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Relevant bibliographies by topics / Convolutional Neural Networks; cannabinoids / Dissertations / Theses

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Dissertations / Theses on the topic 'Convolutional Neural Networks; cannabinoids'

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Published: 5 June 2025

Last updated: 15 July 2025

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1

Buratti, Luca. "Visualisation of Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Le Reti Neurali, e in particolare le Reti Neurali Convoluzionali, hanno recentemente dimostrato risultati straordinari in vari campi. Purtroppo, comunque, non vi è ancora una chiara comprensione del perchè queste architetture funzionino così bene e soprattutto è difficile spiegare il comportamento nel caso di fallimenti. Questa mancanza di chiarezza è quello che separa questi modelli dall’essere applicati in scenari concreti e critici della vita reale, come la sanità o le auto a guida autonoma. Per questa ragione, durante gli ultimi anni sono stati portati avanti diversi studi in modo tale d

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Mancevo, del Castillo Ayala Diego. "Compressing Deep Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217316.

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Deep Convolutional Neural Networks and "deep learning" in general stand at the cutting edge on a range of applications, from image based recognition and classification to natural language processing, speech and speaker recognition and reinforcement learning. Very deep models however are often large, complex and computationally expensive to train and evaluate. Deep learning models are thus seldom deployed natively in environments where computational resources are scarce or expensive. To address this problem we turn our attention towards a range of techniques that we collectively refer to as "mo

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Long, Cameron E. "Quaternion Temporal Convolutional Neural Networks." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597.

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Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n

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Molin, David. "Pedestrian Detection Using Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120019.

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Pedestrian detection is an important field with applications in active safety systems for cars as well as autonomous driving. Since autonomous driving and active safety are becoming technically feasible now the interest for these applications has dramatically increased.The aim of this thesis is to investigate convolutional neural networks (CNN) for pedestrian detection. The reason for this is that CNN have recently beensuccessfully applied to several different computer vision problems. The main applications of pedestrian detection are in real time systems. For this reason,this thesis investiga

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Mattsson, Niklas. "Classification Performance of Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305342.

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The purpose of this thesis is to determine the performance of convolutional neural networks in classifications per millisecond, not training or accuracy, for the GTX960 and the TegraX1. This is done through varying parameters of the convolutional neural networks and using the Python framework Theano's function profiler to measure the time taken for different networks. The results show that increasing any parameter of the convolutional neural network also increases the time required for the classification of an image. The parameters do not punish the network equally, however. Convolutional laye

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Jönsson, Jonatan, and Felix Stenbäck. "Fence surveillance with convolutional neural networks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37116.

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Broken fences is a big security risk for any facility or area with strict security standards. In this report we suggest a machine learning approach to automate the surveillance for chain-linked fences. The main challenge is to classify broken and non-broken fences with the help of a convolution neural network. Gathering data for this task is done by hand and the dataset is about 127 videos at 26 minutes length total on 23 diﬀerent locations. The model and dataset are tested on three performances traits, scaling, augmentation improvement and false rate. In these tests we concluded that nearest

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Katrenko, Maksim, and Максим Олександрович Катренко. "Convolutional neural networks during object identification." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50753.

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1. Geoffrey E. Hinton, A. Krizhevsky & S. D. Wang. URL: http://www.cs.toronto.edu/~fritz/absps/transauto6.pdf (Last accessed: 17.02.2021). 2. Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. URL: https://arxiv.org/abs/1710.09829 (Last accessed: 14.02.2021). 3. Geoffrey E. Hinton. URL: https://u.to/Ov4rGw (Last accessed: 14.02.2021). 4. Anish Athalye, Logan Engstrom, Andrew Ilyas & Kevin Kwok. URL: https://www.labsix.org/physical-objects-that-fool-neural-nets/ (Last accessed: 14.02.2021).<br>Nowadays, convolutional neural networks perform very well in identifying objects, but unfortunately,

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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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Gousseau, Clément. "Hyperparameter Optimization for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272107.

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Training algorithms for artificial neural networks depend on parameters called the hyperparameters. They can have a strong influence on the trained model but are often chosen manually with trial and error experiments. This thesis, conducted at Orange Labs Lannion, presents and evaluates three algorithms that aim at solving this task: a naive approach (random search), a Bayesian approach (Tree Parzen Estimator) and an evolutionary approach (Particle Swarm Optimization). A well-known dataset for handwritten digit recognition (MNIST) is used to compare these algorithms. These algorithms are also

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Plouet, Erwan. "Convolutional and dynamical spintronic neural networks." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP120.

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Cette thèse aborde le développement de composants spintroniques pour le calcul neuromorphique, une approche novatrice visant à réduire la consommation énergétique significative des applications d'intelligence artificielle (IA). L'adoption généralisée de l'IA, y compris des très grands modèles de langage tels que ChatGPT, a entraîné une augmentation des besoins énergétiques, les centres de données consommant environ 1 à 2 de l'énergie mondiale, avec une projection de doublement d'ici 2030. Les architectures hardware traditionnelles, qui séparent la mémoire et les unités de traitement, ne sont p

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Battilana, Pietro. "Convolutional Neural Networks for Image Style Transfer." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16770/.

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In this thesis we will use deep learning tools to tackle an interesting and complex problem of image processing called style transfer. Given a content image and a style image as inputs, the aim is to create a new image preserving the global structure of the content image but showing the artistic patterns of the style image. Before the renaissance of Arti�cial Neural Networks, early work in the �field called texture synthesis, only transferred limited and repeatitive geometric patterns of textures. Due to the avaibility of large amounts of data and cheap computational resources in the last

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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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Avramova, Vanya. "Curriculum Learning with Deep Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-178453.

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Curriculum learning is a machine learning technique inspired by the way humans acquire knowledge and skills: by mastering simple concepts first, and progressing through information with increasing difficulty to grasp more complex topics. Curriculum Learning, and its derivatives Self Paced Learning (SPL) and Self Paced Learning with Diversity (SPLD), have been previously applied within various machine learning contexts: Support Vector Machines (SVMs), perceptrons, and multi-layer neural networks, where they have been shown to improve both training speed and model accuracy. This project ventured

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Svensson, Göran, and Jonas Westlund. "Intravenous bag monitoring with Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148449.

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Drip bags are used in hospital environments to administerdrugs and nutrition to patients. Ensuring that they are usedcorrectly and are refilled in time are important for the safetyof patients. This study examines the use of a ConvolutionalNeural Network (CNN) to monitor the fluid levels of drip bagsvia image recognition to potentially form the base of an earlywarning system, and assisting in making medical care moreefficient. Videos of drip bags were recorded as they wereemptying their contents in a controlled environment and fromdifferent angles. A CNN was built to analyze the recordeddata in

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Eberlein, Matthias, Raphael Hildebrand, Ronald Tetzlaff, et al. "Convolutional Neural Networks for Epileptic Seizure Prediction." Institute of Electrical and Electronics Engineers (IEEE), 2018. https://tud.qucosa.de/id/qucosa%3A33336.

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Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient’s uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segme

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Gnacek, Matthew. "Convolutional Neural Networks for Enhanced Compression Techniques." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620139118743853.

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Emmot, Sebastian. "Characterizing Video Compression Using Convolutional Neural Networks." Thesis, Luleå tekniska universitet, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-79430.

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Can compression parameters used in video encoding be estimated, given only the visual information of the resulting compressed video? If so, these parameters could potentially improve existing parametric video quality estimation models. Today, parametric models use information like bitrate to estimate the quality of a given video. This method is inaccurate since it does not consider the coding complexity of a video. The constant rate factor (CRF) parameter for h.264 encoding aims to keep the quality constant while varying the bitrate, if the CRF for a video is known together with bitrate, a bet

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Zamboni, Simone. "Pedestrian trajectory prediction with Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278818.

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Modelling the behaviour of pedestrians is essential in autonomous driving because consequences for misjudging the intentions of a pedestrian can be severe when dealing with vehicles. Therefore, for an autonomous vehicle to plan a safe and collision-free path, it is necessary not only to know the current position of nearby pedestrians but also their future trajectory. In literature, methods to approach the problem of pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. This thesis proposes a new approach

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Messou, Ehounoud Joseph Christopher. "Handling Invalid Pixels in Convolutional Neural Networks." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98619.

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Most neural networks use a normal convolutional layer that assumes that all input pixels are valid pixels. However, pixels added to the input through padding result in adding extra information that was not initially present. This extra information can be considered invalid. Invalid pixels can also be inside the image where they are referred to as holes in completion tasks like image inpainting. In this work, we look for a method that can handle both types of invalid pixels. We compare on the same test bench two methods previously used to handle invalid pixels outside the image (Partial and Edg

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Martell, Patrick Keith. "Hierarchical Auto-Associative Polynomial Convolutional Neural Networks." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

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Norée, Palm Caspar, and Hugo Granström. "Upscaling of pictures using convolutional neural networks." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445567.

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The task of upscaling pictures is very ill-posed since it requires the creation of novel data. Any algorithm or model trying to perform this task will have to interpolate and guess the missing pixels in the pictures. Classical algorithms usually result in blurred or pixelated interpolations, especially visible around sharp edges. The reason it could be considered a good idea to use neural networks to upscale pictures is because they can infer context when upsampling different parts of an image. In this report, a special deep learning structure called U-Net is trained on reconstructing high-res

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Sallander, Oscar. "Convolutional Neural Networks: Performance on Imbalanced Data." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184920.

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Imbalanced data is a major problem in machine learning classification, since predictive performance can be hindered when one class occurs more frequently than the others. For example, in medical science, imbalanced data sets are very common. When searching for rare diseases in a population, the healthy proportion can be extremely large in comparison to the proportion with a disease.This raises a problem, because when a model is given only a few example observations of one class and a larger amount of observations of the other, the model tends to be biased towards the majority class. When the l

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Andriolo, Stefano. "Convolutional Neural Networks in Tomographic Image Enhancement." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22843/.

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Convolutional Neural Networks have seen a huge rise in popularity in image applications. They have been used in medical imaging contexts to enhance the overall quality of the digital representation of the patient's scanned body region and have been very useful when dealing with limited-angle tomographic data. In this thesis, a particular type of convolutional neural network called Unet will be used as the starting point to explore the effectiveness of different networks in enhancing tomographic image reconstructions. We will first make minor tweaks to the 2-dimensional convolutional network an

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Duffner, Stefan. "Face image analysis with convolutional neural networks." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-48350.

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Stiff, Harald. "Stem Cell Classification With Convolutional Neural Networks." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214732.

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In this bachelor thesis project, the problem of imageclassification with convolutional neural networks is considered.In several fields of biology, automatized cell detection is a helpfultool for facilitating the process of cellular analysis. This reportanswers the question whether a computer program can tell if animage contains muscle stem cells or not. Analogously to the neuronsof the human brain, the creation of such a program involvestraining thousands of mathematically modeled artificial neuronsto maximize the likelihood of producing correct classifications.This report covers how such a ne

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Zhong, Weilun. "Movie scene recognition with Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174838.

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Ilg, Eddy [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Estimating optical flow with convolutional neural networks." Freiburg : Universität, 2019. http://d-nb.info/1214592465/34.

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Wang, Run Fen. "Semantic Text Matching Using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362134.

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Semantic text matching is a fundamental task for many applications in NaturalLanguage Processing (NLP). Traditional methods using term frequencyinversedocument frequency (TF-IDF) to match exact words in documentshave one strong drawback which is TF-IDF is unable to capture semanticrelations between closely-related words which will lead to a disappointingmatching result. Neural networks have recently been used for various applicationsin NLP, and achieved state-of-the-art performances on many tasks.Recurrent Neural Networks (RNN) have been tested on text classificationand text matching, but it d

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Jackman, Simeon. "Football Shot Detection using Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architecture

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Gerard, Alex Michael. "Iterative cerebellar segmentation using convolutional neural networks." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6579.

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Convolutional neural networks (ConvNets) have quickly become the most widely used tool for image perception and interpretation tasks over the past several years. The single most important resource needed for training a ConvNet that will successfully generalize to unseen examples is an adequately sized labeled dataset. In many interesting medical imaging cases, the necessary size or quality of training data is not suitable for directly training a ConvNet. Furthermore, access to the expertise to manually label such datasets is often infeasible. To address these barriers, we investigate a method

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Huynh, L. (Lam). "Structure-from-motion using convolutional neural networks." Master's thesis, University of Oulu, 2018. http://jultika.oulu.fi/Record/nbnfioulu-201809062760.

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Abstract. There is an increasing interest in the research community to 3D scene reconstruction from monocular RGB cameras. Conventionally, structure from motion or special hardware such as depth sensors or LIDAR systems were used to reconstruct the point clouds of complex scenes. However, structure from motion technique usually fails to create the dense point cloud, while particular sensors are inconvenient and more expensive than RGB cameras. Recent advances in deep learning research have presented remarkable results in many computer vision tasks. Nevertheless, complete solution for large-sca

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Trinh, Loc Quang. "Greedy layerwise training of convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123128.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-63).<br>Layerwise training presents an alternative approach to end-to-end back-propagation for training deep convolutional neural networks. Although previous work was unsuccessful in demonstrating the viability of layerwise

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LEONARDI, MARCO. "Image Collection Management using Convolutional Neural Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365014.

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Al giorno d’oggi ormai quasi chiunque possiede uno smartphone dotato di una telecamera ad alta risoluzione. Negli ultimi decenni, i contenuti multimediali (immagini e video) stanno sempre più spesso diventando il principale mezzo di comunicazione. Dato il continuo calo dei prezzi dei dispositivi di archiviazione, il numero totale di immagini salvate sta aumentando notevolmente, andando così a creare collezioni di immagini sempre più grandi, a tal punto da essere una problema per chi vuole le vuole esplorare. Data una libreria di immagini, il processo di selezione di un gruppo di foto che rap

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Morefield, Samantha I. (Samantha Irene). "Responses of Cultured Neuronal Networks to the Cannabinoid Mimetic Anandamide." Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc277717/.

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The effects of cannabinoid agonists on spontaneous neuronal network activity were characterized in murine spinal cord and auditory cortical cultures with multichannel extracellular recording using photoetched electrode arrays. Different cultures responded reproducibly with global decreases of spiking and bursting to anandamide and methanandamide, but each agonist showed unique minor effects on network activity. The two tissues responded in a tissue-specific manner. Spontaneous activity in spinal tissue was terminated by 1 μM anandamide and 6.1 μM methanandamide. Cortical activity ceased at 3.5

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Ferroni, Nicola. "Exact Combinatorial Optimization with Graph Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17502/.

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Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly lar

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Laflamme, Patrick. "Superstitious perception in humans and convolutional neural networks." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62605.

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The advent of complex Hierarchical Convolutional Neural Networks (HCNNs) has led to great progress in the field of computer vision, with modern implementations of HCNNs rivalling human performance in object recognition tasks. The design of HCNNs was inspired by current understanding of how the neurons of the human visual system are organized to support object recognition. There are researchers who claim that the computations undertaken by HCNNs are approximating those of the human visual system, because of their high accuracy in predicting the neural activity of regions of the brain involved i

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Venkatesh, Anirudh. "Object Tracking in Games using Convolutional Neural Networks." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1845.

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Computer vision research has been growing rapidly over the last decade. Recent advancements in the field have been widely used in staple products across various industries. The automotive and medical industries have even pushed cars and equipment into production that use computer vision. However, there seems to be a lack of computer vision research in the game industry. With the advent of e-sports, competitive and casual gaming have reached new heights with regard to players, viewers, and content creators. This has allowed for avenues of research that did not exist prior. In this thesis, we ex

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Cronje, Frans. "Human action recognition with 3D convolutional neural networks." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15482.

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Convolutional neural networks (CNNs) adapt the regular fully-connected neural network (NN) algorithm to facilitate image classification. Recently, CNNs have been demonstrated to provide superior performance across numerous image classification databases including large natural images (Krizhevsky et al., 2012). Furthermore, CNNs are more readily transferable between different image classification problems when compared to common alternatives. The extension of CNNs to video classification is simple and the rationale behind the components of the model are still applicable due to the similarity be

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Neri, Mattia. "Segmentazione di immagini mammografiche con convolutional neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6681/.

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Il tumore al seno si colloca al primo posto per livello di mortalità tra le patologie tumorali che colpiscono la popolazione femminile mondiale. Diversi studi clinici hanno dimostrato come la diagnosi da parte del radiologo possa essere aiutata e migliorata dai sistemi di Computer Aided Detection (CAD). A causa della grande variabilità di forma e dimensioni delle masse tumorali e della somiglianza di queste con i tessuti che le ospitano, la loro ricerca automatizzata è un problema estremamente complicato. Un sistema di CAD è generalmente composto da due livelli di classificazione: la detecti

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Lin, Shan. "Analysing Generalisation Error Bounds For Convolutional Neural Networks." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20315.

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Analysing Generalisation Error Bounds for Convolutional Neural Networks Abstract: Convolutional neural networks (CNNs) have achieved breakthrough performance in a wide range of applications including image classification, semantic segmentation, and object detection. Previous research on characterising the generalisability of neural networks has mostly focused on fully connected neural networks (FNNs), with CNNs regarded as a special case of FNNs without taking into account the special structure of convolutional layers; therefore, the CNN bounds may not be as tight as in FNNs. Here we propose a

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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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Ziogas, Georgios. "Classifying Handwritten Chinese Characters using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371526.

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Image recognition applications have been increasingly gaining popularity, as computer hardware was getting more powerful and cheaper. This increase in computational resources, led researchers even closer to their target on creating algorithms that could achieve high accuracy in image recognition tasks. These algorithms are applied in many different fields, such as in medical images analysis and object recognition in real-time applications.Previously studies have shown that among many image recognition algorithms, artificial neural networks and specifically deep neural networks, perform outstan

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Larsson, Susanna. "Monocular Depth Estimation Using Deep Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159981.

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For a long time stereo-cameras have been deployed in visual Simultaneous Localization And Mapping (SLAM) systems to gain 3D information. Even though stereo-cameras show good performance, the main disadvantage is the complex and expensive hardware setup it requires, which limits the use of the system. A simpler and cheaper alternative are monocular cameras, however monocular images lack the important depth information. Recent works have shown that having access to depth maps in monocular SLAM system is beneficial since they can be used to improve the 3D reconstruction. This work proposes a deep

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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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Truzzi, Stefano. "Event classification in MAGIC through Convolutional Neural Networks." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1216295.

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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology an

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Ressi, Dalila <1991&gt. "Convolutional Neural Networks Compression for Embedded Industrial Applications." Doctoral thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/22050.

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Convolutional Neural Networks have proved to be a powerful tool to solve a wide range of Computer Vision tasks, especially where is difficult to implement a solution in a purelyalgorithmic way. In the industry, the availability of powerful deep models to address classification, detection, and image segmentation now offers new possibilities for automating not only the production, but also the quality assessment of the final products. Unfortunately, industrial applications have to face some limitations, especially when dealing with the so called ”Embedded Vision” solutions where such models have

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DiMascio, Michelle Augustine. "Convolutional Neural Network Optimization for Homography Estimation." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544214038882564.

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Borra, Davide <1992&gt. "Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10345/1/phdthesis_dborra.pdf.

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Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and h

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Maragno, Alessandro. "Programmazione di Convolutional Neural Networks orientata all'accelerazione su FPGA." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12476/.

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Attualmente la Computer Vision, disciplina che consente di estrarre informazioni a partire da immagini digitali, è uno dei settori informatici più in fermento. Grazie alle recenti conquiste e progressi, tale settore ha raggiunto uno stato di maturità tale da poter essere applicato in svariati ambiti, a partire da quello industriale, fino ad arrivare ad applicazioni più vicine alla vita quotidiana. In particolare, si è raggiunto uno stato dell'arte sempre più solido nel campo del riconoscimento di oggetti (object detection) grazie allo sviluppo delle Convolutional Neural Networks (CNN): sistemi

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