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Relevant bibliographies by topics / Semantic segmentation / Dissertations / Theses

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Dissertations / Theses on the topic 'Semantic segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Zou, Wenbin. "Semantic-oriented Object Segmentation." Thesis, Rennes, INSA, 2014. http://www.theses.fr/2014ISAR0007/document.

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Cette thèse porte sur les problèmes de segmentation d’objets et la segmentation sémantique qui visent soit à séparer des objets du fond, soit à l’attribution d’une étiquette sémantique spécifique à chaque pixel de l’image. Nous proposons deux approches pour la segmentation d’objets, et une approche pour la segmentation sémantique. La première approche est basée sur la détection de saillance. Motivés par notre but de segmentation d’objets, un nouveau modèle de détection de saillance est proposé. Cette approche se formule dans le modèle de récupération de la matrice de faible rang en exploitant

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2

Johnson, M. A. "Semantic segmentation and image search." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605649.

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Understanding the meaning behind visual data is increasingly important as the quantity of digital images in circulation explodes, and as computing in general and the Internet in specific shifts quickly towards an increasingly visual presentation of data. However, the remarkable amount of variance inside categories (e.g. different kinds of chairs) combined with the occurrence of similarity between categories (e.g. similar breeds of cats and dogs) makes this problem incredibly difficult to solve. In particular, the <i>semantic segmentation</i> of images into contiguous regions of similar interpr

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3

Kernell, Björn. "Improving Photogrammetry using Semantic Segmentation." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148491.

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3D reconstruction is the process of constructing a three-dimensional model from images. It contains multiple steps where each step can induce errors. When doing 3D reconstruction of outdoor scenes, there are some types of scene content that regularly cause problems and affect the resulting 3D model. Two of these are water, due to its fluctuating nature, and sky because of it containing no useful (3D) data. These areas cause different problems throughout the process and do generally not benefit it in any way. Therefore, masking them early in the reconstruction chain could be a useful step in an

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4

Malec, Stanislaw. "Semantic Segmentation with Carla Simulator." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105287.

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Autonomous vehicles perform semantic segmentation to orient themselves, but training neural networks for semantic segmentation requires large amounts of labeled data. A hand-labeled real-life dataset requires considerable effort to create, so we instead turn to virtual simulators where the segmented labels are known to generate large datasets virtually for free. This work investigates how effective synthetic datasets are in driving scenarios by collecting a dataset from a simulator and testing it against a real-life hand-labeled dataset. We show that we can get a model up and running faster by

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5

Gulshan, Varun. "From interactive to semantic image segmentation." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:706b648a-e5e7-4334-a456-0f0b5701dbc4.

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This thesis investigates two well defined problems in image segmentation, viz. interactive and semantic image segmentation. Interactive segmentation involves power assisting a user in cutting out objects from an image, whereas semantic segmentation involves partitioning pixels in an image into object categories. We investigate various models and energy formulations for both these problems in this thesis. In order to improve the performance of interactive systems, low level texture features are introduced as a replacement for the more commonly used RGB features. To quantify the improvement obta

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6

Gao, Jizhou. "VISUAL SEMANTIC SEGMENTATION AND ITS APPLICATIONS." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/14.

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This dissertation addresses the difficulties of semantic segmentation when dealing with an extensive collection of images and 3D point clouds. Due to the ubiquity of digital cameras that help capture the world around us, as well as the advanced scanning techniques that are able to record 3D replicas of real cities, the sheer amount of visual data available presents many opportunities for both academic research and industrial applications. But the mere quantity of data also poses a tremendous challenge. In particular, the problem of distilling useful information from such a large repository of

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7

Raza, Syed H. "Temporally consistent semantic segmentation in videos." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53455.

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The objective of this Thesis research is to develop algorithms for temporally consistent semantic segmentation in videos. Though many different forms of semantic segmentations exist, this research is focused on the problem of temporally-consistent holistic scene understanding in outdoor videos. Holistic scene understanding requires an understanding of many individual aspects of the scene including 3D layout, objects present, occlusion boundaries, and depth. Such a description of a dynamic scene would be useful for many robotic applications including object reasoning, 3D perception, video analy

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8

Chen, Yifu. "Deep learning for visual semantic segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.

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Dans cette thèse, nous nous intéressons à la segmentation sémantique visuelle, une des tâches de haut niveau qui ouvre la voie à une compréhension complète des scènes. Plus précisément, elle requiert une compréhension sémantique au niveau du pixel. Avec le succès de l’apprentissage approfondi de ces dernières années, les problèmes de segmentation sémantique sont abordés en utilisant des architectures profondes. Dans la première partie, nous nous concentrons sur la construction d’une fonction de coût plus appropriée pour la segmentation sémantique. En particulier, nous définissons une nouvelle

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9

Lotz, Max. "Depth Inclusion for Classification and Semantic Segmentation." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233371.

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The majority of computer vision algorithms only use RGB images to make inferencesabout the state of the world. With the increasing availability of RGB-D cameras it is im-portant to examine ways to effectively fuse this extra modality for increased effective-ness. This paper examines how depth can be fused into CNNs to increase accuracy in thetasks of classification and semantic segmentation, as well as examining how this depthshould best be effectively encoded prior to inclusion in the network. Concatenating depthas

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10

Jain, Shipra. "Pushing the boundary of Semantic Image Segmentation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290304.

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The state-of-the-art object detection and image classification methods can perform impressively on more than 9k classes. In contrast, the number of classes in semantic segmentation datasets are fairly limited. This is not surprising , when the restrictions caused by the lack of labeled data and high computation demand are considered. To efficiently perform pixel-wise classification for c number of classes, segmentation models use cross-entropy loss on c-channel output for each pixel. The computational demand for such prediction turns out to be a major bottleneck for higher number of classes. T

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11

Jia, Fan. "Regularized neural networks for semantic image segmentation." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/882.

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Image processing consists of a series of tasks which widely appear in many areas. It can be used for processing photos taken by people's cameras, astronomy radio, radar imaging, medical devices and tomography. Among these tasks, image segmentation is a fundamental task in a series of applications. Image segmentation is so important that it attracts hundreds of thousands of researchers from lots of fields all over the world. Given an image, the goal of image segmentation is to classify all pixels into several classes. Given an image defined over a domain, the segmentation task is to divide the

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Menart, Christopher J. Menart. "Global-Context Refinement for Semantic Image Segmentation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808.

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Sarpangala, Kishan. "Semantic Segmentation Using Deep Learning Neural Architectures." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin157106185092304.

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14

andreini, paolo. "Multi-stage generation for semantic image segmentation." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1105677.

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Computer vision is one of the most active fields of research in artificial intelligence. Recently, state–of–the–art results have been achieved in a variety of tasks related to computer vision adopting deep learning models. However, most of these approaches rely on supervised end–to–end training of complex neural networks that contain a huge number of parameters. Therefore, training these models requires large sets of supervised examples to obtain good generalization capabilities, which prevents their use in scenarios where there is scarcity of data. The main goal of this thesis is to provid

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Paternesi, Claudio. "Virtual Reality Labelling Tool for 3D Semantic Segmentation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Durante gli ultimi anni nel campo della Computer Vision si sono susseguiti studi sempre più approfonditi sulla segmentazione semantica 3D, questi lavori richiedono spesso una enorme quantità di modelli 3D su cui fare le elaborazioni. Non sempre però, i dataset disponibili forniscono delle informazioni complete riguardanti anche la segmentazione dei modelli 3D. In questa tesi si propone uno strumento software con cui si possa creare, a partire da un modello 3D, la sua versione segmentata semanticamente, così da poter creare dei dataset completi da usare nelle fasi di training e test di modelli

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Schroff, Florian. "Semantic Image Segmentation and Web-Supervised Visual Learning." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504578.

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Thirde, David J. "Motion segmentation of semantic objects in video sequences." Thesis, Kingston University, 2007. http://eprints.kingston.ac.uk/20299/.

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The extraction of meaningful objects from video sequences is becoming increasingly important in many multimedia applications such as video compression or video post-production. The goal of this thesis is to review, evaluate and build upon the wealth of recent work on the problem of video object segmentation in the context of probabilistic techniques for generic video object segmentation. Methods are suggested that solve this problem using formal probabilistic learning techniques, this allows principled justification of methods applied to the problem of segmenting video objects. By applying a s

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Bishop, Griffin R. "Unsupervised Semantic Segmentation through Cross-Instance Representation Similarity." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1371.

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Semantic segmentation methods using deep neural networks typically require huge volumes of annotated data to train properly. Due to the expense of collecting these pixel-level dataset annotations, the problem of semantic segmentation without ground-truth labels has been recently proposed. Many current approaches to unsupervised semantic segmentation frame the problem as a pixel clustering task, and in particular focus heavily on color differences between image regions. In this paper, we explore a weakness to this approach: By focusing on color, these approaches do not adequately capture relati

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Sorg, Bradley R. "Multi-Task Learning SegNet Architecture for Semantic Segmentation." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1542726487025455.

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Tosteberg, Patrik. "Semantic Segmentation of Point Clouds Using Deep Learning." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136793.

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In computer vision, it has in recent years become more popular to use point clouds to represent 3D data. To understand what a point cloud contains, methods like semantic segmentation can be used. Semantic segmentation is the problem of segmenting images or point clouds and understanding what the different segments are. An application for semantic segmentation of point clouds are e.g. autonomous driving, where the car needs information about objects in its surrounding. Our approach to the problem, is to project the point clouds into 2D virtual images using the Katz projection. Then we use pre-t

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Ravì, Daniele. "True scene understanding: classification, semantic segmentation and retriaval." Doctoral thesis, Università di Catania, 2014. http://hdl.handle.net/10761/1556.

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The huge volume of images shared in the web sites and on personal archives has provided us challenges on massive multimedia management. Due to the well-known semantic gap between human-understandable high-level semantics and machine generated low-level features, recent years have witnessed plenty of research effort on multimedia content understanding and indexing. Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have reached impressive results. The next challenge is to integrate all these algorithms and address the problem of the complete s

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MATRONE, FRANCESCA. "Deep Semantic Segmentation of Built Heritage Point Clouds." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2924998.

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SOHALIYA, GAURAV. "SEMANTIC SEGMENTATION USING CONDITIONAL GAN WITH PERCEPTUAL LOSS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18857.

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Image-to-semantic labels classification is a very challenging task in image processing. Convolutional neural networks (CNN) have managed to achieve the state-of-the-art quality of the segmented image in semantic segmentation tasks. Still, the classification capability of such algorithms is not satisfactory to segment images that contain complex object boundaries and minimal regions. Recently, the Generative Adversarial Networks (GAN) were introduced, which can solve the overfitting of the generator network using the adversarial loss. In this paper, a GAN-based segmentation model is propo

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Tranell, Victor. "Semantic Segmentation of Oblique Views in a 3D-Environment." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153866.

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This thesis presents and evaluates different methods to semantically segment 3D-models by rendered 2D-views. The 2D-views are segmented separately and then merged together. The thesis evaluates three different merge strategies, two different classification architectures, how many views should be rendered and how these rendered views should be arranged. The results are evaluated both quantitatively and qualitatively and then compared with the current classifier at Vricon presented in [30]. The conclusion of this thesis is that there is a performance gain to be had using this method. The best mo

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Kolhatkar, Dhanvin. "Real-Time Instance and Semantic Segmentation Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40616.

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In this thesis, we explore the use of Convolutional Neural Networks for semantic and instance segmentation, with a focus on studying the application of existing methods with cheaper neural networks. We modify a fast object detection architecture for the instance segmentation task, and study the concepts behind these modifications both in the simpler context of semantic segmentation and the more difficult context of instance segmentation. Various instance segmentation branch architectures are implemented in parallel with a box prediction branch, using its results to crop each instance's feature

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Fröhlich, Björn [Verfasser]. "Semantic Segmentation with Efficient Tree-Based Methods / Björn Fröhlich." München : Verlag Dr. Hut, 2014. http://d-nb.info/1047994607/34.

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Zhang, Chenxi. "Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling." UKnowledge, 2014. http://uknowledge.uky.edu/cs_etds/27.

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This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics p

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Lindberg, Hampus. "Semantic Segmentation of Iron Ore Pellets in the Cloud." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86896.

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This master's thesis evaluates data annotation, semantic segmentation and Docker for use in AWS. The data provided has to be annotated and is to be used as a dataset for the creation of a neural network. Different neural network models are then to be compared based on performance. AWS has the option to use Docker containers and thus that option is to be examined, and lastly the different tools available in AWS SageMaker will be analyzed for bringing a neural network to the cloud. Images were annotated in Ilastik and the dataset size is 276 images, then a neural network was created in PyTorch b

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Espis, Andrea. "Object detection and semantic segmentation for assisted data labeling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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The automation of data labeling tasks is a solution to the errors and time costs related to human labeling. In this thesis work CenterNet, DeepLabV3, and K-Means applied to the RGB color space, are deployed to build a pipeline for Assisted data labeling: a semi-automatic process to iteratively improve the quality of the annotations. The proposed pipeline pointed out a total of 1547 wrong and missing annotations when applied to a dataset originally containing 8,300 annotations. Moreover, the quality of each annotation has been drastically improved, and at the same time, more than 600 hours of w

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Lilja, Harald. "Semantic Scene Segmentation using RGB-D & LRF fusion." Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42239.

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In the field of robotics and autonomous vehicles, the use of RGB-D data and LiDAR sensors is a popular practice for applications such as SLAM[14], object classification[19] and scene understanding[5]. This thesis explores the problem of semantic segmentation using deep multimodal fusion of LRF and depth data. Two data set consisting of 1080 and 108 data points from two scenes is created and manually labeled in 2D space and transferred to 1D using a proposed label transfer method utilizing hierarchical clustering. The data set is used to train and validate the suggested method for segmentation

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luo, sai. "Semantic Movie Scene Segmentation Using Bag-of-Words Representation." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500375283397255.

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Christopher, Rosenvall. "Semantic Segmentation of Iron Pellets as a Cloud Service." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81950.

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This master’s thesis evaluates automatic data annotation and machine learning predictions of iron ore pellets using tools provided by Amazon Web Services (AWS) in the cloud. The main tool in focus is Amazon SageMaker which is capable of automatic data annotation as well as building, training and deploying machine learning models quickly. Three different models was trained using SageMakers built in semantic segmentation algorithm, PSP, FCN and DeepLabV3. The dataset used for training and evaluation contains 180 images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå

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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the

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Zhou, Wei. "Analysing the Robustness of Semantic Segmentation for Autonomous Vehicles." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/22699.

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Intelligent systems require the capability to perceive and interact with the surrounding environment. Semantic segmentation, as a pixel-level classification task, is at the frontier of providing a human-like understanding to intelligent systems enabling them to view and understand the world as we do. Deep learning based semantic segmentation algorithms have shown considerable success for certain tasks in recent years. However, in real-world safety critical applications such as autonomous vehicles, there are still many complexities that restrict the use of this technology. My research topic is

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Şimon, Anca-Roxana. "Semantic structuring of video collections fromspeech : segmentation and hyperlinking." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S076/document.

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Au cours des dernières années, de nouveaux challenges ont émergé avec la transformation très significative du paysage audiovisuel due à l'émergence de la télévision sur Internet. La décision de ce qui est regardé et dans quel ordre n'appartient en effet plus à la chaîne TV concernée mais à l'utilisateur. De nouveaux moyens facilitant l'accès précis et rapide à l'information souhaitée au sein des quantités toujours croissantes de contenus audiovisuels doivent donc être proposés aux utilisateurs : par exemple, pour repérer un événement spécifique, un fragment d'émission contenant une certaine pe

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Svensson, Terese. "Semantic Segmentation of Iron Ore Pellets with Neural Networks." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74352.

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This master’s thesis evaluates five existing Convolutional Neural Network (CNN) models for semantic segmentation of optical microscopy images of iron ore pellets. The models are PSPNet, FC-DenseNet, DeepLabv3+, BiSeNet and GCN. The dataset used for training and evaluation contains 180 microscopy images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. This thesis also investigates the impact of the dataset size and data augmentation on performance. The best performing CNN model on the task was PSPNet, which had an average accuracy of 91.7% on the dataset. S

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Granli, Petter. "Semantic segmentation of seabed sonar imagery using deep learning." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160561.

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For investigating the large parts of the ocean which have yet to be mapped, there is a need for autonomous underwater vehicles. Current state-of-the-art underwater positioning often relies on external data from other vessels or beacons. Processing seabed image data could potentially improve autonomy for underwater vehicles. In this thesis, image data from a synthetic aperture sonar (SAS) was manually segmented into two classes: sand and gravel. Two different convolutional neural networks (CNN) were trained using different loss functions, and the results were examined. The best performing netwo

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Türkmen, S. (Sercan). "Scene understanding through semantic image segmentation in augmented reality." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906262666.

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Abstract. Semantic image segmentation, the task of assigning a label to each pixel in an image, is a major challenge in the field of computer vision. Semantic image segmentation using fully convolutional neural networks (FCNNs) offers an online solution to the scene understanding while having a simple training procedure and fast inference speed if designed efficiently. The semantic information provided by the semantic segmentation is a detailed understanding of the current context and this scene understanding is vital for scene modification in augmented reality (AR), especially if one aims to

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Bou, Albert. "Deep Learning models for semantic segmentation of mammography screenings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265652.

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This work explores the performance of state-of-the-art semantic segmentation models on mammographic imagery. It does so by comparing several reference semantic segmentation deep learning models on a newly proposed medical dataset of mammograpgy screenings. All models are re-implemented in Tensorflow and validated first on the benchmark dataset Cityscapes. The new medical image corpus was gathered and annotated at the Science for Life Laboratory in Stockholm. In addition, this master thesis shows that it is possible to boost segmentation performance by training the models in an adversarial mann

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Carlsson, Mattias. "Neural Networks for Semantic Segmentation in the Food Packaging Industry." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-145413.

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Industrial applications of computer vision often utilize traditional image processing techniques whereas state-of-the-art methods in most image processing challenges are almost exclusively based on convolutional neural networks (CNNs). Thus there is a large potential for improving the performance of many machine vision applications by incorporating CNNs. One such application is the classification of juice boxes with straws, where the baseline solution uses classical image processing techniques on depth images to reject or accept juice boxes. This thesis aim to investigate how CNNs perform on t

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Ren, Jinchang. "Semantic content analysis for effective video segmentation, summarisation and retrieval." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4251.

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This thesis focuses on four main research themes namely shot boundary detection, fast frame alignment, activity-driven video summarisation, and highlights based video annotation and retrieval. A number of novel algorithms have been proposed to address these issues, which can be highlighted as follows. Firstly, accurate and robust shot boundary detection is achieved through modelling of cuts into sub-categories and appearance based modelling of several gradual transitions, along with some novel features extracted from compressed video. Secondly, fast and robust frame alignment is achieved via t

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Leivas, Oliveira Gabriel [Verfasser], Thomas [Akademischer Betreuer] Brox, and Wolfram [Akademischer Betreuer] Burgard. "Encoder-decoder methods for semantic segmentation: efficiency and robustness aspects." Freiburg : Universität, 2019. http://d-nb.info/1191689476/34.

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Wang, Chen. "2D object detection and semantic segmentation in the Carla simulator." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291337.

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The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decisio

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Alinder, Helena. "Semantic Image Segmentation on Clothing Imagery with Deep Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280788.

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Semantic Image Segmentation is a field within machine learning and computer vision, where the goal is to link each pixel in an image with a label. A successful segmentation will label all pixels that belong to an object with the correct label, and this prediction can be measured with a score known as mean Intersection over Union (mIoU). In a selling process of second-hand clothes, the clothes are placed on a mannequin and then photographed and post-processed. The post-processing algorithm attempts to remove the pole of the mannequin and crop out the mannequin itself to create a clear backgroun

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Daliparthi, Venkata Satya Sai Ajay. "Semantic Segmentation of Urban Scene Images Using Recurrent Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20651.

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Background: In Autonomous Driving Vehicles, the vehicle receives pixel-wise sensor data from RGB cameras, point-wise depth information from the cameras, and sensors data as input. The computer present inside the Autonomous Driving vehicle processes the input data and provides the desired output, such as steering angle, torque, and brake. To make an accurate decision by the vehicle, the computer inside the vehicle should be completely aware of its surroundings and understand each pixel in the driving scene. Semantic Segmentation is the task of assigning a class label (Such as Car, Road, Pedestr

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Ahrneteg, Jakob, and Dean Kulenovic. "Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18219.

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Background. This thesis focuses on the task of historical document semantic segmentation with recurrent neural networks. Document semantic segmentation involves the segmentation of a page into different meaningful regions and is an important prerequisite step of automated document analysis and digitisation with optical character recognition. At the time of writing, convolutional neural network based solutions are the state-of-the-art for analyzing document images while the use of recurrent neural networks in document semantic segmentation has not yet been studied. Considering the nature of a r

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Ding, Lei. "Novel methods for the Semantic Segmentation of Remote Sensing Images." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/322099.

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With the development of Earth observation technologies, there is a tremendous increase in the volume of available remote sensing images (RSIs), and subsequently a growing need for the automatic analysis of the collected data. The pixel-wise classification, i.e., the semantic segmentation of RSIs, is important for a variety of land-cover and land-use mapping applications. Recent studies on the semantic segmentation of RSIs have achieved great progress with the use of Convolutional Neural Networks (CNNs). However, they suffer from some common problems such as fragmentation errors, boundary ambig

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Lenczner, Gaston. "Interactive semantic segmentation of aerial images with deep neural networks." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG067.

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Nous proposons dans cette thèse de mettre en place une collaboration entre un réseau de neurones profond et un utilisateur pour collecter rapidement des cartes de segmentation sémantiques précises d'images de télédétection. En bref, l'utilisateur interagit de manière itérative avec le réseau pour corriger ses prédictions initialement erronées. Concrètement, ces interactions sont des annotations représentant les labels sémantiques. Nos contributions se décomposent en quatre parties. Premièrement, nous proposons deux schémas d'apprentissage interactif pour intégrer les entrées de l'utilisateur d

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Tang, Liyao. "Sub-scene Regulated Attentional Pooling for Point Cloud Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25892.

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Semantic segmentation is one of the key tasks in point cloud processing. To better facilitate the improvement in model design, we conduct a thorough review of various network architecture for general point cloud processing as well as specifically for point cloud semantic segmentation. According to the literature, point-based networks demonstrate great potentials in achieving superior performance due to their ability to directly handle 3D points, compared with multi-view and voxel-based approaches. However, point-based networks usually rely on the max pooling operation to extract useful point

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Lind, Johan. "Make it Meaningful : Semantic Segmentation of Three-Dimensional Urban Scene Models." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143599.

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Semantic segmentation of a scene aims to give meaning to the scene by dividing it into meaningful — semantic — parts. Understanding the scene is of great interest for all kinds of autonomous systems, but manual annotation is simply too time consuming, which is why there is a need for an alternative approach. This thesis investigates the possibility of automatically segmenting 3D-models of urban scenes, such as buildings, into a predetermined set of labels. The approach was to first acquire ground truth data by manually annotating five 3D-models of different urban scenes. The next step was to e

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