Relevant bibliographies by topics / Computer-Assisted Pattern Recognition Software Support

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Computer-Assisted Pattern Recognition Software Support'

Author: Grafiati
Published: 3 June 2025
Last updated: 7 July 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Computer-Assisted Pattern Recognition Software Support"

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Arthur, Gerald L. Gong Yang. "Implementation of a fuzzy rule-based decision support system for the immunohistochemical diagnosis of small B-cell lymphomas." Diss., Columbia, Mo. : University of Missouri-Columbia, 2009. http://hdl.handle.net/10355/6569.

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Thesis (M.S.)--University of Missouri-Columbia, 2009.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Thesis advisor: Yang Gong. "May 2009" Includes bibliographical references.
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Romero, Gómez Verónica. "Multimodal Interactive Transcription of Handwritten Text Images." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8541.

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En esta tesis se presenta un nuevo marco interactivo y multimodal para la transcripción de Documentos manuscritos. Esta aproximación, lejos de proporcionar la transcripción completa pretende asistir al experto en la dura tarea de transcribir. Hasta la fecha, los sistemas de reconocimiento de texto manuscrito disponibles no proporcionan transcripciones aceptables por los usuarios y, generalmente, se requiere la intervención del humano para corregir las transcripciones obtenidas. Estos sistemas han demostrado ser realmente útiles en aplicaciones restringidas y con vocabularios limitados (como es el caso del reconocimiento de direcciones postales o de cantidades numéricas en cheques bancarios), consiguiendo en este tipo de tareas resultados aceptables. Sin embargo, cuando se trabaja con documentos manuscritos sin ningún tipo de restricción (como documentos manuscritos antiguos o texto espontáneo), la tecnología actual solo consigue resultados inaceptables. El escenario interactivo estudiado en esta tesis permite una solución más efectiva. En este escenario, el sistema de reconocimiento y el usuario cooperan para generar la transcripción final de la imagen de texto. El sistema utiliza la imagen de texto y una parte de la transcripción previamente validada (prefijo) para proponer una posible continuación. Despues, el usuario encuentra y corrige el siguente error producido por el sistema, generando así un nuevo prefijo mas largo. Este nuevo prefijo, es utilizado por el sistema para sugerir una nueva hipótesis. La tecnología utilizada se basa en modelos ocultos de Markov y n-gramas. Estos modelos son utilizados aquí de la misma manera que en el reconocimiento automático del habla. Algunas modificaciones en la definición convencional de los n-gramas han sido necesarias para tener en cuenta la retroalimentación del usuario en este sistema.<br>Romero Gómez, V. (2010). Multimodal Interactive Transcription of Handwritten Text Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8541<br>Palancia
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(8771429), Ashley S. Dale. "3D OBJECT DETECTION USING VIRTUAL ENVIRONMENT ASSISTED DEEP NETWORK TRAINING." Thesis, 2021.

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<div> <div> <div> <p>An RGBZ synthetic dataset consisting of five object classes in a variety of virtual environments and orientations was combined with a small sample of real-world image data and used to train the Mask R-CNN (MR-CNN) architecture in a variety of configurations. When the MR-CNN architecture was initialized with MS COCO weights and the heads were trained with a mix of synthetic data and real world data, F1 scores improved in four of the five classes: The average maximum F1-score of all classes and all epochs for the networks trained with synthetic data is F1∗ = 0.91, compared to F1 = 0.89 for the networks trained exclusively with real data, and the standard deviation of the maximum mean F1-score for synthetically trained networks is σ∗ <sub>F1 </sub>= 0.015, compared to σF 1 = 0.020 for the networks trained exclusively with real data. Various backgrounds in synthetic data were shown to have negligible impact on F1 scores, opening the door to abstract backgrounds and minimizing the need for intensive synthetic data fabrication. When the MR-CNN architecture was initialized with MS COCO weights and depth data was included in the training data, the net- work was shown to rely heavily on the initial convolutional input to feed features into the network, the image depth channel was shown to influence mask generation, and the image color channels were shown to influence object classification. A set of latent variables for a subset of the synthetic datatset was generated with a Variational Autoencoder then analyzed using Principle Component Analysis and Uniform Manifold Projection and Approximation (UMAP). The UMAP analysis showed no meaningful distinction between real-world and synthetic data, and a small bias towards clustering based on image background. </p></div></div></div>
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Books on the topic "Computer-Assisted Pattern Recognition Software Support"

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Suzuki, Kenji. Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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IPCAI 2010 (2010 Geneva, Switzerland). Information processing in computer-assisted interventions: First international conference, IPCAI 2010, Geneva, Switzerland, June 23, 2010 : proceedings. Springer, 2010.

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MCBR-CDS 2009 (2009 London, England). Medical content-based retrieval for clinical decision support: First MICCAI international workshop, MCBR-CDS 2009, London, UK, September 20, 2009 : revised selected papers. Springer, 2010.

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Liu, Tianming. Multimodal Brain Image Analysis: First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Suzuki, Kenji, Fei Wang, and Dinggang Shen. Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, Proceedings. Springer, 2011.

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Suzuki, Kenji, Fei Wang, Daoqiang Zhang, Guorong Wu, Dinggang Shen, and Pingkun Yan. Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, ... Springer, 2013.

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Campilho, Aurélio, Fakhri Karray, and Farida Cheriet. Image Analysis and Recognition: 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017, Proceedings. Springer, 2017.

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Campilho, Aurélio, Fakhri Karray, and Farida Cheriet. Image Analysis and Recognition: 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5-7, 2017, Proceedings. Springer, 2017.

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Campilho, Aurélio, and Mohamed Kamel. Image Analysis and Recognition: 11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22-24, 2014, Proceedings, Part II. Springer International Publishing AG, 2014.

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Campilho, Aurélio, and Mohamed Kamel. Image Analysis and Recognition: International Conference ICIAR 2004, Porto, Portugal, September 29 - October 1, 2004, Proceedings, Part II. Springer London, Limited, 2004.

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Book chapters on the topic "Computer-Assisted Pattern Recognition Software Support"

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Dumeez, Joost, Maxime Bernaert, and Geert Poels. "Development of Software Tool Support for Enterprise Architecture in Small and Medium-Sized Enterprises." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-642-38490-5_7.

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Rakesh B S, Sujay K, and Anushree Raj. "SURVEY ON FACE DETECTION AND RECOGNITION ALGORITHMS USING DEEP LEARNING." In INFORMATION TECHNOLOGY & BIOINFORMATICS: INTERNATIONAL CONFERENCE ON ADVANCE IT, ENGINEERING AND MANAGEMENT - SACAIM-2022 (VOL 1). REDSHINE India, 2020. http://dx.doi.org/10.25215/8119070682.19.

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A facial recognition system uses a number of algorithms to recognize faces in digital photographs, identify people, and then confirm the authenticity of the acquired images by comparing them to facial images that have been saved in a database. Biometric technology is based on facial features of a person. Face detection and Recognition are major concerns in the area of biometric based systems and purposes. This process must ensure recognition accuracy and minimum processing time. Some cutting-edge techniques allow it to be retrieved more quickly in a single scan of the raw image and lie in a smaller dimensional space while effectively keeping face information. The techniques for face detection and recognition are classified on the bases of their target application. Also, the techniques are classified and analysed on the bases of their working domain as spatial, frequency, integrated and hardware support. Face detection is a challenging topic in computer vision because the human face is a dynamic object with a great degree of diversity in its appearance. There have been many different ways put forth, from straightforward edge-based algorithms to composite high-level systems leveraging cutting-edge pattern recognition techniques. With the help of biometrics, a facial recognition system can extract facial details from a picture or video. The data faceprint stored via facial traits is compared by the face recognition software using deep learning algorithms. Among them, face detection is a very potent tool for face recognition, image database management, human computer interface, and video surveillance. Face recognition is a rapidly developing technology that has been used extensively in forensics for purpose including criminal identification, airport security, and controlled access.
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Foran David J., Chen Wenjin, and Yang Lin. "Automated Image Interpretation and Computer-Assisted Diagnostics." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-234-9-77.

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Much of the difficulty in reaching consistent evaluations of radiology and pathology imaging studies arises from subjective impressions of individual observers. Developing strategies that can reliably transform complex visual observations into well-defined algorithmic procedures is an active area of exploration which can advance clinical practice, investigative research and outcome studies. The literature shows that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Advanced imaging and computational tools could potentially enable investigators to detect and track subtle changes in measurable parameters leading to the discovery of novel diagnostic and prognostic clues which are not apparent by human visual inspection alone. The overarching objective of this book chapter is to provide readers with a summary of the origin, evolution and future directions for the fields of automated image interpretation and computer-assisted diagnostics. The chapter begins with a high-level overview of the fields of image processing, pattern recognition, and computer vision followed by a description of how these disciplines relate to the more comprehensive fields of computer-assisted diagnostics and image guided decision support. Throughout the remainder of the chapter we have supplied multiple illustrative examples demonstrating how recent advances and innovations in each of these areas have impacted clinical and research activities throughout pathology and radiology including high-throughput tissue microarray analysis, multi-spectral imaging, and image co-registration.
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Vo, Le Thi Hong, Thien Tuan Hang, and Ayman Youssef Nassif. "Customer Satisfaction With a Named Entity Recognition (NER) Store-Based Management System Using Computer-Mediated Communication." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7034-3.ch013.

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With the rise of the popularity of e-commerce, it is evident that the service retail industries aim to reduce inventory and increase sales and profit margins. To achieve this, it is of paramount importance to establish excellent and effective interaction between customers and customer support. When a customer orders a product online, it is essential that the store demonstrates whether the products are in stock and the nearest stores to where the customers are. Currently, the needs of the customers are unlikely to be effectively met. Hence, the stores are unlikely to provide desirable products to customers even with high inventory. This paper investigates this issue at a typical and popular retail store in Vietnam. The authors present an investigation of this issue through two main stages. Corpus analysis for a set of collected text messages posted on the stores' websites for customer support was first carried out to explore the lexical patterns that indicate the customers' needs. This analysis revealed the frequency of customers' requests for the stores' locations where they can buy the goods and/or whether they are in stock. In the second stage of the investigation, the valuable findings from the corpus analysis were used for data extraction based on Named Entity Recognition (NER) software. The NER recognizes entities, including locations and names.
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Dutta, Dr Meghna. "ARTIFICIAL INTELLIGENCE IN MODERN MEDICINE." In Futuristic Trends in Medical Sciences Volume 3 Book 14. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfms14p1ch9.

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The foundation of evidence-based medicine is the derivation of associations and patterns from the pre-existing collection of data in order to produce clinical correlations and insights. In the past, we have relied on statistical techniques to identify these trends and connections. Using flowcharts and database approaches, computers can learn how to diagnose patients. Artificial intelligence (AI) is the phrase for the application of technology to replicate intelligent behavior and smart thought that is comparable to that of humans. The current focus of these applications involves software for analytical purposes, natural language processing, voice recognition and machine vision. Artificial intelligence (AI) is extending its reach into the realm of public health and is poised to exert a substantial influence across all aspects of healthcare. With the support of AI-enabled computer programs, healthcare professionals, including physicians, are now better prepared to identify patients requiring heightened attention and provide personalized treatment plans for each unique case. Practicing physicians can leverage AI to assist in note-taking, analyze patient interactions, and input essential data into Electronic Medical Record (EMR) systems. These software solutions will collect and analyze patient data, offering healthcare providers valuable insights into their patients' medical needs. In the forthcoming years, artificial intelligence (AI) is poised to become an essential element of the medical field. Consequently, it is imperative to instruct the upcoming generation of medical students in the concepts and practical applications of AI, enabling them to effectively collaborate with AI systems in a professional setting to enhance productivity. Simultaneously, it is equally important to nurture soft skills, such as empathy, among these students.
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Bose, Ranit Kumar. "ARTIFICIAL INTELLIGENCE AND INDIAN JUDICIAL SYSTEM: A CRITICAL ANALYSIS IN PERSPECTIVES OF SPEEDY TRIAL." In Disruptive Technologies and the Law: Navigating Legal Challenges in an Era of Innovation. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/nbennurdtch10.

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Apart from the need for an efficient and healthy court system for every democratic country, quick justice is an individual‘s basic right, particularly of the accused in the name of justice. It is a component of the fair and just legal process that guarantees the non arbitrariness of the court and the rationality of justice. Article 21 of the Indian Constitution ensures reasonable, fair, and due process of law and fast trial is an unmentionable but unquestionable aspect of this Article, that increases public confidence in the management of justice. Utilizing efficient adjudicatory systems with enhanced technology, the court is entitled to direct for increasing and strengthen the judicial system so that proper and swift justice shall be reasonably accessible. The digital revolution results in artificial intelligence (AI), an established or developing computer software to finish tasks free from human intellect implications. Its approach of detecting new patterns, auto-learning, auto adoptability, feeling, logical reasoning, problem understanding, and solving methods helps people to produce better products and services. Moreover, its fast and accurate recognition and solving mechanism encourages greater longand short-term output. Certainly, Artificial Legal Intelligence (ALI) is a useful tool that could improve legal research and services. An expert artificial legal assistant can offer better legal reason analysis, fast identification, case research, fact framework, drafting, judgment, and intelligent support to Judges, Legal Professionals, Advocates, Legal Researchers, and Scholars. Moreover, Justice delayed is justice denied‖ is one of the fundamental ideas of justice. The Preamble of the Indian Constitution expresses the objective, fair, and faster justice towards the people of India through the phrase JUSTICE, social, economic and political. Thus, one of the elements and methods to ensure justice which might be suppressed by the complicated, formal, and tedious process of the current legal system is Artificial Intelligence, sometimes known as Artificial Legal Intelligence
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Conference papers on the topic "Computer-Assisted Pattern Recognition Software Support"

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Zhu, Hong, Ian Bayley, Lijun Shan, and Richard Amphlett. "Tool Support for Design Pattern Recognition at Model Level." In 2009 33rd Annual IEEE International Computer Software and Applications Conference. IEEE, 2009. http://dx.doi.org/10.1109/compsac.2009.37.

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Cunha Filho, Carlos Alberto Hagge da, Hugo Abreu Mendes, Adriano Rodrigues de Paula, Ravi Barreto Doria Figueiredo, and Jessamine Maria de Lima Azevedo. "TVTAT - A Real Time Camera Imaging Testing Tool for Smart TVs: Preliminary Results." In Simpósio Brasileiro de Testes de Software Sistemático e Automatizado. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sast.2024.3692.

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Test automation tools that can accurately control a smart TV device are rare to be found. The difficulty of creating a system that is able to control such a device generates specific needs. In addition, the Brazilian digital television system supports DTVPlay, a middleware that provides the ability to broadcast interactive applications written in HTML5, NCL and Lua, that must be fully implemented on at least 90% of televisions manufactured in Brazil. Thus, there is a standard to be followed and a set of tests that need to be performed with each new middleware release. This work presents an automation tool called TV Test Automation Tool (TVTAT), that performs non-invasive tests on smart TVs, mainly but not restricted to DTVPlay tests. TVTAT uses real-time computer vision techniques such as optical character recognition, image pattern matching and color verification to assert that the middleware’s implementation is according to the published specification. The results of some test scenarios are presented, demonstrating that there are trends that can be found either in application performance situations or in tests of availability of transmitted applications that depend on DTVPlay.
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Azzalini, Loïc, Emmanuel Blazquez, Alexander Hadjiivanov, Gabriele Meoni, and Dario Izzo. "Generating a Synthetic Event-Based Vision Dataset for Navigation and Landing." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-202.

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Neuromorphic vision technology holds great promise for space applications owing to its low power consumption, high temporal precision and high dynamic range [1][2]. Dynamic vision sensors, or event-cameras as they are commonly known, have disrupted research fields across the computer vision landscape, from object tracking to image reconstruction, in robotics to the automotive industry [3]. A dynamic vision sensor is event-driven by design, as independently sensitive image pixels only respond to changes in scene brightness, leading to sparse and asynchronous output streams of data. Only motion between the scene and the camera will generate data, thus avoiding the capture of redundant static information and wasteful usage of onboard resources. Recent demonstrations of dynamic vision for in-orbit space situational awareness (SSA) [4][5] are encouraging, warranting further adoption of event-based hardware in the space sector. Whether tracking space debris or landing on a planetary body or asteroid, events are captured in the image plane of the dynamic vision sensor as a result of the relative dynamics between the onboard camera and the space environment. While event-based vision for guidance navigation and control (GNC) has been considered at a theoretical level in the past [1], onboard demonstrations of this technology have yet to be deployed. Its limited adoption to date can, in part, be explained by the lack of event-based datasets tailored to vision-based navigation in space and state-of-the-art event processing tools found in other disciplines (e.g., robotics). Our main contribution is a software pipeline to generate event-based datasets from simulated spacecraft trajectories in a photorealistic scene generator for planetary bodies and asteroids. Various landing scenarios are considered in this work to illustrate how the pipeline may be configured to capture event-based representation of surface features. We will base an upcoming scientific crowd-sourcing initiative [6] on this landing dataset to engage the wider computer vision communities in determining how best to handle event-based data for navigation and landing. We propose a flexible data pipeline which takes in trajectory specifications as input and outputs streams of events corresponding to the motion of features in the scene. From the initial conditions of the spacecraft and the properties of the target body, we solve an optimal control problem corresponding to a non-ventral, minimum-mass descent trajectory on the Moon and Mars. The trajectories are then used to manipulate the viewpoint of a pinhole camera model in the Planet and Asteroid Natural Scene Generator (PANGU) [7] which renders synthetic images of the surface during approach. Finally, a video-to-event converter [8] is used to generate synthetic events induced by the simulated landings. The sparse and asynchronous events include various artifacts (noise, motion blur, etc.) modelled after the performance of dynamic vision sensors in the field. The resulting dataset captures dynamic, event-based representations of common surface features such as craters, boulders and the target body's horizon. The upcoming data challenge will focus on the representation of event-based data, itself a new paradigm in computer vision, and its processing to meet navigation and landing objectives. The success of previous scientific crowd-sourcing initiatives on spacecraft pose estimation [9][10] testifies to the effectiveness of competitive data challenges in garnering interest and solutions to novel optical navigation opportunities. Beyond the data challenge, the event-based landing dataset is envisioned to support investigations into future onboard opportunities for event- and vision-based navigation, including optical-flow-based motion estimation, surface feature identification and tracking, and terrain relative navigation. By releasing this pipeline, we hope to promote the creation of new datasets for event-based navigation around other planetary bodies and asteroids, and to support the development of state-of-the-art event processing tools for future space missions. References: [1] Izzo, D., Hadjiivanov, A., Dold, D., Meoni, G., &amp; Blazquez, E. (2022). Neuromorphic computing and sensing in space. arXiv preprint arXiv:2212.05236. [2] Roffe, S., Akolkar, H., George, A. D., Linares-Barranco, B., &amp; Benosman, R. B. (2021). Neutron-induced, single-event effects on neuromorphic event-based vision sensor: A first step and tools to space applications. IEEE Access, 9 , 85748-85763. doi: 10.1109/ACCESS.2021.3085136 [3] Gallego, G., Delbrück, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., ... Scaramuzza, D. (2022). Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (1), 154-180. doi: 10.1109/TPAMI.2020.3008413 [4] Roffe, S., Schwarz, T., Cook, T., Perryman, N., Goodwill, J., Gretok, E., ... George, A. (2020). CASPR: Autonomous Sensor Processing Experiment for STP-H7. Retrieved from https://digitalcommons.usu.edu/smallsat/2020/all2020/64/ [5] McHarg, M. G., Balthazor, R. L., McReynolds, B. J., Howe, D. H., Maloney, C. J., O’Keefe, D., ... Cohen, G. (2022). Falcon Neuro: an event-based sensor on the International Space Station. Optical Engineering, 61 (8), 085105. Retrieved from https://doi.org/10.1117/1.OE.61.8.085105 doi: 10.1117/1.OE.61.8.085105 [6] Kelvins - European Space Agency's Advanced Concepts Competition Website, https://kelvins.esa.int [7] Martin, I., &amp; Dunstan, M. (2021, November 20). Pangu v6: Planet and asteroid natural scene generation utility. [8] Hu, Y., Liu, S. C., &amp; Delbruck, T. (2021). v2e: From video frames to realistic DVS events. In 2021 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW). IEEE. [9] Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Martens, M., &amp; D’Amico, S. (2020). Satellite pose estimation challenge: Dataset, competition design, and results. IEEE Transactions on Aerospace and Electronic Systems, 56 (5), 4083–4098. doi: 10.1109/taes.2020.2989063 [10] Park, T. H., Martens, M., Lecuyer, G., Izzo, D., &amp; D’Amico, S. (2022). SPEED+: Next-generation dataset for spacecraft pose estimation across domain gap. 2022 IEEE Aerospace Conference (AERO). doi:10.1109/aero53065.2022.9843439
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Kumar, Manish, Nakul Varma, Manjunath Rao, et al. "Enabling Autonomous Well Optimization by Applications of Edge Gateway Devices & Advanced Analytics." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216468-ms.

Full text
Abstract:
Abstract Data monitoring in a big oil field without any digital platform is a challenging task but it is critical for ALS monitoring and optimization. In sucker rod pumping wells, real time dyna card collection and analyzation is very important for understanding downhole pump behavior and system health. Manual dyna card collection twice a week for ~47 horizontal wells is very low. Real time dyna card requires atleast 256 data points per minute frequency. Its analysis is very effective to optimize well production and increase the pump life and rod run life. The lack of real time monitoring resulted in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning implementation shifted our approach from reactive to proactive which assisted in ALS Optimization and reduced production loss. The data is transmitted to Se Suite Central, a web based Descisison Support System hosted on cloud. Since thousands of dynacards are generated in a day, therefore the algorithm was made with automated card classification using computer driven pattern recognition techniques. The real time data is used for analysis including basic statistics and machine learning algorithms to classify thousands of dynacards every day. The pump signatures were identified using machine learning libraries and categorized. Several informative dashboards are developed which provided quick analysis of ALS performance, a few of them are - Well Operational Status Dyna cards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics After collection of enough data and creation of analytical dashboards using domain knowledge, the gained insights were used for ALS optimization. Smart Alarms were generated using statistics and machine learning settings by the system which gave alerts by e-mail if an abnormal behavior or erratic dyna-cards were identified. This helped in reduction of well downtime in some events which were treated instinctively before. The integration of domain knowledge and digitalization enabled to take informed and effective decisions. This project supported in effectively managing a complete asset of more than 47 wells remotely with limited resources. This set up has capably run low PI wells intermittently, which has saved power consumption to run the surface pumping units. This digitalization plan has prevented many pump &amp; rods failures, resulting in saving numerous workover jobs and well downtime.
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