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You searched for subject:(Semantic Segmentation). Showing records 1 – 30 of 174 total matches.

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University of Adelaide

1. Shen, Tong. Context Learning and Weakly Supervised Learning for Semantic Segmentation.

Degree: 2018, University of Adelaide

 This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel… (more)

Subjects/Keywords: weakly supervised learning; semantic segmentation

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APA (6th Edition):

Shen, T. (2018). Context Learning and Weakly Supervised Learning for Semantic Segmentation. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Thesis, University of Adelaide. Accessed January 28, 2021. http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Web. 28 Jan 2021.

Vancouver:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

2. Bai, Qian (author). Semantic Segmentation of AHN3 Point Clouds with DGCNN.

Degree: 2020, Delft University of Technology

Semantic segmentation of aerial point clouds with high accuracy is significant for many geographical applications, but is not trivial since the data is massive and… (more)

Subjects/Keywords: Point Cloud; Semantic segmentation; DGCNN

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APA (6th Edition):

Bai, Q. (. (2020). Semantic Segmentation of AHN3 Point Clouds with DGCNN. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:492d2981-35ea-4cff-bc5a-eb75d06fc2dc

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bai, Qian (author). “Semantic Segmentation of AHN3 Point Clouds with DGCNN.” 2020. Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:492d2981-35ea-4cff-bc5a-eb75d06fc2dc.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bai, Qian (author). “Semantic Segmentation of AHN3 Point Clouds with DGCNN.” 2020. Web. 28 Jan 2021.

Vancouver:

Bai Q(. Semantic Segmentation of AHN3 Point Clouds with DGCNN. [Internet] [Thesis]. Delft University of Technology; 2020. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:492d2981-35ea-4cff-bc5a-eb75d06fc2dc.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bai Q(. Semantic Segmentation of AHN3 Point Clouds with DGCNN. [Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:492d2981-35ea-4cff-bc5a-eb75d06fc2dc

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Arizona

3. Peng, Kuo-Shiuan. Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation .

Degree: 2019, University of Arizona

Semantic understanding is the foundation of an intelligent system in the field of computer vision. Particularly, the real-time usage of the automation systems, such as… (more)

Subjects/Keywords: depth estimation; semantic object; semantic segmentation; semantic understanding

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APA (6th Edition):

Peng, K. (2019). Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/636671

Chicago Manual of Style (16th Edition):

Peng, Kuo-Shiuan. “Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation .” 2019. Doctoral Dissertation, University of Arizona. Accessed January 28, 2021. http://hdl.handle.net/10150/636671.

MLA Handbook (7th Edition):

Peng, Kuo-Shiuan. “Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation .” 2019. Web. 28 Jan 2021.

Vancouver:

Peng K. Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation . [Internet] [Doctoral dissertation]. University of Arizona; 2019. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10150/636671.

Council of Science Editors:

Peng K. Toward Joint Scene Understanding Using Deep Convolutional Neural Network: Object State, Depth, and Segmentation . [Doctoral Dissertation]. University of Arizona; 2019. Available from: http://hdl.handle.net/10150/636671

4. Zou, Wenbin. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.

Degree: Docteur es, Traitement du signal et de l'image, 2014, Rennes, INSA

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… (more)

Subjects/Keywords: Segmentation d’objets; Segmentation sémantique; Détection de saillance; Object segmentation; Semantic segmentation; Saliency detection; 621.367

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APA (6th Edition):

Zou, W. (2014). Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2014ISAR0007

Chicago Manual of Style (16th Edition):

Zou, Wenbin. “Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.” 2014. Doctoral Dissertation, Rennes, INSA. Accessed January 28, 2021. http://www.theses.fr/2014ISAR0007.

MLA Handbook (7th Edition):

Zou, Wenbin. “Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.” 2014. Web. 28 Jan 2021.

Vancouver:

Zou W. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. [Internet] [Doctoral dissertation]. Rennes, INSA; 2014. [cited 2021 Jan 28]. Available from: http://www.theses.fr/2014ISAR0007.

Council of Science Editors:

Zou W. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. [Doctoral Dissertation]. Rennes, INSA; 2014. Available from: http://www.theses.fr/2014ISAR0007


Oregon State University

5. Roy, Anirban. Semantic Image Segmentation Using Domain Constraints.

Degree: PhD, 2017, Oregon State University

 This dissertation addresses the problem of semantic labeling of image pixels. In the course of our work, we considered different types of semantic labels, including… (more)

Subjects/Keywords: Computer Vision; Semantic Image Segmentation; Deep Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Roy, A. (2017). Semantic Image Segmentation Using Domain Constraints. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61703

Chicago Manual of Style (16th Edition):

Roy, Anirban. “Semantic Image Segmentation Using Domain Constraints.” 2017. Doctoral Dissertation, Oregon State University. Accessed January 28, 2021. http://hdl.handle.net/1957/61703.

MLA Handbook (7th Edition):

Roy, Anirban. “Semantic Image Segmentation Using Domain Constraints.” 2017. Web. 28 Jan 2021.

Vancouver:

Roy A. Semantic Image Segmentation Using Domain Constraints. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/1957/61703.

Council of Science Editors:

Roy A. Semantic Image Segmentation Using Domain Constraints. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61703


Delft University of Technology

6. Ai, Zhiwei (author). Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.

Degree: 2019, Delft University of Technology

Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on… (more)

Subjects/Keywords: Deep Learning; Point Clouds; Semantic Segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ai, Z. (. (2019). Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d

Chicago Manual of Style (16th Edition):

Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.

MLA Handbook (7th Edition):

Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Web. 28 Jan 2021.

Vancouver:

Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.

Council of Science Editors:

Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d


Delft University of Technology

7. van Ramshorst, Arjan (author). Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.

Degree: 2018, Delft University of Technology

Knowledge on adversaries during military missions at sea heavily influences decision making, making identification of unknown vessels an important task. Identification of surrounding vessels based… (more)

Subjects/Keywords: Semantic Segmentation; Deep Learning; Data Augmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Ramshorst, A. (. (2018). Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b

Chicago Manual of Style (16th Edition):

van Ramshorst, Arjan (author). “Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.” 2018. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b.

MLA Handbook (7th Edition):

van Ramshorst, Arjan (author). “Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach.” 2018. Web. 28 Jan 2021.

Vancouver:

van Ramshorst A(. Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b.

Council of Science Editors:

van Ramshorst A(. Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:55de4322-8552-4a2c-84d0-427b2891015b

8. Wagh, Ameya Yatindra. A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors.

Degree: MS, 2019, Worcester Polytechnic Institute

 The task of object detection and pose estimation has widely been done using template matching techniques. However, these algorithms are sensitive to outliers and occlusions,… (more)

Subjects/Keywords: Atlas robots; pose estimation; semantic segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wagh, A. Y. (2019). A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-042419-143553 ; https://digitalcommons.wpi.edu/etd-theses/1287

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wagh, Ameya Yatindra. “A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors.” 2019. Thesis, Worcester Polytechnic Institute. Accessed January 28, 2021. etd-042419-143553 ; https://digitalcommons.wpi.edu/etd-theses/1287.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wagh, Ameya Yatindra. “A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors.” 2019. Web. 28 Jan 2021.

Vancouver:

Wagh AY. A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors. [Internet] [Thesis]. Worcester Polytechnic Institute; 2019. [cited 2021 Jan 28]. Available from: etd-042419-143553 ; https://digitalcommons.wpi.edu/etd-theses/1287.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wagh AY. A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors. [Thesis]. Worcester Polytechnic Institute; 2019. Available from: etd-042419-143553 ; https://digitalcommons.wpi.edu/etd-theses/1287

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Ottawa

9. Kolhatkar, Dhanvin. Real-Time Instance and Semantic Segmentation Using Deep Learning .

Degree: 2020, University of Ottawa

 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… (more)

Subjects/Keywords: Instance segmentation; Semantic segmentation; Deep learning; Real-time; Mask prediction

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APA (6th Edition):

Kolhatkar, D. (2020). Real-Time Instance and Semantic Segmentation Using Deep Learning . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/40616

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kolhatkar, Dhanvin. “Real-Time Instance and Semantic Segmentation Using Deep Learning .” 2020. Thesis, University of Ottawa. Accessed January 28, 2021. http://hdl.handle.net/10393/40616.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kolhatkar, Dhanvin. “Real-Time Instance and Semantic Segmentation Using Deep Learning .” 2020. Web. 28 Jan 2021.

Vancouver:

Kolhatkar D. Real-Time Instance and Semantic Segmentation Using Deep Learning . [Internet] [Thesis]. University of Ottawa; 2020. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10393/40616.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kolhatkar D. Real-Time Instance and Semantic Segmentation Using Deep Learning . [Thesis]. University of Ottawa; 2020. Available from: http://hdl.handle.net/10393/40616

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

10. Coimbra, Danilo Barbosa. Segmentação de cenas em telejornais: uma abordagem multimodal.

Degree: Mestrado, Ciências de Computação e Matemática Computacional, 2011, University of São Paulo

Este trabalho tem como objetivo desenvolver um método de segmentação de cenas em vídeos digitais que trate segmentos semânticamente complexos. Como prova de conceito, é… (more)

Subjects/Keywords: Multimodal scene segmentation; Multimodal video segmentation; Segmentação de cena multimodal; Segmentação de vídeo multimodal; Segmentaçãop semântica; Semantic segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Coimbra, D. B. (2011). Segmentação de cenas em telejornais: uma abordagem multimodal. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28062011-103714/ ;

Chicago Manual of Style (16th Edition):

Coimbra, Danilo Barbosa. “Segmentação de cenas em telejornais: uma abordagem multimodal.” 2011. Masters Thesis, University of São Paulo. Accessed January 28, 2021. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28062011-103714/ ;.

MLA Handbook (7th Edition):

Coimbra, Danilo Barbosa. “Segmentação de cenas em telejornais: uma abordagem multimodal.” 2011. Web. 28 Jan 2021.

Vancouver:

Coimbra DB. Segmentação de cenas em telejornais: uma abordagem multimodal. [Internet] [Masters thesis]. University of São Paulo; 2011. [cited 2021 Jan 28]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28062011-103714/ ;.

Council of Science Editors:

Coimbra DB. Segmentação de cenas em telejornais: uma abordagem multimodal. [Masters Thesis]. University of São Paulo; 2011. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-28062011-103714/ ;


Linköping University

11. Tranell, Victor. Semantic Segmentation of Oblique Views in a 3D-Environment.

Degree: Computer Vision, 2019, Linköping University

  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… (more)

Subjects/Keywords: Semantic segmentation; 3D segmentation; oblique views; multiview segmentation; satellite imagery; convolutional neural networks; Signal Processing; Signalbehandling

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APA (6th Edition):

Tranell, V. (2019). Semantic Segmentation of Oblique Views in a 3D-Environment. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153866

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tranell, Victor. “Semantic Segmentation of Oblique Views in a 3D-Environment.” 2019. Thesis, Linköping University. Accessed January 28, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153866.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tranell, Victor. “Semantic Segmentation of Oblique Views in a 3D-Environment.” 2019. Web. 28 Jan 2021.

Vancouver:

Tranell V. Semantic Segmentation of Oblique Views in a 3D-Environment. [Internet] [Thesis]. Linköping University; 2019. [cited 2021 Jan 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153866.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tranell V. Semantic Segmentation of Oblique Views in a 3D-Environment. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153866

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


UCLA

12. Xia, Fangting. Pose-Guided Human Semantic Part Segmentation.

Degree: Statistics, 2016, UCLA

 Human semantic part segmentation and human pose estimation are two fundamental and complementary tasks in computer vision. The localization of joints in pose estimation can… (more)

Subjects/Keywords: Statistics; Computer science; multi-scale modeling; pose estimation; semantic part segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xia, F. (2016). Pose-Guided Human Semantic Part Segmentation. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/34r7t3d3

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xia, Fangting. “Pose-Guided Human Semantic Part Segmentation.” 2016. Thesis, UCLA. Accessed January 28, 2021. http://www.escholarship.org/uc/item/34r7t3d3.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xia, Fangting. “Pose-Guided Human Semantic Part Segmentation.” 2016. Web. 28 Jan 2021.

Vancouver:

Xia F. Pose-Guided Human Semantic Part Segmentation. [Internet] [Thesis]. UCLA; 2016. [cited 2021 Jan 28]. Available from: http://www.escholarship.org/uc/item/34r7t3d3.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xia F. Pose-Guided Human Semantic Part Segmentation. [Thesis]. UCLA; 2016. Available from: http://www.escholarship.org/uc/item/34r7t3d3

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

13. Karnam, Srivallabha. Self-Supervised Learning for Segmentation using Image Reconstruction.

Degree: MS, Computer Engineering, 2020, Rochester Institute of Technology

  Deep learning is the engine that is piloting tremendous growth in various segments of the industry by consuming valuable fuel called data. We are… (more)

Subjects/Keywords: Classification; Computer vision; Self-supervised learning; Semantic segmentation; Unsupervised learning

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APA (6th Edition):

Karnam, S. (2020). Self-Supervised Learning for Segmentation using Image Reconstruction. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10532

Chicago Manual of Style (16th Edition):

Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed January 28, 2021. https://scholarworks.rit.edu/theses/10532.

MLA Handbook (7th Edition):

Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Web. 28 Jan 2021.

Vancouver:

Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2021 Jan 28]. Available from: https://scholarworks.rit.edu/theses/10532.

Council of Science Editors:

Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10532


University of Waterloo

14. Angus, Matt. Towards Pixel-Level OOD Detection for Semantic Segmentation.

Degree: 2019, University of Waterloo

 There exists wide research surrounding the detection of out of distribution sample for image classification. Safety critical applications, such as autonomous driving, would benefit from… (more)

Subjects/Keywords: Semantic Segmentation; Out of Distribution Detection; Deep Learning; Convolutional Neural Networks

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APA (6th Edition):

Angus, M. (2019). Towards Pixel-Level OOD Detection for Semantic Segmentation. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15004

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Angus, Matt. “Towards Pixel-Level OOD Detection for Semantic Segmentation.” 2019. Thesis, University of Waterloo. Accessed January 28, 2021. http://hdl.handle.net/10012/15004.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Angus, Matt. “Towards Pixel-Level OOD Detection for Semantic Segmentation.” 2019. Web. 28 Jan 2021.

Vancouver:

Angus M. Towards Pixel-Level OOD Detection for Semantic Segmentation. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10012/15004.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Angus M. Towards Pixel-Level OOD Detection for Semantic Segmentation. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/15004

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

15. Muruganandham, Shivaprakash. Semantic Segmentation of Satellite Images using Deep Learning.

Degree: Electrical and Space Engineering, 2016, Luleå University of Technology

  A stark increase in the amount of satellite imagery available in recent years has made the interpretation of this data a challenging problem at… (more)

Subjects/Keywords: Satellite Imagery; Deep Learning; Semantic Segmentation; Machine Learning; Urban Growth

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APA (6th Edition):

Muruganandham, S. (2016). Semantic Segmentation of Satellite Images using Deep Learning. (Thesis). Luleå University of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-38558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Muruganandham, Shivaprakash. “Semantic Segmentation of Satellite Images using Deep Learning.” 2016. Thesis, Luleå University of Technology. Accessed January 28, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-38558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Muruganandham, Shivaprakash. “Semantic Segmentation of Satellite Images using Deep Learning.” 2016. Web. 28 Jan 2021.

Vancouver:

Muruganandham S. Semantic Segmentation of Satellite Images using Deep Learning. [Internet] [Thesis]. Luleå University of Technology; 2016. [cited 2021 Jan 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-38558.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Muruganandham S. Semantic Segmentation of Satellite Images using Deep Learning. [Thesis]. Luleå University of Technology; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-38558

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


George Mason University

16. Singh, Gautam. Visual Scene Understanding through Semantic Segmentation .

Degree: 2014, George Mason University

 The problem of visual scene understanding entails recognizing the semantic constituents of a scene and the complex interactions that occur between them. Development of algorithms… (more)

Subjects/Keywords: Computer science; Computer Vision; Machine Learning; Scene Understanding; Semantic Segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Singh, G. (2014). Visual Scene Understanding through Semantic Segmentation . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/9193

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Singh, Gautam. “Visual Scene Understanding through Semantic Segmentation .” 2014. Thesis, George Mason University. Accessed January 28, 2021. http://hdl.handle.net/1920/9193.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Singh, Gautam. “Visual Scene Understanding through Semantic Segmentation .” 2014. Web. 28 Jan 2021.

Vancouver:

Singh G. Visual Scene Understanding through Semantic Segmentation . [Internet] [Thesis]. George Mason University; 2014. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/1920/9193.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Singh G. Visual Scene Understanding through Semantic Segmentation . [Thesis]. George Mason University; 2014. Available from: http://hdl.handle.net/1920/9193

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. BAYOMI, MOSTAFA MOHAMED. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.

Degree: School of Computer Science & Statistics. Discipline of Computer Science, 2019, Trinity College Dublin

 The volume of digital content resources written as text documents is growing every day, at an unprecedented rate. Because this content is generally not structured… (more)

Subjects/Keywords: Natural Language Processing; Text Segmentation; Semantic Analysis; Adaptive Systems

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APA (6th Edition):

BAYOMI, M. M. (2019). Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. (Thesis). Trinity College Dublin. Retrieved from http://hdl.handle.net/2262/86062

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

BAYOMI, MOSTAFA MOHAMED. “Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.” 2019. Thesis, Trinity College Dublin. Accessed January 28, 2021. http://hdl.handle.net/2262/86062.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

BAYOMI, MOSTAFA MOHAMED. “Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.” 2019. Web. 28 Jan 2021.

Vancouver:

BAYOMI MM. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. [Internet] [Thesis]. Trinity College Dublin; 2019. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/2262/86062.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

BAYOMI MM. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. [Thesis]. Trinity College Dublin; 2019. Available from: http://hdl.handle.net/2262/86062

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. BAYOMI, MOSTAFA. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.

Degree: School of Computer Science & Statistics. Discipline of Computer Science, 2019, Trinity College Dublin

 The volume of digital content resources written as text documents is growing every day, at an unprecedented rate. Because this content is generally not structured… (more)

Subjects/Keywords: Natural Language Processing; Text Segmentation; Semantic Analysis; Adaptive Systems

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APA (6th Edition):

BAYOMI, M. (2019). Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. (Thesis). Trinity College Dublin. Retrieved from http://hdl.handle.net/2262/86075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

BAYOMI, MOSTAFA. “Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.” 2019. Thesis, Trinity College Dublin. Accessed January 28, 2021. http://hdl.handle.net/2262/86075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

BAYOMI, MOSTAFA. “Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems.” 2019. Web. 28 Jan 2021.

Vancouver:

BAYOMI M. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. [Internet] [Thesis]. Trinity College Dublin; 2019. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/2262/86075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

BAYOMI M. Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems. [Thesis]. Trinity College Dublin; 2019. Available from: http://hdl.handle.net/2262/86075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

19. Lengyel, Attila (author). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.

Degree: 2019, Delft University of Technology

 This work investigates how prior knowledge from physics-based reflection models can be used to improve the performance of semantic segmentation models under an illumination-based domain… (more)

Subjects/Keywords: Semantic segmentation; color invariants; deep learning; computer vision; domain adaptation

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APA (6th Edition):

Lengyel, A. (. (2019). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a

Chicago Manual of Style (16th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

MLA Handbook (7th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Web. 28 Jan 2021.

Vancouver:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

Council of Science Editors:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a


Delft University of Technology

20. Zhou, Zequn (author). Automated classification of satellite data of informal urban settlements.

Degree: 2019, Delft University of Technology

 Urban areas are rapidly expanding in developing countries. One of goals of the United Nations Human Settlement Programme (UN-Habitat) is to understand and guide urban… (more)

Subjects/Keywords: deep learning; Semantic segmentation; Satellite Imagery; buildng detection

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APA (6th Edition):

Zhou, Z. (. (2019). Automated classification of satellite data of informal urban settlements. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c7c9c170-eb70-4cf2-adfb-1d08bc1b74d7

Chicago Manual of Style (16th Edition):

Zhou, Zequn (author). “Automated classification of satellite data of informal urban settlements.” 2019. Masters Thesis, Delft University of Technology. Accessed January 28, 2021. http://resolver.tudelft.nl/uuid:c7c9c170-eb70-4cf2-adfb-1d08bc1b74d7.

MLA Handbook (7th Edition):

Zhou, Zequn (author). “Automated classification of satellite data of informal urban settlements.” 2019. Web. 28 Jan 2021.

Vancouver:

Zhou Z(. Automated classification of satellite data of informal urban settlements. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 28]. Available from: http://resolver.tudelft.nl/uuid:c7c9c170-eb70-4cf2-adfb-1d08bc1b74d7.

Council of Science Editors:

Zhou Z(. Automated classification of satellite data of informal urban settlements. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c7c9c170-eb70-4cf2-adfb-1d08bc1b74d7


University of Waterloo

21. Li, Ying. Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds.

Degree: 2021, University of Waterloo

 This thesis focuses on the challenges and opportunities that come with deep learning in the extraction of 3D information from point clouds. To achieve this,… (more)

Subjects/Keywords: deep learning; semantic segmentation; object detection; point cloud

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APA (6th Edition):

Li, Y. (2021). Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16620

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Ying. “Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds.” 2021. Thesis, University of Waterloo. Accessed January 28, 2021. http://hdl.handle.net/10012/16620.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Ying. “Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds.” 2021. Web. 28 Jan 2021.

Vancouver:

Li Y. Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds. [Internet] [Thesis]. University of Waterloo; 2021. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10012/16620.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Y. Deep Learning for 3D Information Extraction from Indoor and Outdoor Point Clouds. [Thesis]. University of Waterloo; 2021. Available from: http://hdl.handle.net/10012/16620

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

22. Raza, Syed H. Temporally consistent semantic segmentation in videos.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 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,… (more)

Subjects/Keywords: Semantic segmentation; Temporal consistency; Causality; Videos; Occlusion boundaries; Depth estimation

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APA (6th Edition):

Raza, S. H. (2014). Temporally consistent semantic segmentation in videos. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53455

Chicago Manual of Style (16th Edition):

Raza, Syed H. “Temporally consistent semantic segmentation in videos.” 2014. Doctoral Dissertation, Georgia Tech. Accessed January 28, 2021. http://hdl.handle.net/1853/53455.

MLA Handbook (7th Edition):

Raza, Syed H. “Temporally consistent semantic segmentation in videos.” 2014. Web. 28 Jan 2021.

Vancouver:

Raza SH. Temporally consistent semantic segmentation in videos. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/1853/53455.

Council of Science Editors:

Raza SH. Temporally consistent semantic segmentation in videos. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/53455


University of South Carolina

23. Guo, Dazhou. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.

Degree: PhD, Computer Science and Engineering, 2019, University of South Carolina

  Recently, semantic segmentation – assigning a categorical label to each pixel in an im- age – plays an important role in image understanding applications,… (more)

Subjects/Keywords: Computer Sciences; semantic segmentation; convolutional neural networks; Convolutional Neural Networks Backbones

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APA (6th Edition):

Guo, D. (2019). Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/5600

Chicago Manual of Style (16th Edition):

Guo, Dazhou. “Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.” 2019. Doctoral Dissertation, University of South Carolina. Accessed January 28, 2021. https://scholarcommons.sc.edu/etd/5600.

MLA Handbook (7th Edition):

Guo, Dazhou. “Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing.” 2019. Web. 28 Jan 2021.

Vancouver:

Guo D. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. [Internet] [Doctoral dissertation]. University of South Carolina; 2019. [cited 2021 Jan 28]. Available from: https://scholarcommons.sc.edu/etd/5600.

Council of Science Editors:

Guo D. Semantic Segmentation Considering Image Degradation, Global Context, and Data Balancing. [Doctoral Dissertation]. University of South Carolina; 2019. Available from: https://scholarcommons.sc.edu/etd/5600


Virginia Tech

24. Christie, Gordon A. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.

Degree: PhD, Computer Engineering, 2017, Virginia Tech

 Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g.… (more)

Subjects/Keywords: Scene Understanding; Semantic Segmentation; Unmanned Systems; UAV; UGV; Path Planning

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APA (6th Edition):

Christie, G. A. (2017). Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83807

Chicago Manual of Style (16th Edition):

Christie, Gordon A. “Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.” 2017. Doctoral Dissertation, Virginia Tech. Accessed January 28, 2021. http://hdl.handle.net/10919/83807.

MLA Handbook (7th Edition):

Christie, Gordon A. “Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.” 2017. Web. 28 Jan 2021.

Vancouver:

Christie GA. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10919/83807.

Council of Science Editors:

Christie GA. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/83807


University of Victoria

25. Rose, Spencer. An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography.

Degree: Department of Computer Science, 2020, University of Victoria

 This thesis presents a case study on the application of deep learning methods for the dense prediction of land cover types in oblique ground-based photography.… (more)

Subjects/Keywords: landscape classification; semantic segmentation; change detection; deep learning

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APA (6th Edition):

Rose, S. (2020). An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/12156

Chicago Manual of Style (16th Edition):

Rose, Spencer. “An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography.” 2020. Masters Thesis, University of Victoria. Accessed January 28, 2021. http://hdl.handle.net/1828/12156.

MLA Handbook (7th Edition):

Rose, Spencer. “An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography.” 2020. Web. 28 Jan 2021.

Vancouver:

Rose S. An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography. [Internet] [Masters thesis]. University of Victoria; 2020. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/1828/12156.

Council of Science Editors:

Rose S. An evaluation of deep learning semantic segmentation for land cover classification of oblique ground-based photography. [Masters Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/12156


University of Sydney

26. Weerasiriwardhane, Charika. Multi-Modal Learning For Adaptive Scene Understanding .

Degree: 2017, University of Sydney

 Modern robotics systems typically possess sensors of different modalities. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement… (more)

Subjects/Keywords: conditional random fields; semantic segmentation; adaptive learning; multi-modal scene understanding

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APA (6th Edition):

Weerasiriwardhane, C. (2017). Multi-Modal Learning For Adaptive Scene Understanding . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17191

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Weerasiriwardhane, Charika. “Multi-Modal Learning For Adaptive Scene Understanding .” 2017. Thesis, University of Sydney. Accessed January 28, 2021. http://hdl.handle.net/2123/17191.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Weerasiriwardhane, Charika. “Multi-Modal Learning For Adaptive Scene Understanding .” 2017. Web. 28 Jan 2021.

Vancouver:

Weerasiriwardhane C. Multi-Modal Learning For Adaptive Scene Understanding . [Internet] [Thesis]. University of Sydney; 2017. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/2123/17191.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Weerasiriwardhane C. Multi-Modal Learning For Adaptive Scene Understanding . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17191

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


King Abdullah University of Science and Technology

27. Itani, Hani. A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks.

Degree: 2020, King Abdullah University of Science and Technology

 Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric rep-… (more)

Subjects/Keywords: Deep Learning on point clouds; Local Aggregation Function; Semantic Segmentation

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APA (6th Edition):

Itani, H. (2020). A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/665898

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Itani, Hani. “A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks.” 2020. Thesis, King Abdullah University of Science and Technology. Accessed January 28, 2021. http://hdl.handle.net/10754/665898.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Itani, Hani. “A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks.” 2020. Web. 28 Jan 2021.

Vancouver:

Itani H. A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2020. [cited 2021 Jan 28]. Available from: http://hdl.handle.net/10754/665898.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Itani H. A Closer Look at Neighborhoods in Graph Based Point Cloud Scene Semantic Segmentation Networks. [Thesis]. King Abdullah University of Science and Technology; 2020. Available from: http://hdl.handle.net/10754/665898

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Manchester

28. Gupta, Ananya. Deep Learning for Semantic Feature Extraction in Aerial Imagery.

Degree: 2020, University of Manchester

 Remote sensing provides image and LiDAR data that can be useful for a number of tasks such as disaster mapping and surveying. Deep learning (DL)… (more)

Subjects/Keywords: Deep Learning; Semantic Segmentation; LiDAR; Point Cloud; Satellite Imagery; Aerial Imagery

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gupta, A. (2020). Deep Learning for Semantic Feature Extraction in Aerial Imagery. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:326952

Chicago Manual of Style (16th Edition):

Gupta, Ananya. “Deep Learning for Semantic Feature Extraction in Aerial Imagery.” 2020. Doctoral Dissertation, University of Manchester. Accessed January 28, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:326952.

MLA Handbook (7th Edition):

Gupta, Ananya. “Deep Learning for Semantic Feature Extraction in Aerial Imagery.” 2020. Web. 28 Jan 2021.

Vancouver:

Gupta A. Deep Learning for Semantic Feature Extraction in Aerial Imagery. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2021 Jan 28]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:326952.

Council of Science Editors:

Gupta A. Deep Learning for Semantic Feature Extraction in Aerial Imagery. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:326952

29. Gadde, Raghu Deep. Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images.

Degree: Docteur es, Signal, Image, Automatique, 2017, Université Paris-Est

Cette thèse pour but de développer des méthodes de segmentation pour des scènes fortement structurées (ex. bâtiments et environnements urbains) ou faiblement structurées (ex. paysages… (more)

Subjects/Keywords: Grammar learning; Facade parsing; Facade segmentation; Aemantic segmentation; Cnn; Auto-Context; Grammar learning; Facade parsing; Facade segmentation; Semantic segmentation; Cnn; Auto-Context

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gadde, R. D. (2017). Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2017PESC1083

Chicago Manual of Style (16th Edition):

Gadde, Raghu Deep. “Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images.” 2017. Doctoral Dissertation, Université Paris-Est. Accessed January 28, 2021. http://www.theses.fr/2017PESC1083.

MLA Handbook (7th Edition):

Gadde, Raghu Deep. “Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images.” 2017. Web. 28 Jan 2021.

Vancouver:

Gadde RD. Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images. [Internet] [Doctoral dissertation]. Université Paris-Est; 2017. [cited 2021 Jan 28]. Available from: http://www.theses.fr/2017PESC1083.

Council of Science Editors:

Gadde RD. Segmentation sémantique d'images fortement structurées et faiblement structurées : Semantic Segmentation of Highly Structured and Weakly Structured Images. [Doctoral Dissertation]. Université Paris-Est; 2017. Available from: http://www.theses.fr/2017PESC1083

30. Luc, Pauline. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.

Degree: Docteur es, Mathématiques et informatique, 2019, Université Grenoble Alpes (ComUE)

Les modèles prédictifs ont le potentiel de permettre le transfert des succès récents en apprentissage par renforcement à de nombreuses tâches du monde réel, en… (more)

Subjects/Keywords: Apprentissage profond; Segmentation sémantique; Segmentation d’instance; Modèles génératifs; Apprentissage prédictif; Compréhension vidéo; Deep learning; Semantic segmentation; Instance segmentation; Generative modeling; Predictive learning; Video understanding; 004; 510

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Luc, P. (2019). Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2019GREAM024

Chicago Manual of Style (16th Edition):

Luc, Pauline. “Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed January 28, 2021. http://www.theses.fr/2019GREAM024.

MLA Handbook (7th Edition):

Luc, Pauline. “Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.” 2019. Web. 28 Jan 2021.

Vancouver:

Luc P. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2019. [cited 2021 Jan 28]. Available from: http://www.theses.fr/2019GREAM024.

Council of Science Editors:

Luc P. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2019. Available from: http://www.theses.fr/2019GREAM024

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