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Dissertations / Theses on the topic 'Region based convolutional neural networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Stone, David L. "The Application of Index Based, Region Segmentation, and Deep Learning Approaches to Sensor Fusion for Vegetation Detection." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5708.

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This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning me

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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more

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Hebert, Joshua A. "Ballistocardiography-based Authentication using Convolutional Neural Networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1228.

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This work demonstrates the viability of the ballistocardiogram (BCG) signal derived from a head-worn device as a biometric modality for authentication. The BCG signal is the measure of an individual's body acceleration as a result of the heart's ejection of blood. It is a characterization of an individual's cardiac cycle and can be derived non-invasively from the measurement of subtle movements of a person's extremities. Through the use of accelerometer and gyroscope sensors on a Smart Eyewear (SEW) device, derived BCG signals are used to train a convolutional neural network (CNN) as an authen

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Nikzad, Dehaji Mohammad. "Structural Improvements of Convolutional Neural Networks." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410448.

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Over the last decade, deep learning has demonstrated outstanding performance in almost every application domain. Among different types of deep frameworks, convolutional neural networks (CNNs), inspired by the biological process of the visual system, can learn to extract discriminative features from raw inputs without any prior manipulation. However, efficient information circulation and the ability to explore effective new features are still two key and challenging factors for a successful deep neural network. In this thesis, we aim at presenting novel structural improvements of the CNN framew

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Song, Weilian. "Image-Based Roadway Assessment Using Convolutional Neural Networks." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/78.

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Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-

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Jonnarth, Arvi. "Camera-Based Friction Estimation with Deep Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355618.

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During recent years, great progress has been made within the field of deep learning, and more specifically, within neural networks. Deep convolutional neural networks (CNN) have been especially successful within image processing in tasks such as image classification and object detection. Car manufacturers, amongst other actors, are starting to realize the potential of deep learning and have begun applying it to autonomous driving. This is not a simple task, and many challenges still lie ahead. A sub-problem, that needs to be solved, is a way of automatically determining the road conditions, in

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Julin, Fredrik. "Vision based facial emotion detection using deep convolutional neural networks." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42622.

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Emotion detection, also known as Facial expression recognition, is the art of mapping an emotion to some sort of input data taken from a human. This is a powerful tool to extract valuable information from individuals which can be used as data for many different purposes, ranging from medical conditions such as depression to customer feedback. To be able to solve the problem of facial expression recognition, smaller subtasks are required and all of them together form the complete system to the problem. Breaking down the bigger task at hand, one can think of these smaller subtasks in the form of

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Khlif, Wafa. "Multi-lingual scene text detection based on convolutional neural networks." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS022.

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Cette thèse propose des approches de détection de texte par des techniques d'apprentissage profond pour explorer et récupérer des contenus faiblement structurés dans des images de scène naturelles. Ces travaux proposent, dans un premier temps, une méthode de détection de texte dans des images de scène naturelle basée sur une analyse multi-niveaux des composantes connexes (CC) et l'apprentissage des caractéristiques du texte par un réseau de neurones convolutionnel (CNN), suivie d'un regroupement des zones de texte détectées par une méthode à base de graphes. Les caractéristiques des composante

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Söderström, Albin. "Anomaly-based Intrusion Detection Using Convolutional Neural Networks for IoT Devices." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21870.

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Background. The rapid growth of IoT devices in homes put people at risk of cyberattacks and the low power and computing capabilities in IoT devices make it difficultto design a security solution for them. One method of preventing cyber attacks isan Intrusion Detection System (IDS) that can identify incoming attacks so that anappropriate action can be taken. Previous attempts have been made using machinelearning and deep learning however these attempts have struggled at detecting newattacks.Objectives. In this work we use a convolutional neural network IoTNet designed forIoT devices to classify

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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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Harris, William H. (William Hunt). "Machine learning transferable physics-based force fields using graph convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128979.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 22-24).<br>Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accur

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Zhang, Yehui. "Prediction of ICD-9 Code Assignment Using Attention-Based Convolutional Neural Networks." Thesis, University of Oregon, 2019. http://hdl.handle.net/1794/24547.

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In intensive care units, most patients are usually in critical conditions which require physicians to make immediate diagnosis and treatments. However, not every patient could get the best treatment because it highly related to the physician’s expertise. With the development of the machine learning, many studies have started trying to develop models that can learn the representations in Electronic Health Records (EHR) and make accurate predictions on clinical tasks. On code assignment tasks, models based on convolutional neural networks (CNN) or Recurrent Neural Networks (RNN) have shown promi

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Stewart, Seth Andrew. "Fully Convolutional Neural Networks for Pixel Classification in Historical Document Images." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7064.

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We use a Fully Convolutional Neural Network (FCNN) to classify pixels in historical document images, enabling the extraction of high-quality, pixel-precise and semantically consistent layers of masked content. We also analyze a dataset of hand-labeled historical form images of unprecedented detail and complexity. The semantic categories we consider in this new dataset include handwriting, machine-printed text, dotted and solid lines, and stamps. Segmentation of document images into distinct layers allows handwriting, machine print, and other content to be processed and recognized discriminativ

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Haraldsson, Truls. "Real-time Vision-based Fall Detection : with Motion History Images and Convolutional Neural Networks." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71137.

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Falls among the elderly is a major health concern worldwide due to theserious consequences, such as higher mortality and morbidity. And as theelderly are the fastest growing age group, an important challenge for soci-ety is to provide support in their every day life activities. Given the socialand economical advantages of having an automatic fall detection system,these systems have attracted the attention from the healthcare industry.With the emerging trend of Smart Homes and the increasing numberof cameras in our daily environments, this creates an excellent opportu-nity for vision-based fall

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Larsson, Olov. "A Reward-based Algorithm for Hyperparameter Optimization of Neural Networks." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78827.

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Machine learning and its wide range of applications is becoming increasingly prevalent in both academia and industry. This thesis will focus on the two machine learning methods convolutional neural networks and reinforcement learning. Convolutional neural networks has seen great success in various applications for both classification and regression problems in a diverse range of fields, e.g. vision for self-driving cars or facial recognition. These networks are built on a set of trainable weights optimized on data, and a set of hyperparameters set by the designer of the network which will rema

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Sällqvist, Jessica. "Real-time 3D Semantic Segmentation of Timber Loads with Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148862.

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Volume measurements of timber loads is done in conjunction with timber trade. When dealing with goods of major economic values such as these, it is important to achieve an impartial and fair assessment when determining price-based volumes. With the help of Saab’s missile targeting technology, CIND AB develops products for digital volume measurement of timber loads. Currently there is a system in operation that automatically reconstructs timber trucks in motion to create measurable images of them. Future iterations of the system is expected to fully automate the scaling by generating a volumetr

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Melendez, Melendez Roy Kelvin. "Sperm cell segmentation in digital micrographs based on convolutional neural networks using u-net architecture." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/19908.

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Human infertility is considered a serious disease of the the reproductive system that affects more than 10% of couples worldwide,and more than 30% of reported cases are related to men. The crucial step in evaluating male in fertility is a semen analysis, highly dependent on sperm morphology. However,this analysis is done at the laboratory manually and depends mainly on the doctor’s experience. Besides,it is laborious, and there is also a high degree of interlaboratory variability in the results. This article proposes applying a specialized convolutional neural network architecture (U-Net),whi

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Tang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.

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Dans cette thèse, nous nous intéressons au problème de la détection d’objets faiblement supervisée. Le but est de reconnaître et de localiser des objets dans les images, n’ayant à notre disposition durant la phase d’apprentissage que des images partiellement annotées au niveau des objets. Pour cela, nous avons proposé deux méthodes basées sur des modèles différents. Pour la première méthode, nous avons proposé une amélioration de l’approche ”Deformable Part-based Models” (DPM) faiblement supervisée, en insistant sur l’importance de la position et de la taille du filtre racine initial spécifiqu

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Singh, Vivek Kumar. "Segmentation and classification of multimodal medical images based on generative adversarial learning and convolutional neural networks." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668445.

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L’objectiu principal d’aquesta tesi és crear un sistema CAD avançat per a qualsevol tipus de modalitat d’imatge mèdica amb altes taxes de sensibilitat i especificitat basades en tècniques d’aprenentatge profund. Més concretament, volem millorar el mètode automàtic de detecció de les regions d’interès (ROI), que són àrees de la imatge que contenen possibles teixits malalts, així com la segmentació de les troballes (delimitació de la frontera) i, en definitiva, una predicció del diagnosi més adequat (classificació). En aquesta tesi ens centrem en diversos camps, que inclouen mamografies i

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Zhang, Xitong. "vU-net: edge detection in time-lapse fluorescence live cell images based on convolutional neural networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1224.

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Time-lapse fluorescence live cell imaging has been widely used to study various dynamic processes in cell biology. As the initial step of image analysis, it is important to localize and segment cell edges with higher accuracy. However, fluorescence live-cell images usually have issues such as low contrast, noises, uneven illumination in comparison to immunofluorescence images. Deep convolutional neural networks, which learn features directly from training images, have successfully been applied in natural image analysis problems. However, the limited amount of training samples prevents their ro

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Kvedaraite, Indre. "Sentiment Analysis of YouTube Public Videos based on their Comments." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105754.

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With the rise of social media and publicly available data, opinion mining is more accessible than ever. It is valuable for content creators, companies and advertisers to gain insights into what users think and feel. This work examines comments on YouTube videos, and builds a deep learning classifier to automatically determine their sentiment. Four Long Short-Term Memory-based models are trained and evaluated. Experiments are performed to determine which deep learning model performs with the best accuracy, recall, precision, F1 score and ROC curve on a labelled YouTube Comment dataset. The resu

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Papakis, Ioannis. "A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103372.

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This thesis presents a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the "contextual" information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of the proposed framework

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Tivive, Fok Hing Chi. "A new class of convolutional neural networks based on shunting inhibition with applications to visual pattern recognition." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061025.164437/index.html.

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Broyelle, Antoine. "Automated Pulmonary Nodule Detection on Computed Tomography Images with 3D Deep Convolutional Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231930.

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Object detection on natural images has become a single-stage end-to-end process thanks to recent breakthroughs on deep neural networks. By contrast, automated pulmonary nodule detection is usually a three steps method: lung segmentation, generation of nodule candidates and false positive reduction. This project tackles the nodule detection problem with a single stage modelusing a deep neural network. Pulmonary nodules have unique shapes and characteristics which are not present outside of the lungs. We expect the model to capture these characteristics and to only focus on elements inside the l

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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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Pfundstein, Maximilian. "Human Age Prediction Based on Real and Simulated RR Intervals using Temporal Convolutional Neural Networks and Gaussian Processes." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165987.

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Electrocardiography (ECG) is a non-invasive method used in medicine to track the electrical pulses sent by the heart. The time between two subsequent electrical impulses and hence the heartbeat of a subject, is referred to as an RR interval. Previous studies show that RR intervals can be used for identifying sleep patterns and cardiovascular diseases. Additional research indicates that RR intervals can be used to predict the cardiovascular age of a subject. This thesis investigates, if this assumption is true, based on two different datasets as well as simulated data based on Gaussian Processe

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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.

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Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis

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Paradza, Masimba Wellington. "Development of a neural network based model for predicting the occurrence of spread F within the Brazilian sector." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005245.

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Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity)

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Liu, Lanfa, Min Ji, and Manfred Buchroithner. "Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery." Multidisciplinary Digital Publishing Institute (MDPI), 2018. https://tud.qucosa.de/id/qucosa%3A32490.

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Soil spectra are often measured in the laboratory, and there is an increasing number of large-scale soil spectral libraries establishing across the world. However, calibration models developed from soil libraries are difficult to apply to spectral data acquired from the field or space. Transfer learning has the potential to bridge the gap and make the calibration model transferrable from one sensor to another. The objective of this study is to explore the potential of transfer learning for soil spectroscopy and its performance on soil clay content estimation using hyperspectral data. First, a

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Pirovano, Antoine. "Computer-aided diagnosis methods for cervical cancer screening on liquid-based Pap smears using convolutional neural networks : design, optimization and interpretability." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT011.

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Le cancer du col de l’utérus est le deuxième cancer le plus important pour les femmes après le cancer du sein. En 2012, le nombre de cas recensés dépasse 500,000 à travers le monde, dont la moitié se sont révélés mortels. Jusqu'à maintenant, le dépistage primaire du cancer du col de l’utérus est réalisé par l’inspection visuelle de cellules, prélevées par frottis vaginal, par des cytopathologistes utilisant la microscopie en fond clair dans des laboratoires de pathologie. En France, environ 5 millions de dépistage sont réalisés chaque année et environ 90% mènent à un diagnostic négatifs (i.e.

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"Identifying Critical Regions for Robot Planning Using Convolutional Neural Networks." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53626.

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abstract: In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP). In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based

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Kuo, Hsiao-Fu, and 郭曉芙. "Infarct Region Segmentation in Rat Brain MR Images after Stroke Based on Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vbc8s3.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>106<br>Stroke has the second highest fatality rate around the world. It can be divided into two major categories: ischemic and hemorrhagic. In the clinically, there is lots of development prospect. Rodents associated with magnetic resonance (MR) images are often preclinical experimental models. Researchers need to go through many processing steps before analyzing the operation, such as extracting the brain regions and infarct regions. However, these regions need lots of time for manual segmentation by experts, who may have the problems of different standards. T

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Islam, Md Mahmudul. "Region-based Convolutional Neural Network and Implementation Of The Network Through Zedboard Zynq." Thesis, 2019. http://hdl.handle.net/1805/18485.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>In autonomous driving, medical diagnosis, unmanned vehicles and many other new technologies, the neural network and computer vision has become extremely popular and influential. In particular, for classifying objects, convolutional neural networks (CNN) is very efficient and accurate. One version is the Region-based CNN (RCNN). This is our selected network design for a new implementation in an FPGA. This network identifies stop signs in an image. We successfully designed and trained an RCNN network in MATLAB and implemented it

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(6372773), MD MAHMUDUL ISLAM. "Region-based Convolutional Neural Network and Implementation of the Network Through Zedboard Zynq." Thesis, 2019.

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<div>In autonomous driving, medical diagnosis, unmanned vehicles and many other new technologies, the neural network and computer vision has become extremely popular and influential. In particular, for classifying objects, convolutional neural networks (CNN) is very efficient and accurate. One version is the Region-based CNN (RCNN). This is our selected network design for a new implementation in an FPGA.</div><div><br></div><div>This network identies stop signs in an image. We successfully designed and trained an RCNN network in MATLAB and implemented it in the hardware to use in an embedded r

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Jing-LuneYang and 楊景倫. "Automatic Orchid Bottle Seedling Image Feature Extraction and Measurement based on Deep Mask Regions Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/b4s94c.

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碩士<br>國立成功大學<br>電機工程學系<br>107<br>This thesis aims to develop an automatic orchid bottle seedling image feature extraction and measurement algorithms based on mask regions convolutional neural networks (Mask R-CNN) for extracting the important growth features of orchid bottle seedlings to reach the goal of precise cultivation. In this study, to train and test the Mask R-CNN, orchid bottle seedling images from different view angles were obtained from an orchid plantation factory in the southern Taiwan. The original images collected from the factory were first labeled for their outlook contours s

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Boyagoda, Ekanayaka Mudiyanse Ralahamilage Chamodi Lakmali. "Object detection for single tree species identification with high resolution aerial images." Master's thesis, 2020. http://hdl.handle.net/10362/93643.

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Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies<br>Object recognition is one of the computer vision tasks developing rapidly with the invention of Region-based Convolutional Neural Network (RCNN). This thesis contains a study conducted using RCNN base object detection technique to identify palm trees in three datasets having RGB images taken by Unnamed Aerial Vehicles (UAVs). The method was entirely implemented using TensorFlow object detection API to compare the performance of pre-trained faster RCNN object detect

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YOU, BIN-YEN, and 游秉諺. "Image Colorization Based on Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f5fm5v.

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碩士<br>國立臺北科技大學<br>電機工程系<br>107<br>Gray-scale image colorization can provide more visual information for humans. Many works of literature today use deep learning networks to realize full-automatic image colorization. However, machine-generated color images generally have the problems of dim color with low saturation and need large parameter size of deep learning networks. Therefore, this work proposes to improve the color saturation with HSV color space and add the concept of image pyramid to reduce the parameter amount by our two-stage convolutional neural network (CNN) to realize automatic co

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Jhe-WeiLi and 李哲緯. "Sign Language Recognition Based on Convolutional Neural Networks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/90081447272112881843.

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碩士<br>國立成功大學<br>資訊工程學系<br>104<br>In this paper, we propose a vision-based sign language recognition system using convolutional neural networks (CNN). We design an skin color modeling method for hand segmentation so that the hand region can be derived accurately even when different users use our system in various background scenes. A feature descriptor is extracted from the fully-connected layer of the fine-tuned CNN model as the representation of a hand gesture, and Support Vector Machine (SVM) is applied to classify hand gestures. Our recognition method is evaluated by two datasets: (1) The C

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Huang, Chi-Chou, and 黃啟州. "Convolutional Neural Networks Hardware Design Based on FPGA." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/wa7j25.

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碩士<br>國立中正大學<br>電機工程研究所<br>105<br>In recent years, Deep Learning has been widely applied to fields like computer vision、automatic speech recognition、audio recognition、image processing and bioinformatics. There are several deep learning architectures, such as the Deep Neural Network(DNN), the Convolutional Neural Networks(CNN), and the Recurrent Neural Networks (RNN), etc. Compared to others, the CNN has better results in the fileds of image and text recognition. However, due to the huge amount of data required during processing and the complexity of the network structure, it has been a challe

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WEI, YIN-JHIH, and 魏吟芝. "Illuminant Estimation based on Shallow Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/559apg.

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碩士<br>逢甲大學<br>通訊工程學系<br>106<br>White balance in imaging devices mimics the ability of color constancy in human vision. It assures an accurate color reproduction in images under a variety of illuminant conditions. The first step of white balance is to estimate the color of the illuminant. Then, the estimated illuminant color is applied to eliminate the color cast so that the corrected image appears as taken under the canonical illuminant. The learning based approach in color constancy shows much higher performance than that of the traditional statistical approach. Especially, a convolutional ne

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LI, PEI-YING, and 李培英. "Image Generation Methods Based on Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cag45t.

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碩士<br>國立高雄科技大學<br>電腦與通訊工程系<br>107<br>Image generation methods based on convolutional neural networks are studied, including style image generation, super-resolution image generation and fake image generation. In the style image generation, the advantages and disadvantages of style transfer and fast style transfer are analyzed. The VGG-19 network is used to extract the feature maps which have their implicit meaning as a learning basis, it can avoid unnecessary parameter adjustments. The superiority is not only for style transfer, but also for color transfer and even texture transfer tasks. In t

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Chen, Chien-Hung, and 陳建宏. "Stingray Detection and Recognition of Aerial Videos with Region-based Convolution Neural Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2zzp7v.

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碩士<br>國立中山大學<br>機械與機電工程學系研究所<br>106<br>In recent years, image processing technology has made a major breakthrough because of the appearance of deep learning. Nowadays, many problems that were difficult to deal with in the past can be resolved by using deep learning methods. Image processing has been used as an assistant tool in many different kinds of research fields. In ecological research, researchers usually utilize photography equipment to collecting image or video data, and then process them for further analysis. Processing some types of data for people is very tedious and time-consuming.

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HUANG, JHEN-KE, and 黃振珂. "Tire Bubble Defect Detection Based on Convolutional Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/fd9r86.

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碩士<br>國立雲林科技大學<br>資訊工程系<br>105<br>Digital shearography is a non-destructive method, which has been used to detect internal defects in many products. With the rapid development of deep learning, automatic inspection becomes practicable. In this paper, we apply two convolutional neural networks for detecting tire bubble defects. Experimental results show that the proposed bubble defect detection system can complete detect the bubble defects and reduce the false positive of the non-bubble defects.

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Dang, L. M., S. I. Hassan, I. Suhyeon, et al. "UAV based wilt detection system via convolutional neural networks." 2018. http://hdl.handle.net/10454/17186.

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Yes<br>The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing a significant decline in both quality and coverage ratio of the plants and crops on a global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnos

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Chen, Nai-Wei, and 陳乃瑋. "Compression of Convolutional Neural Networks Based on Kernel Redundancy." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6wquk2.

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碩士<br>國立政治大學<br>資訊科學系<br>107<br>The model size and floating-point operations (FLOP) required by convolutional neural networks make it difficult to deploy these models to mobile devices or embedded systems. In this thesis, we propose a method known as the diversifying algorithm to compress CNN models. The key concept is to maintain the diversity of convolutional kernels by preserving the most representative filters in each network layer. This is achieved by expressing the network architecture as an undirected graph. The nodes in the graph denote the filters, and the weights are computed using c

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CHEN, ZHENG-DA, and 陳政達. "Image Colorization Based on Two-stage Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/384s37.

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碩士<br>國立臺北科技大學<br>電機工程研究所<br>107<br>In human visual habits, color images can provide more information than grayscale images. If we can color grayscale images, it not only increases the visibility of the images, but also provides more image information. At present, most of the papers mainly use convolutional neural networks. Although they have good coloring effects, the complexity of the network architecture is too high. Therefore, this paper proposes two-stage networks architecture to reduce the complexity. This work proposes an image colorization framework based on a two-stage con

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Huang, Kuo-Chung, and 黃國忠. "Document Classification based on Deep Convolutional Recurrent Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/rchsg2.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>107<br>Due to the rise of deep learning, our study aims at using the 20 Newsgroups data to run computer simulation with machine learning and deep learning techniques and the frameworks of scikit-learn and Keras. With different machine learning and deep learning techniques, the accuracies of text classification are various. 96% accuracy can be achieved via deep learning. Regarding deep learning, we constructed neural networks. Further, we utilized pre-trained word vectors as inputs of the neural networks, which are generated by glove6b, word2vec, and fasttext, and

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Su, Yu-Sheng, and 蘇裕勝. "Heterogeneous Graph Embedding Based on Graph Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/222yfr.

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碩士<br>國立政治大學<br>資訊科學系<br>108<br>In recent years, information network embedding has become popular because the techniques enable to encode information into low-dimensions representation, even for a graph/network with multiple types of nodes and relations. In addition, graph neural network (GNN) has also shown its effectiveness in learning large-scale node representations on node classification. In this paper, therefore, we propose a framework based on the heterogeneous network embedding and the idea of graph neural network. In our framework, we first generate node representations by various net

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Chao, Yung-Lin, and 趙永霖. "Multiclass restaurant image classification based on Convolutional Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kpdv56.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>Artificial intelligence and deep learning are now widely used in various fields, For example,image recognition, electronic commerce, or the new media industry... its performance is much higher than the traditional way. In today's online world, restaurants often place photos of restaurants on the Internet for public reference, or Internet celebrities will share the restaurants they have visited to social media such as Instagram and Facebook, but the photos are dazzling and unfocused. So we want to design an application that can properly classify cluttered images

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Li, Yi-Hsiu, and 李易修. "Face Alignment Based on Modified Deep Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v79j68.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>106<br>Many face-related applications including face recognition, emotion detection, and medical cosmetology, rely on accurate facial features information. Manual labeling are inefficient, unstable, and subjective, and therefore an efficient automatic facial landmarking technique has been a crucial research topic. Current automatic facial feature extraction techniques based on deep learning networks are mostly applied to frontal facial landmarking. This thesis explores the method of deep convolutional neural networks for detecting 21 features on profile faces. Three

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