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Dissertations / Theses on the topic 'CNN'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Garbay, Thomas. "Zip-CNN." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS210.pdf.

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Les systèmes numériques utilisés pour l'Internet des Objets (IoT) et les Systèmes Embarqués ont connu une utilisation croissante ces dernières décennies. Les systèmes embarqués basés sur des microcontrôleurs (MCU) permettent de résoudre des problématiques variées, en récoltant de nombreuses données. Aujourd'hui, environ 250 milliards de MCU sont utilisés. Les projections d'utilisation de ces systèmes pour les années à venir annoncent une croissance très forte. L'intelligence artificielle a connu un regain d'intérêt dans les années 2012. L'utilisation de réseaux de neurones convolutifs (CNN) a

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Carpani, Valerio. "CNN-based video analytics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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The content of this thesis illustrates the six months work done during my internship at TKH Security Solutions - Siqura B.V. in Gouda, Netherlands. The aim of this thesis is to investigate on convolutional neural networks possible usage, from two different point of view: first we propose a novel algorithm for person re-identification, second we propose a deployment chain, for bringing research concepts to product ready solutions. In existing works, the person re-identification task is assumed to be independent of the person detection task. In this thesis instead, we consider the two ta

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Lara, Teodoro. "Controllability and applications of CNN." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/28921.

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Samal, Kruttidipta. "FPGA acceleration of CNN training." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54467.

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This thesis presents the results of an architectural study on the design of FPGA- based architectures for convolutional neural networks (CNNs). We have analyzed the memory access patterns of a Convolutional Neural Network (one of the biggest networks in the family of deep learning algorithms) by creating a trace of a well-known CNN architecture and by developing a trace-driven DRAM simulator. The simulator uses the traces to analyze the effect that different storage patterns and dissonance in speed between memory and processing element, can have on the CNN system. This insight is then used cre

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Mohamed, Moussa Elmokhtar. "Conversion d’écriture hors-ligne en écriture en-ligne et réseaux de neurones profonds." Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU4001.

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Cette thèse se focalise sur la conversion d’images statiques d’écriture hors- ligne en signaux temporels d’écriture en-ligne. L’objectif est d’étendre l’approche à réseau de neurone au-delà des images de lettres isolées ainsi que de les généraliser à d’autres types de contenus plus complexes. La thèse explore deux approches neuronales distinctes, la première approche est un réseau de neurones convolutif entièrement convolutif multitâche UNet basé sur la méthode de [ZYT18]. Cette approche a démontré des bons résultats de squelettisation mais en revanche une extraction de trait problé- matique.

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Rossetto, Andrea. "CNN per view synthesis da mappe depth." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16570/.

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Breve introduzione alle reti neurali e al deep learning con descrizione dei sistemi utilizzati per i modelli e i test effettuati. Spiegazione del funzionamento dei sistemi creati ed esposizione dei risultati ottenuti.

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Castelli, Filippo Maria. "3D CNN methods in biomedical image segmentation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18796/.

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A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedic

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Ringenson, Josefin. "Efficiency of CNN on Heterogeneous Processing Devices." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-155034.

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In the development of advanced driver assistance systems, computer vision problemsneed to be optimized to run efficiently on embedded platforms. Convolutional neural network(CNN) accelerators have proven to be very efficient for embedded camera platforms,such as the ones used for automotive vision systems. Therefore, the focus of this thesisis to evaluate the efficiency of a CNN on a future embedded heterogeneous processingdevice. The memory size in an embedded system is often very limited, and it is necessary todivide the input into multiple tiles. In addition, there are power and speed const

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Kristin, Hallberg. "Islam, BBC och CNN : Palestinska inbördeskriget 2006-2007." Thesis, Uppsala universitet, Teologiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295888.

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The topic of this paper is how CNN and BBC, two of the largest media companies in the world, presented Islam in the Palestinian civil war during the years 2006-2007. Articles that CNN and BBC published on the Palestinian civil war have been analyzed in order to answer this question. The purpose is to see if Islam is portrayed in an Islamophobic way by CNN and BBC and if it is possible to find discursive tracks from Clash of Civilizations-theory in the analyzed articles. The findings indicate that there are elements of Islamophobia and discursive tracks of Clash of Civilizations when it comes t

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Eklund, Anton. "Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415371.

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Parsing floorplans have been a problem in automatic document analysis for long and have up until recent years been approached with algorithmic methods. With the rise of convolutional neural networks (CNN), this problem too has seen an upswing in performance. In this thesis the task is to recover, as accurately as possible, spatial and geometric information from floorplans. This project builds around instance segmentation models like Cascade Mask R-CNN to extract the bulk of information from a floorplan image. To complement the segmentation, a new style of using keypoint-CNN is presented to fin

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Gu, Dongfeng. "3D Densely Connected Convolutional Network for the Recognition of Human Shopping Actions." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36739.

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In recent years, deep convolutional neural networks (CNNs) have shown remarkable results in the image domain. However, most of the neural networks in action recognition do not have very deep layer compared with the CNN in the image domain. This thesis presents a 3D Densely Connected Convolutional Network (3D-DenseNet) for action recognition that can have more than 100 layers without exhibiting performance degradation or overfitting. Our network expands Densely Connected Convolutional Networks (DenseNet) [32] to 3D-DenseNet by adding the temporal dimension to all internal convolution and poolin

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Chen, Tairui. "Going Deeper with Convolutional Neural Network for Intelligent Transportation." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/144.

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Over last several decades, computer vision researchers have been devoted to find good feature to solve different tasks, object recognition, object detection, object segmentation, activity recognition and so forth. Ideal features transform raw pixel intensity values to a representation in which these computer vision problems are easier to solve. Recently, deep feature from covolutional neural network(CNN) have attracted many researchers to solve many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective functi

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Mukhtar, Hind. "Machine Learning Enabled-Localization in 5G and LTE Using Image Classification and Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42449.

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Demand for localization has been growing due to the increase in location-based services and high bandwidth applications requiring precise localization of users to improve resource management and beam forming. Outdoor localization has been traditionally done through Global Positioning System (GPS), however it’s performance degrades in urban settings due to obstruction and multi-path effects, creating the need for better localization techniques. This thesis proposes a technique using a cascaded approach composed of image classification and deep learning using LIDAR or satellit

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Hossain, Md Tahmid. "Towards robust convolutional neural networks in challenging environments." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181882.

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Image classification is one of the fundamental tasks in the field of computer vision. Although Artificial Neural Network (ANN) showed a lot of promise in this field, the lack of efficient computer hardware subdued its potential to a great extent. In the early 2000s, advances in hardware coupled with better network design saw the dramatic rise of Convolutional Neural Network (CNN). Deep CNNs pushed the State-of-The-Art (SOTA) in a number of vision tasks, including image classification, object detection, and segmentation. Presently, CNNs dominate these tasks. Although CNNs exhibit impressive cla

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Bark, Filip. "Embedded Implementation of Lane Keeping Functionality Using CNN." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230193.

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The interest in autonomous vehicles has recently increased and as a consequence many companies and researchers have begun working on their own solutions to many of the issues that ensue when a car has to handle complicated decisions on its own. This project looks into the possibility of relegating as many decisions as possible to only one sensor and engine control unit (ECU) — in this work, by letting a Raspberry Pi with a camera attached control a vehicle following a road. To solve this problem, image processing, or more specifically, machine learning’s convolutional neural networks (CNN) are

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Fernandez, Brillet Lucas. "Réseaux de neurones CNN pour la vision embarquée." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM043.

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Pour obtenir des hauts taux de détection, les CNNs requièrent d'un grand nombre de paramètres à stocker, et en fonction de l'application, aussi un grand nombre d'opérations. Cela complique gravement le déploiement de ce type de solutions dans les systèmes embarqués. Ce manuscrit propose plusieurs solutions à ce problème en visant une coadaptation entre l'algorithme, l'application et le matériel.Dans ce manuscrit, les principaux leviers permettant de fixer la complexité computationnelle d'un détecteur d'objets basé sur les CNNs sont identifiés et étudies. Lorsqu'un CNN est employé pour détecter

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Lind, Johan. "Evaluating CNN-based models for unsupervised image denoising." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176092.

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Images are often corrupted by noise which reduces their visual quality and interferes with analysis. Convolutional Neural Networks (CNNs) have become a popular method for denoising images, but their training typically relies on access to thousands of pairs of noisy and clean versions of the same underlying picture. Unsupervised methods lack this requirement and can instead be trained purely using noisy images. This thesis evaluated two different unsupervised denoising algorithms: Noise2Self (N2S) and Parametric Probabilistic Noise2Void (PPN2V), both of which train an internal CNN to denoise im

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Söderström, Douglas. "Comparing pre-trained CNN models on agricultural machines." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185333.

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Li, Xile. "Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36707.

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This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face

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El-Shafei, Ahmed. "Time multiplexing of cellular neural networks." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365221.

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Кириченко, І. О. "Інтелектуальна технологія детектування стану трубопроводів з аугментацією даних в режимі екзамену". Master's thesis, Сумський державний університет, 2021. https://essuir.sumdu.edu.ua/handle/123456789/86859.

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Cпроектовано та розроблено класифікатор детектування стану трубопроводів. При цьому задача оцінки стану труб була розв’язана за допомогою підходу аугментації зображень, а сама технологія працює в режимі екзамену. Розроблений алгоритм реалізовано у формі програмного забезпечення, створеного за допомогою інструментального програмного середовища Python 3.0.

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Gustafsson, Magnus, and Niclas Hagel. "Al-Jazeera och CNN - En jämförande fallstudie i krigsjournalistik." Thesis, Halmstad University, School of Social and Health Sciences (HOS), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2234.

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<p>Författare: Magnus Gustafsson Niclas Hagel</p><p>Handledare: Thomas Knoll</p><p>Examinator: Martin Danielsson</p><p>Titel: Al-Jazeera och CNN - En jämförande fallstudie i krigsjournalistik</p><p>Typ av rapport: C - uppsats</p><p>Ämne: Medie- och Kommunikationsvetenskap</p><p>År: Höstterminen 2008</p><p>Sektion: Sektionen för Hälsa och Samhälle</p><p>Syfte: Vårt syfte är att studera och jämföra al-Jazeeras och CNN:s</p><p>bevakning av en händelse i Afghanistankonflikten för att kunna</p><p>redogöra för eventuella skillnader. Vi vill se hur olika faktorer</p><p>påverkar journalistiken. En ana

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Berg, Albin. "Jämförelse av CNN modeller för objektidentifiering och automatisk markering." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18637.

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En svårighet med att använda Artificiell Intelligens, är resurserna som krävs för att utföra beräkningarna under en acceptabel tidsram, men också med en bra träffsäkerhet. Målet med denna uppsats är att jämföra olika modeller av convolutional neural networks, mellan träffsäkerhet och hastighet, för att hitta den modell som är mest effektiv. Dessutom evalueras den mest effektiva modellen genom en webblösning, som kan markera bilder med text. Resultatet visar att varje modell har olika fördelar i hastighet och träffsäkerhet, men att VGG16 har nära till bäst resultat utan de problem som andra mod

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El, Ahmar Wassim. "Head and Shoulder Detection using CNN and RGBD Data." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39448.

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Alex Krizhevsky and his colleagues changed the world of machine vision and image processing in 2012 when their deep learning model, named Alexnet, won the Im- ageNet Large Scale Visual Recognition Challenge with more than 10.8% lower error rate than their closest competitor. Ever since, deep learning approaches have been an area of extensive research for the tasks of object detection, classification, pose esti- mation, etc...This thesis presents a comprehensive analysis of different deep learning models and architectures that have delivered state of the art performances in various machi

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Grogan, Andree Marie. "Observations on the news factory a case study of CNN /." restricted, 2005. http://etd.gsu.edu/theses/available/etd-11172005-173426/.

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Thesis (M.A.)--Georgia State University, 2005.<br>Title from title screen. Merrill Morris, committee chair; Marian Meyers, Douglas Barthlow, committee members. Electronic text (98 p.) : digital, PDF file. Description based on contents viewed June 21, 2007. Includes bibliographical references (p. 89-96).

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Grogan, Andree Marie. "Observations on the News Factory: A Case Study of CNN." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/communication_theses/6.

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News provides us with information about our world so we can make decisions about the matters that affect our daily lives—both for our personal and the public good. Television news is a pervasive force in our society, and it is important to study because of the influence it exerts on human action. But news is produced by human beings, and those human beings must make selections and rejections regarding what makes it into a newscast and what doesn’t. In addition, decisions have to be made on how to frame, present, order, word, edit, shape what news items are included. Many forces influence these

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Hiselius, Leo. "Igenkänning av musikalisk genre med CNN-nätverk och transfer learning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254764.

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Detta projekt studerar effekterna av transfer learning på inhämtandet av information från CNN-baserade ljuddatarepresentationer. Flera otränade CNN- nätverk matas med melspektrogrammatriser och tränas på tre olika uppgifter, nämligen ’genre’, ’region’ och ’year’ och klassifikationsprestandan mäts. Efter detta appliceras transfer learning och klassifikationsprestandan mäts igen. F1- score för individuella klasser inom de olika uppgifterna mäts också. Genom att jämföra resultaten visas att transfer learning är applicerbart på denna domän.<br>This project studies the effects of transfer learning

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Lee, Yi-Jou, and 李依柔. "A Reconfigurable CNN Accelerator Design." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/15122663000772368149.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>With the large size of the convolutional neural network (CNN), performance and energy efficiency of CNN accelerator become an important problem. From previous works, we can find that DRAM accesses took a large part in energy consumption. To reduce DRAM accesses, we observe the computation behavior of convolutional layer, and many parameters are shared between computation. Those data may be loaded on-chip repeatedly with the limitation of on-chip buffer size in an accelerator. We would like to capture data reuse via the on-chip buffer to reduce DRAM accesses o

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Lopez, Paola Denisse Gomez, and 鮑樂. "Face Keypoint Recognition with CNN." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/87259949052618555601.

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碩士<br>元智大學<br>通訊工程學系<br>104<br>Purpose This is an attempt to unravel the problem of human face keypoints recognition. In the new area of machine learning research called deep learning. Different approaches to this problem were evaluated and proposed one system to implement using python libraries for computational skills. Methodology Face keypoints detection was achieved by using a template algorithm. Using GPU instances and convolutional networks consisting of multiple levels. The key idea is to pre-train models in completely unsupervised way and finally they can be fine-tuned for the task at

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CHEN, CHUN-LIN, and 陳俊霖. "CNN-based identity recognition system." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/drtnp8.

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碩士<br>國立中央大學<br>資訊管理學系在職專班<br>107<br>This paper proposes a set of "CNN-based identity recognition system" for identity recognition using a computer vision library OpenCV and deep learning technology and webcam. It is expected to be applied to access control and regional security. Monitoring, advertising, or other related systems that need to be enhanced by confirming their identity. This thesis is based on Python and TensorFlow's built-in GoogLeNet CNN model. Supervised learning is used to obtain facial image features and classified by identity. This paper uses self-organizing face image data

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Chen, Zih-Jie, and 陳子傑. "CNN-based Gaze Block Estimation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3mzyzg.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>The visual is one of the most important senses that a human receives outside information. The visual helps us explore the world, receive new knowledge, and communicate with computer. As contactless human-computer interaction (HCI) model continues to develop, the technology of communicating with gaze behavior has become a highlight in this field. There have been many applications in the fields of education, advertising, nursing, entertainment or virtual reality. In general, most of the eye tracking devices need calibration in advance or fixing head. There are st

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Rebelo, José Soares. "CNN-Based Refinement for Image Segmentation." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/114115.

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LIAO, PEN-MIN, and 廖本閔. "Streamflow Forecasting by CNN-GRU Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8rs76r.

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碩士<br>逢甲大學<br>水利工程與資源保育學系<br>107<br>During the last two decades, the application of artificial intelligence in the field of flood forecasting has increased noticeably. Since the information of flood forecasting is the most important part of disaster management, also the emergency response and the mechanism of Recurrent Neural Network (RNN) include the behavior of the time series, this study attempt to adopt the Gated Recurrent Unit (GRU) which is a type of RNN used to develop a rainfall-runoff model for the mentioned purpose above. In this research RNN is using Gated Recurrent Unit (GRU). In e

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Rebelo, José Soares. "CNN-Based Refinement for Image Segmentation." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/114115.

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Chen, Shih-Che, and 陳釋澈. "Mandarin Tone Classification Using CNN/DNN." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6ptt3a.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>In Mandarin Chinese system, the tone plays an important role. Different tone patterns of the same syllable may result in different meanings. People whose native language aren’t Mandarin can be distinguished by their tone patterns. Therefore, we propose a method for tone classification. First, we convert the audio signal into the spectrogram. We treat the spectrogram as images, apply them as the image inputs for image recognition convolutional neural networks, and create tone classification models. We compare different image recognition models for tone classif

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Yang, Hsin-Wei, and 楊馨媁. "CNN-based Handwritten Invoice Recognition System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c5zem6.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>107<br>This paper proposes a method that uses deep learning and convolution neural network (CNN) for handwritten invoice recognition, this method can help enterprises solve that enterprises use only handwritten invoices and reduce labor costs of sorting invoices. Invoice recognition contains invoice number, buyer's government uniform invoice number, seller's government uniform invoice number, digital total amount, and Chinese total amount. Models train by different content, analyze and calculate the best results based on the labels, coordinates and scores of the m

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Shih, Yi-Hao, and 史鎰豪. "CNN-Based Distorted Barcode Number Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/bnzv68.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>107<br>The rapid development of deep learning in recent years saw breakthroughs after breakthroughs. AlphaGo’s victory against the world’s top-ranked professional GO player took only two years of learning. Then, Alpha Zero took only 21 days of self-learning to beat AlphaGo. We are now fully aware of the fast progress in deep learning, which uses Artificial Neural Network modeled upon the neuron transmission in the human brain to solve problems. This thesis uses a convolutional neural network Yolov3 to capture the feature of distorted barcode number images and made v

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Shen, Yu-Ru, and 沈渝茹. "Hands-on Image Recognition with CNN." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f7b527.

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碩士<br>元智大學<br>資訊管理學系<br>107<br>Human beings are visualizers. The amount of information received from the visuals accounts for about 60% of all our senses. In the process of developing artificial intelligence, we train that machines what see the world, understand the world and use images recognition as a source of data for making decision and judgment. Deep learning is the mainstream of artificial intelligence, which a class of machine learning algorithms that use multiple layers to progressively extract higher level features from raw input. Artificial Neural Networks (ANNs) were inspired by in

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Tsao, Po-Ho, and 曹博賀. "Boat License Number Recognition Using CNN." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gu58wv.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>106<br>This paper proposes the use of convolutional neural networks (CNNs) for real-time recognition of numbers on fishing vessels entering and leaving their ports. First, video cameras were mounted at the access of fishing ports to capture images of entries into and exits from these ports. Then, fishing vessels in the images were detected and positions of license plates on the vessels were located. After cutting and trimming, numbers on the fishing vessels were recognized. The recognized fishing vessel numbers then underwent rearrangement of their positions and tru

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Ming-Wei-Huang and 黃銘偉. "CNN-based gender and age classification." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/a7wytt.

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碩士<br>中原大學<br>資訊工程研究所<br>106<br>In this paper, we propose a method for classifying gender and age of pedestrian that can be applied to CCTV. With the development of science and technology, identifying the gender and age of pedestrians/face images becomes a popular and important task in social network and surveillance domain. We first perform face detection and extract facial landmarks from each image. Face alignment is then applied to gain aligned face images as training data. We use “GoogLeNet” which is one of the framework of Convolutional Neural Network (CNN) to train the models for gender

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GUPTA, RASHI. "IMAGE FORGERY DETECTION USING CNN MODEL." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19175.

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Image forgery detection has become more relevant in the real world in recent years since it is so easy to change a particular image and share it throughout social media, which may quickly lead to fake news and fake rumors all over the world. These editing softwares have posed a significant challenge to image forensics in terms of proposing and implementing various methods and strategies for detecting image counterfeiting. There have been a variety of traditional approaches for forgery detection, but they all focus on simple feature extraction and are more specialized to the type of

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Soldátová, Jana. "Hybridizace konceptu TV stanic na příkladu CNN Prima News." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448007.

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The diploma thesis deals with the origin and adaptation of the concept of CNN Prima NEWS on the Czech market and its possible hybridization. The expansion of global media corporations is a phenomenon of the 20th century that affects persisted till present. The television companies set up centers, branches or, through the sale of licenses, reach its audience through localized television stations. This thesis approaches the theory of globalization with a focus on the concepts of global culture, glocalization and hybridization. With standardization of successful patterns the companys strengthen t

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Hsiao, Chiao-Wei, and 蕭喬蔚. "A New CMOS Large-Neighborhood Cellular-Neural-Network (CNN) Cell Structure For Large-Neighborhood CNN Universal Machine (CNNUM)." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/17286284714479061976.

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碩士<br>國立交通大學<br>電子工程系<br>89<br>In this thesis, a new structure for the VLSI implementation of large- neighborhood cellular neural network (LN-CNN) is proposed and analyzed. In the proposed LN-CNN structure, the parasitic lateral bipolar junction transistor (BJT) in the CMOS process is used to implement both the neuron and synaptic path. Based on the basic device physics of the neuron-BJT (νBJT), a new compact neuron structure is proposed and analyzed. Besides, because of using NPN and PNP BJTs together, a low-power structure of synaptic path is designed and verified. The new low power is compo

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Wei, Cian-Pin, and 魏千評. "Signal Reconstruction-LMI, GA and CNN Approaches." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/07302740737170892869.

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碩士<br>國立高雄應用科技大學<br>電子與資訊工程研究所碩士班<br>94<br>In this thesis, first, the design of FIR and IIR equalizers for the communication channels via the genetic algorithm (GA) and linear matrix inequality (LMI) approaches from an H-inf perspective is presented, which the communication channels are considered as linear time-invariant and nonlinear time-invariant models, respectively. In general, the equalizer plays an important role in modern digital communication systems that can be used to recover the corrupted signal. For the linear time-invariant channel, the problem of IIR equalizer design can be tra

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Chou, Hung-Chun, and 周宏春. "Discriminatively-learned CNN Features for Image Retrieval." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/21952307307565825693.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>103<br>The thesis aims to learn discriminative features for image retrieval tasks based on using deep convolutional neural networks (CNN). Motivated by the great success of CNN in recognition tasks, one may be tempted to simply adopt the output of CNN for retrieval. However, CNN pre-trained model for classification tasks may not optimized for retrieval tasks. To address this issue, the CNN’s weight parameters are specifically adapted by a contrastive loss function to suit retrieval tasks. Extensive experiments conducted on typical retrieval datasets confirm the su

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黃園芳. "Two-Dimensional CNN with L-shaped Template." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/96492000877316767105.

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碩士<br>國立交通大學<br>應用數學系所<br>95<br>In this paper, we consider the simplest two-dimensional CNN template, L-shaped template. This work had investigated on [Lin&Yang, 2001] before. They use the building block to discuss the spatial entropy. In this paper, we reappraise the spatial entropy by pattern generation method which could refer to [Ban&Lin, 2005]. When we could not evaluate the spatial entropy, we use connecting operator referred to [Ban, Lin&Lin, 2006] to evaluate the lower bounded of spatial entropy. Finally, we compare the result with [Lin&Yang].

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Chen, Wei-Cheng, and 陳威成. "A Hrbrid Method for CNN Template Design." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/82335919178615802468.

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碩士<br>國立中正大學<br>電機工程研究所<br>90<br>In this study, a hybrid method for CNN (Cellar Neural Networks) template design is proposed. The objective is to efficiently find robust template for CNN with non-zero boundary consideration. In the proposed method, we analyzed the dynamic transient of the CNN and found the influence of non-zero boundary on the analytic method of CNN. This discovery can provide a limitation in the searching of robust template using GA (Genetic Algorithm). Incorporating the limitation in the procedure of GA can decrease the searching space and thus decrease the useles

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Hua, Chen Bo, and 陳柏樺. "Handwritten Character Recognition using GA-based CNN." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/69041942101896744315.

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碩士<br>國立高雄應用科技大學<br>電機工程系<br>99<br>In this paper, the static simulation method is used for handwritten characters recognition. Since a lot of noise and some non-character traces would occur while scanning image of writting characters, it evoked an inevitable noise-elimination problems in character recognition. This paper simulates the image preprocessing by adding several types of noise, and then filter it out by using conventional and gene-based CNN methods. The results demonstrate the superiority of CNN optimization method. In dealing with salt-pepper noise and Gaussian noise, CNN algorithm

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Lu, Pei-Hsuan, and 呂姵萱. "L1-Norm Based Adversarial Example against CNN." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ua49z8.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>106<br>In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data reformer is by far one of the strongest defenses in the black-box setting, where the attacker aims to craft transferable adversarial examples from an undefended DNN model to bypass a defense module without knowing its existence. MagNet can succes

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CHANG, CHANG, and 張競. "Fast Gender Detection System based on CNN." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jwvbz6.

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碩士<br>輔仁大學<br>資訊工程學系碩士班<br>107<br>Artificial Intelligence(AI), Machine Learning, Deep Learning, have always been very popular topic. As the time moved forward, there are more and more open source tools appeared e.g. OpenAI, TensorFlow, char-RNN that people can get the hang of Artificial Intelligence and Machine Learning more easily and more quickly. As the technology arising, Artificial Intelligence can apply to many fields such as simple AI can apply to refrigerator, sweeper, air conditioner and so on. They can detect external signal e.g. humidity, temperature, brightness, image, horizontal,

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