition. Until now, gender recognition from image aesthetics has not been explored in the biometric community. In this paper, the authors present an authentic model for gender recognition, based on the discriminating visual features found in user favorite images. They validate the model on a publicly shared database consisting of 24,000 images provided by 120 Flickr (image based OSN) users. The authors propose the method based on the mixture of experts model to estimate the discriminating hyperplane from 56 dimensional aesthetic feature space. The experts are based on k-nearest neighbor, support vector machine and decision tree methods. To improve the model accuracy, they apply a systematic feature selection using statistical two sampled t-test. Moreover, the authors provide statistical feature analysis with graph visualization to show discriminating behavior between male and female for each feature. The proposed method achieves 77% accuracy in predicting gender, which is 5% better than recently reported results.

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Cao, Dongwei, Osama T. Masoud, Daniel Boley, and Nikolaos Papanikolopoulos. "Human motion recognition using support vector machines." Computer Vision and Image Understanding 113, no. 10 (2009): 1064–75. http://dx.doi.org/10.1016/j.cviu.2009.06.002.

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CAO, NHAN THI, AN HOA TON-THAT, and HYUNG IL CHOI. "FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 06 (2014): 1456012. http://dx.doi.org/10.1142/s0218001414560126.

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Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into nonoverlap square regions for extracting LBP features. The method was experimented on three typical kinds of database: small (213 images), medium (2040 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods.

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Vodyanitskyi, V., and V. Yuskovych-Zhukovska. "ADAPTIVE VISION AI." Automation of technological and business processes 16, no. 4 (2024): 73–81. https://doi.org/10.15673/atbp.v16i4.3013.

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Abstract. As of today, computer vision systems are continuously developing and systematically improving. Machines see visual content in the form of numbers, in which each pixel represents its own piece of information. Computer vision, as a component of artificial intelligence, allows machines to see, observe and understand everything. It enables computer systems to obtain useful information from digital images, video, visual data and perform programmed actions. Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. Tracking moving objects and their identification is a difficult task, as it requires the accuracy of pattern recognition. An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. Therefore, the algorithm must be trained. The paper considers models that are trained on a high-performance computing cluster with GPU support. The developed open source software allows detection, tracking and recognition of blurry moving objects with the help of artificial intelligence that adapts to any video camera. A significant increase in accuracy is achieved thanks to machine learning.

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Guan, Fengwei, and Lianglun Cheng. "Abnormal Quality Pattern Recognition of Industrial Process Based on Multi-Support Vector Machine." Journal of Software 13, no. 9 (2018): 506–19. http://dx.doi.org/10.17706/jsw.13.9.506-519.

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Zhang, Xuelong. "Research on Data Mining Algorithm Based on Pattern Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (2019): 2059015. http://dx.doi.org/10.1142/s0218001420590156.

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With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

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Liu, Liming, Ping Li, Maoxiang Chu, and Hongbin Cai. "Stochastic gradient support vector machine with local structural information for pattern recognition." International Journal of Machine Learning and Cybernetics 12, no. 8 (2021): 2237–54. http://dx.doi.org/10.1007/s13042-021-01303-x.

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Sutcliffe, Alistair, Oscar de Bruijn, Sarah Thew, et al. "Developing visualization-based decision support tools for epidemiology." Information Visualization 13, no. 1 (2012): 3–17. http://dx.doi.org/10.1177/1473871612445832.

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The paper describes the application of user-centred design (UCD) methods to a case study of the development of visual decision support tools to support epidemiological research. Understanding the causes of obesity requires analysis of complex medical surveys and geographic information. Translating research on obesity into effective public health measures requires collaboration between medical researchers and public health analysts. The objective of this research is to develop software tools to support medical researchers and public health analysts in collaborative investigation of obesity in children. The UCD approach consisted of scenario-based design, storyboarding and prototyping to explore design options to meet the needs of public health analysts and academic researchers. An evaluation of the prototype was carried out to assess the extent to which the medical researcher model would support public health professionals in their analysis activities. The design and evaluation of the prototype are discussed. A visualization-based research and decision-support system was implemented leading to positive evaluation results from users.

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Jones, James A., Alessandro Orso, and Mary Jean Harrold. "Gammatella: Visualizing Program-Execution Data for Deployed Software." Information Visualization 3, no. 3 (2004): 173–88. http://dx.doi.org/10.1057/palgrave.ivs.9500077.

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Software systems are often released with missing functionality, errors, or incompatibilities that may result in failures in the field, inferior performances, or, more generally, user dissatisfaction. In previous work, some of the authors presented the gamma approach, whose goal is to improve software quality by augmenting software-engineering tasks with dynamic information collected from deployed software. The gamma approach enables analyses that (1) rely on actual field data instead of synthetic in-house data and (2) leverage the vast and heterogeneous resources of an entire user community instead of limited, and often homogeneous, in-house resources. When monitoring a large number of deployed instances of a software product, however, a significant amount of data is collected. Such raw data are useless in the absence of suitable datamining and visualization techniques that support exploration and understanding of the data. In this paper, we present a new technique for collecting, storing, and visualizing program-execution data gathered from deployed instances of a software product. We also present a prototype toolset, Gammatella, that implements the technique. Finally, we show how the visualization capabilities of Gammatella facilitate effective investigation of several kinds of execution-related information in an interactive fashion, and discuss our initial experience with a semi-public display of Gammatella.

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Normakristagaluh, Pesigrihastamadya, Geert J. Laanstra, Luuk J. Spreeuwers, and Raymond N. J. Veldhuis. "The Impact of Illumination on Finger Vascular Pattern Recognition." IET Biometrics 2024 (February 3, 2024): 1–17. http://dx.doi.org/10.1049/2024/4413655.

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This paper studies the impact of illumination direction and bundle width on finger vascular pattern imaging and recognition performance. A qualitative theoretical model is presented to explain the projection of finger blood vessels on the skin. A series of experiments were conducted using a scanner of our design with illumination from the top, a single-direction side (left or right), and narrow or wide beams. A new dataset was collected for the experiments, containing 4,428 NIR images of finger vein patterns captured under well-controlled conditions to minimize position and rotation angle differences between different sessions. Top illumination performs well because of more homogenous, which enhances a larger number of visible veins. Narrower bundles of light do not affect which veins are visible, but they reduce the overexposure at finger boundaries and increase the quality of vascular pattern images. The narrow beam achieves the best performance with 0% of FNMR@FMR0.01%, and the wide beam consistently results in a higher false nonmatch rate. The comparison of left- and right-side illumination has the highest error rates because only the veins in the middle of the finger are visible in both images. Different directional illumination may be interoperable since they produce the same vascular pattern and principally are the projected shadows on the finger surface. Score and image fusion for right- and left-side result in recognition performance similar to that obtained with top illumination, indicating the vein patterns are independent of illumination direction. All results of these experiments support the proposed model.

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Malon, Christopher, Seiichi Uchida, and Masakazu Suzuki. "Mathematical symbol recognition with support vector machines." Pattern Recognition Letters 29, no. 9 (2008): 1326–32. http://dx.doi.org/10.1016/j.patrec.2008.02.005.

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GENOV, ROMAN, SHANTANU CHAKRABARTTY, and GERT CAUWENBERGHS. "SILICON SUPPORT VECTOR MACHINE WITH ON-LINE LEARNING." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 385–404. http://dx.doi.org/10.1142/s0218001403002472.

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Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin returned by the SVM classifier prior to assignment, subject to inequality constraints. The training scheme lends efficiently to dedicated SVM hardware for real-time pattern recognition, implemented using resources already provided for run-time operation. Performance gains are illustrated using the Kerneltron, a massively parallel mixed-signal VLSI processor for kernel-based real-time video recognition.

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LU, CHUNYUAN, JIANMIN JIANG, and GUOCAN FENG. "A BOOSTED MANIFOLD LEARNING FOR AUTOMATIC FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 02 (2010): 321–35. http://dx.doi.org/10.1142/s0218001410007919.

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Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates.

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Wang, Yan. "Efficient Prediction Method of Defect of Monitor Configuration Software." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (2019): 340–44. http://dx.doi.org/10.20965/jaciii.2019.p0340.

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In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

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Shi, Rongmei, Jun Zhang, Zhao Xie, Jun Gao, and Xinxiang Zheng. "Robust tracking with per‐exemplar support vector machine." IET Computer Vision 9, no. 5 (2015): 699–710. http://dx.doi.org/10.1049/iet-cvi.2014.0234.

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Qi, Shuren, Yushu Zhang, Chao Wang, Jiantao Zhou, and Xiaochun Cao. "A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation." ACM Computing Surveys 55, no. 1 (2023): 1–35. http://dx.doi.org/10.1145/3479428.

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Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications toward understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This article presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. We also create a software package for a variety of widely used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting real-world applications.

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SHIH, FRANK Y., and KAI ZHANG. "SUPPORT VECTOR MACHINE NETWORKS FOR MULTI-CLASS CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 06 (2005): 775–86. http://dx.doi.org/10.1142/s0218001405004320.

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The support vector machine (SVM) has recently attracted growing interest in pattern classification due to its competitive performance. It was originally designed for two-class classification, and many researchers have been working on extensions to multiclass. In this paper, we present a new framework that adapts the SVM with neural networks and analyze the source of misclassification in guiding our preprocessing for optimization in multiclass classification. We perform experiments on the ORL database and the results show that our framework can achieve high recognition rates.

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Wang, Yingying, Jixiang Du, Hongbo Zhang, and Xiuhong Yang. "Mushroom Toxicity Recognition Based on Multigrained Cascade Forest." Scientific Programming 2020 (August 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8849011.

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Due to the tastiness of mushroom, this edible fungus often appears in people’s daily meals. Nevertheless, there are still various mushroom species that have not been identified. Thus, the automatic identification of mushroom toxicity is of great value. A number of methods are commonly employed to recognize mushroom toxicity, such as folk experience, chemical testing, animal experiments, and fungal classification, all of which cannot produce quick, accurate results and have a complicated cycle. To solve these problems, in this paper, we proposed an automatic toxicity identification method based on visual features. The proposed method regards toxicity identification as a binary classification problem. First, intuitive and easily accessible appearance data, such as the cap shape and color of mushrooms, were taken as features. Second, the missing data in any of the features were handled in two ways. Finally, three pattern-recognition methods, including logistic regression, support vector machine, and multigrained cascade forest, were used to construct 3 different toxicity classifiers for mushrooms. Compared with the logistic regression and support vector machine classifiers, the multigrained cascade forest classifier had better performance with an accuracy of approximately 98%, enhancing the possibility of preventing food poisoning. These classifiers can recognize the toxicity of mushrooms—even that of some unknown species—according to their appearance features and important social and application value.

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Wang, Wei, Yongmei Jiang, Boli Xiong, Lingjun Zhao, and Gangyao Kuang. "Contour matching using the affine‐invariant support point set." IET Computer Vision 8, no. 1 (2014): 35–44. http://dx.doi.org/10.1049/iet-cvi.2013.0031.

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Li, Xuesong, Jianguo Liu, Guang Chen, and Heng Fu. "Efficient methods using slanted support windows for slanted surfaces." IET Computer Vision 10, no. 5 (2016): 384–91. http://dx.doi.org/10.1049/iet-cvi.2015.0106.

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Passricha, Vishal, and Rajesh Kumar Aggarwal. "Convolutional support vector machines for speech recognition." International Journal of Speech Technology 22, no. 3 (2018): 601–9. http://dx.doi.org/10.1007/s10772-018-09584-4.

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Xue, Ming, and Hongtao Chen. "A Football Shot Action Recognition Method Based on Deep Learning Algorithm." Scientific Programming 2022 (March 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/9330798.

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Football is regarded as the world’s number one sport and is loved by all countries, and large-scale football matches are held basically every year. The key to football matches is to shoot goals, and how to improve the accuracy of football shooting requires the identification and analysis of football shooting actions. Deep learning enables machines to imitate human activities such as seeing, hearing, and thinking. It solves many complex pattern recognition problems. Especially, the deep learning algorithm is unique in the recognition of pictures with high accuracy, and it provides technical support for the recognition and analysis of football shooting actions. What this paper will discuss is the recognition method of football shooting action based on a deep learning algorithm. Experiments show that the football shooting action recognition method developed in this paper has a great effect on promoting the accuracy of football shooting, which can make the accuracy rate reach about 96%. The research in this paper has great reference value and practical significance for the team’s ability to shoot and grasp the opportunity to score.

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Yang, Liming, and Hongwei Dong. "Support vector machine with truncated pinball loss and its application in pattern recognition." Chemometrics and Intelligent Laboratory Systems 177 (June 2018): 89–99. http://dx.doi.org/10.1016/j.chemolab.2018.04.003.

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Qi, Yali, and Guoshan Zhang. "Strategy of active learning support vector machine for image retrieval." IET Computer Vision 10, no. 1 (2016): 87–94. http://dx.doi.org/10.1049/iet-cvi.2015.0101.

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WALAVALKAR, L., M. YEASIN, A. NARASIMHAMURTHY, and R. SHARMA. "SUPPORT VECTOR LEARNING FOR GENDER CLASSIFICATION USING AUDIO AND VISUAL CUES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 417–39. http://dx.doi.org/10.1142/s0218001403002447.

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Computer vision systems for monitoring people and collecting valuable demographic information in a social environment is an important research problem. It is expected that such a system will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system can provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates gender classification using Support Vector Machines (SVMs). The visual (thumbnail frontal face) and the audio (features from speech data) cues were considered for designing the classifier. Three different representations of the data, namely, raw data, principle component analysis (PCA) and non-negative matrix factorization (NMF) were used for the experimentation with visual signal. For speech, mel-cepstral coefficient and pitch were used for the experimentation. It was found that the best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively, on data set collected in laboratory environment. The performance of the SVM was compared with two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. To further understand the robustness issues, the proposed approach has been applied on a large balanced (roughly equal distribution of gender, ethnicity and age group) data-base consisting of 8000 faces collected in real world environment. While, the results are very promising it indicates more to be done to make a statistically meaningful conclusion.

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Jordan, Claudius V., Franziska Maurer, Sven Lowenberg, and Julien Provost. "Framework for Flexible, Adaptive Support of Test Management by Means of Software Agents." IEEE Robotics and Automation Letters 4, no. 3 (2019): 2754–61. http://dx.doi.org/10.1109/lra.2019.2918486.

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FUGATE, MIKE, and JAMES R. GATTIKER. "COMPUTER INTRUSION DETECTION WITH CLASSIFICATION AND ANOMALY DETECTION, USING SVMs." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 441–58. http://dx.doi.org/10.1142/s0218001403002459.

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This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks. SVM methods are seen as competitive with benchmark methods and other studies, and are used as a standard for the anomaly detection investigation. The anomaly detection approaches compare one class SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA/KDD-99 publicly available dataset of features from network packets, classified into nonattack and four-attack categories.

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Libertin, Claudia R., Prakash Kempaiah, Ravindra Durvasula, and Ariel Rivas. "364. Individualized Prognostics in COVID-19 Facilitated by Computer Recognition of Blood Leukocyte Subsets." Open Forum Infectious Diseases 8, Supplement_1 (2021): S284—S285. http://dx.doi.org/10.1093/ofid/ofab466.565.

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Abstract Background To determine whether CBC differentials of COVID+ inpatients can predict, at admission, both maximum oxygen requirements (MOR) and 30-day mortality. Methods Based on an approved IRB protocol, CBC differentials from the first 3 days of hospitalization of 12 SARS CoV-2 infected patients were retrospectively extracted from hospital records and analyzed with a privately owned Pattern Recognition Software (PRS, US Patent 10,429,389 B2) previously validated in sepsis, HIV, and hantavirus infections. PRS partitions the data into subsets immunologically dissimilar from one another, although internally similar. Results Regardless of the angle considered, the classic analysis −which measured the percentages of lymphocytes, monocytes, and neutrophils− did not distinguish outcomes (A). In contrast, non-overlapping patterns generated by the PRS differentiated 3 (left, vertical, and right) groups of patients (B). One subset was only composed of survivors (B). The remaining subsets included the highest oxygenation requirements (B). At least two immunologically interpretable, multi-cellular indicators distinguished the 3 data subsets with statistically significant differences (C, p≤ 0.05). Survivors (the left subset) showed lower N/L and/or higher M/L ratios than non-survivors (the vertical subset, C).Therefore, PRS partitioned the data into subsets that displayed both biological and significant differences. Because it offers visually explicit information, clinicians do not require a specialized training to interpret PRS-generated results. CBCs vs. outcomes - Software-analyzed CBCs vs. outcomes Conclusion (1) Analysis of blood leukocyte data predicts MOR and 30-d mortality. (2) Real time PRS analysis facilitates personalized medical decisions. (3) PRS measures two dimensions rarely assessed: multi-cellularity and dynamics. (4) Even with very small datasets, PRS may achieve statistical significance. (5) Larger COVID+ infected cohort is being analyzed for potential commercialization. Disclosures Claudia R. Libertin, MD, Gilead (Grant/Research Support)

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Yoon, K. J. "Stereo matching based on nonlinear diffusion with disparity-dependent support weights." IET Computer Vision 6, no. 4 (2012): 306. http://dx.doi.org/10.1049/iet-cvi.2011.0231.

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Zyda, Michael J., David R. Pratt, John S. Falby, Chuck Lombardo, and Kristen M. Kelleher. "The Software Required for the Computer Generation of Virtual Environments." Presence: Teleoperators and Virtual Environments 2, no. 2 (1993): 130–40. http://dx.doi.org/10.1162/pres.1993.2.2.130.

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The first phase of virtual world development has focused on the novel hardware (3-D input and 3-D output) and the graphics demo. The second phase of virtual worlds development will be to focus in on the more significant part of the problem, the software bed underlying “real” applications. The focus of this paper is on the software required to support large scale, networked, multiparty virtual environments. We discuss navigation (virtual camera view point control and its coupling to real-time, hidden surface elimination), interaction (software for constructing a dialogue from the inputs read from our devices and for applying that dialogue to display changes), communication (software for passing changes in the world model to other players on the network, and software for allowing the entry of previously undescribed players into the system), autonomy (software for playing autonomous agents in our virtual world against interactive players), scripting (software for recording, playing back, and multitracking previous play against live or autonomous players, with autonomy provided for departures from the recorded script), and hypermedia integration (software for integrating hypermedia data—audio, compressed video, with embedded links—into our geometrically described virtual world). All of this software serves as the base for the fully detailed, fully interactive, seamless environment of the third phase of virtual world development. We discuss the development of such software by describing how a real system, the NPSNET virtual world, is being constructed.

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Chuang, Tien-Yow, Chih-Hung Chen, Hwa-Ann Chang, Hui-Chen Lee, Cheng-Lian Chou, and Ji-Liang Doong. "Virtual Reality Serves as a Support Technology in Cardiopulmonary Exercise Testing." Presence: Teleoperators and Virtual Environments 12, no. 3 (2003): 326–31. http://dx.doi.org/10.1162/105474603765879567.

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The purpose of this study was to develop a virtual cycling system and examine the influence of virtual reality (VR) on test performance during clinical exercise testing. We aimed to compare the physiological responses of the cardiovascular and ventilatory systems during incremental exercise testing with or without VR, and to measure VR effects on the ratings of perceived exertion (RPE) and cycling duration throughout the test. Twelve healthy senior citizens (ten men and two women) with a mean age of 74.5-4.7 years participated in the study. The codes of behavior for this study included a maximum graded exercise tolerance test, a submaximal endurance VR exercise, and a submaximal endurance non-VR exercise. A friction-braked cycle ergometer was used to conduct the exercise tests. For the subject's movement speed to create an appropriate environment flow on the display screen, the bike was connected to a personal computer. The cardiorespiratory and hemodynamic parameters were evaluated at both peak and submaximal exertion. The results show that the VR versus non-VR programs did not differ on submaximal and peak exercise responses during the cycling test. However, significant differences were observed between the mean values for cycling duration, distance, and energy consumption. The difference between RPE curves for VR and non-VR protocols revealed promising results within 45 min. of cycling (Breslow test, p = .06); however, no statistical significance was achieved at the test termination (log rank test, p =.17). In conclusion, this study found that the maintenance of endurance, the increase in target intensity, and total energy consumption in exercise programs may be assisted by introducing VR technology. In addition, the activities taking place in virtual environments can be performed in complete safety.

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Déniz, O., M. Castrillón, and M. Hernández. "Face recognition using independent component analysis and support vector machines." Pattern Recognition Letters 24, no. 13 (2003): 2153–57. http://dx.doi.org/10.1016/s0167-8655(03)00081-3.

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Lee, Sang-Woong, Jooyoung Park, and Seong-Whan Lee. "Low resolution face recognition based on support vector data description." Pattern Recognition 39, no. 9 (2006): 1809–12. http://dx.doi.org/10.1016/j.patcog.2006.04.033.

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Qiang, Yi, Matthias Delafontaine, Mathias Versichele, Philippe De Maeyer, and Nico Van de Weghe. "Interactive analysis of time intervals in a two-dimensional space." Information Visualization 11, no. 4 (2012): 255–72. http://dx.doi.org/10.1177/1473871612436775.

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Time intervals are conventionally represented as linear segments in a one-dimensional space. An alternative representation of time intervals is the triangular model (TM), which represents time intervals as points in a two-dimensional space. In this paper, the use of TM in visualising and analysing time intervals is investigated. Not only does this model offer a compact visualisation of the distribution of intervals, it also supports an innovative temporal query mechanism that relies on geometries in the two-dimensional space. This query mechanism has the potential to simplify queries that are difficult to specify using traditional linear temporal query devices. Moreover, a software prototype that implements TM in a geographical information system (GIS) is introduced. This prototype has been applied in a real scenario to analyse time intervals that were detected by a Bluetooth tracking system. This application shows that TM has the potential to support a traditional GIS to analyse interval-based geographical data.

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De Oliveira Martins, Leonardo, Geraldo Braz Junior, Aristófanes Correa Silva, Anselmo Cardoso de Paiva, and Marcelo Gattass. "Detection of Masses in Digital Mammograms using K-Means and Support Vector Machine." ELCVIA Electronic Letters on Computer Vision and Image Analysis 8, no. 2 (2009): 39. http://dx.doi.org/10.5565/rev/elcvia.216.

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Zhu, Wenqiu, Haixing Bao, Zhigao Zeng, Zhiqiang Wen, Yanhui Zhu, and Huazheng Xiang. "Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (2019): 1956010. http://dx.doi.org/10.1142/s021800141956010x.

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Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

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SHIH, FRANK Y., KAI ZHANG, and YAN-YU FU. "A HYBRID TWO-PHASE ALGORITHM FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 08 (2004): 1423–35. http://dx.doi.org/10.1142/s0218001404003782.

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Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.

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Zou, Ying, Dahu Wang, and Leian Liu. "Research on Human Movement Target Recognition Algorithm in Complex Traffic Environment." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (2019): 2050012. http://dx.doi.org/10.1142/s0218001420500123.

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With the increase in the total population of the society and the continuous increase in the number of trips, the traffic pressures faced by people are increasing. With the development and advancement of computer technology, the emergence of intelligent transportation provides a better way to solve the problem of effectively alleviating traffic pressure and reducing the incidence of traffic accidents. In recent years, intelligent traffic monitoring system, as one of the important branches in the field of intelligent transportation, has also received more and more attention. Among them, video-based moving target recognition technology involves theoretical knowledge in various fields such as artificial intelligence, image processing, pattern recognition and computer vision. It is an important means to realize “safe city” and “smart city” and a key technology for intelligent monitoring. Therefore, the research on human motion target recognition algorithm in complex traffic environment has important theoretical and practical value. In the field of intelligent traffic monitoring, the moving target detection and recognition effect of video images will have certain influence on the classification and behavior understanding of subsequent moving targets. In this paper, the commonly used moving target detection methods are studied first, and the convergence problem of the traditional Adaboost algorithm is improved. An Adaboost algorithm based on adaptive weight update is proposed, and then the support vector machine (SVM) is used. The algorithm identifies the detected moving target. Finally, through simulation experiments on the acquired video images, the results show that the proposed human motion target recognition algorithm based on adaptive weight update Adaboost and SVM has good feasibility and rationality.

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Lee, Gangin, Unil Yun, Heungmo Ryang, and Donggyu Kim. "Approximate Maximal Frequent Pattern Mining with Weight Conditions and Error Tolerance." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (2016): 1650012. http://dx.doi.org/10.1142/s0218001416500129.

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Since the concept of frequent pattern mining was proposed, there have been many efforts to obtain useful pattern information from large databases. As one of them, applying weight conditions allows us to mine weighted frequent patterns considering unique importance of each item composing databases, and the result of analysis for the patterns provides more useful information than that of considering only frequency or support information. However, although this approach gives us more meaningful pattern information, the number of patterns found from large databases is extremely large in general; therefore, analyzing all of them may become inefficient and hard work. Thus, it is essential to apply a method that can selectively extract representative patterns from the enormous ones. Moreover, in the real-world applications, unexpected errors such as noise may occur, which can have a negative effect on the values of databases. Although the changes by the error are quite small, the characteristics of generated patterns can be turned definitely. For this reason, we propose a novel algorithm that can solve the above problems, called AWMax (an algorithm for mining Approximate weighted maximal frequent patterns (AWMFPs) considering error tolerance). Through the algorithm, we can obtain useful AWMFPs regardless of noise because of the consideration of error tolerance. Comprehensive performance experiments present that the proposed algorithm has more outstanding performance than previous state-of-the-art ones.

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Wang, Zhe, and Songcan Chen. "Matrix-pattern-oriented least squares support vector classifier with AdaBoost." Pattern Recognition Letters 29, no. 6 (2008): 745–53. http://dx.doi.org/10.1016/j.patrec.2007.12.005.

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KANG, SEONGHOON, HYERAN BYUN, and SEONG-WHAN LEE. "REAL-TIME PEDESTRIAN DETECTION USING SUPPORT VECTOR MACHINES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 405–16. http://dx.doi.org/10.1142/s0218001403002435.

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In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.

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KAFLE, V. P. "MoRaRo: Mobile Router-Assisted Route Optimization for Network Mobility (NEMO) Support." IEICE Transactions on Information and Systems E89-D, no. 1 (2006): 158–70. http://dx.doi.org/10.1093/ietisy/e89-d.1.158.

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Vu, L. G., Abeer Alsadoon, P. W. C. Prasad, and A. M. S. Rahma. "Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA)." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 01 (2017): 1756001. http://dx.doi.org/10.1142/s0218001417560018.

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Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have shown results when using a combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of a triangular algorithm in face normalization in order to enhance indexing accuracy. To evaluate the accuracy of the proposed hybrid indexing algorithm, the PCAaTA is tested and the results are compared with current solutions.

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Journal articles on the topic 'Computer-Assisted Pattern Recognition Software Support'

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