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You searched for +publisher:"Delft University of Technology" +contributor:("Khademi, Seyran"). Showing records 1 – 5 of 5 total matches.

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Delft University of Technology

1. Shi, Xiangwei (author). Interpretable Deep Visual Place Recognition.

Degree: 2018, Delft University of Technology

 We propose a framework to interpret deep convolutional models for visual place classification. Given a deep place classification model, our proposed method produces visual explanations… (more)

Subjects/Keywords: Convolutional Neural Networks; Visual Place Recognition; Interpreting Deep Neural Networks

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

APA (6th Edition):

Shi, X. (. (2018). Interpretable Deep Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486

Chicago Manual of Style (16th Edition):

Shi, Xiangwei (author). “Interpretable Deep Visual Place Recognition.” 2018. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486.

MLA Handbook (7th Edition):

Shi, Xiangwei (author). “Interpretable Deep Visual Place Recognition.” 2018. Web. 26 Feb 2021.

Vancouver:

Shi X(. Interpretable Deep Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486.

Council of Science Editors:

Shi X(. Interpretable Deep Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:a5d18d54-eb6b-43f2-8f26-8e7c34e49486


Delft University of Technology

2. Cheng, Zhaoyang (author). Detecting phenological transition dates of vegetation based on multiple deep learning models.

Degree: 2018, Delft University of Technology

Vegetation phenology is the interaction between vegetation activities and ecosystem. Accurate monitoring of vegetation phenology is required to build models and enhance the understanding of… (more)

Subjects/Keywords: Deep Learning; Phenology; Vegetation

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

APA (6th Edition):

Cheng, Z. (. (2018). Detecting phenological transition dates of vegetation based on multiple deep learning models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1abc07cb-2fa3-466d-8e15-21c96d275c91

Chicago Manual of Style (16th Edition):

Cheng, Zhaoyang (author). “Detecting phenological transition dates of vegetation based on multiple deep learning models.” 2018. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:1abc07cb-2fa3-466d-8e15-21c96d275c91.

MLA Handbook (7th Edition):

Cheng, Zhaoyang (author). “Detecting phenological transition dates of vegetation based on multiple deep learning models.” 2018. Web. 26 Feb 2021.

Vancouver:

Cheng Z(. Detecting phenological transition dates of vegetation based on multiple deep learning models. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:1abc07cb-2fa3-466d-8e15-21c96d275c91.

Council of Science Editors:

Cheng Z(. Detecting phenological transition dates of vegetation based on multiple deep learning models. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:1abc07cb-2fa3-466d-8e15-21c96d275c91


Delft University of Technology

3. Garg, Chirag (author). Indoor 3D Reconstruction from a Single Image.

Degree: 2020, Delft University of Technology

3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose… (more)

Subjects/Keywords: 3D Reconstruction; Deep Learning; indoor reconstruction; Piecewise Planar Reconstruction; Point Cloud; 3D Model; Convolutional Neural Networks; Depth reconstruction; Supervised Learning; Indoor environment; Single Image

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

APA (6th Edition):

Garg, C. (. (2020). Indoor 3D Reconstruction from a Single Image. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0

Chicago Manual of Style (16th Edition):

Garg, Chirag (author). “Indoor 3D Reconstruction from a Single Image.” 2020. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0.

MLA Handbook (7th Edition):

Garg, Chirag (author). “Indoor 3D Reconstruction from a Single Image.” 2020. Web. 26 Feb 2021.

Vancouver:

Garg C(. Indoor 3D Reconstruction from a Single Image. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0.

Council of Science Editors:

Garg C(. Indoor 3D Reconstruction from a Single Image. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:70f42eb0-241a-46a2-8bf8-87d89bea26b0


Delft University of Technology

4. Liu, Xin (author). Unsupervised Cross Domain Image Matching with Outlier Detection.

Degree: 2018, Delft University of Technology

This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Outlier Detection

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

APA (6th Edition):

Liu, X. (. (2018). Unsupervised Cross Domain Image Matching with Outlier Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73

Chicago Manual of Style (16th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

MLA Handbook (7th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Web. 26 Feb 2021.

Vancouver:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

Council of Science Editors:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73


Delft University of Technology

5. Li, Jiahui (author). Attention-Aware Age-Agnostic Visual Place Recognition.

Degree: 2019, Delft University of Technology

 A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Attention Mechanism

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

APA (6th Edition):

Li, J. (. (2019). Attention-Aware Age-Agnostic Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

Chicago Manual of Style (16th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Masters Thesis, Delft University of Technology. Accessed February 26, 2021. http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

MLA Handbook (7th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Web. 26 Feb 2021.

Vancouver:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 26]. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

Council of Science Editors:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

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