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You searched for +publisher:"Delft University of Technology" +contributor:("van Gemert, Jan"). Showing records 1 – 30 of 37 total matches.

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Delft University of Technology

1. Batheja, Dhruv (author). Repetition counting in videos using deep learning.

Degree: 2019, Delft University of Technology

This work tackles the problem of repetition counting in videos using modern deep learning techniques. For this task, the intention is to build an end-to-end… (more)

Subjects/Keywords: Deep Learning; Matrix Profile; Backpropagation; LSTM; TCN; Videos; Python; Pytorch; CNN

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APA (6th Edition):

Batheja, D. (. (2019). Repetition counting in videos using deep learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:19ae2a31-5c22-4da0-9627-352a7e66a6b1

Chicago Manual of Style (16th Edition):

Batheja, Dhruv (author). “Repetition counting in videos using deep learning.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:19ae2a31-5c22-4da0-9627-352a7e66a6b1.

MLA Handbook (7th Edition):

Batheja, Dhruv (author). “Repetition counting in videos using deep learning.” 2019. Web. 27 Feb 2021.

Vancouver:

Batheja D(. Repetition counting in videos using deep learning. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:19ae2a31-5c22-4da0-9627-352a7e66a6b1.

Council of Science Editors:

Batheja D(. Repetition counting in videos using deep learning. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:19ae2a31-5c22-4da0-9627-352a7e66a6b1


Delft University of Technology

2. Yang, Hongyu (author). Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning.

Degree: 2018, Delft University of Technology

Occlusion and crossing in Multi-Person Tracking always influence the tracking results. In this paper, we show how deep Re-Identification (ReID), which aims at matching pedestrians… (more)

Subjects/Keywords: Multi-Person Tracking; Occlusion; Scale loss; Person Re-identification

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APA (6th Edition):

Yang, H. (. (2018). Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:047f6900-3021-459c-9b4b-b5eda4a5c4a1

Chicago Manual of Style (16th Edition):

Yang, Hongyu (author). “Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:047f6900-3021-459c-9b4b-b5eda4a5c4a1.

MLA Handbook (7th Edition):

Yang, Hongyu (author). “Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning.” 2018. Web. 27 Feb 2021.

Vancouver:

Yang H(. Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:047f6900-3021-459c-9b4b-b5eda4a5c4a1.

Council of Science Editors:

Yang H(. Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:047f6900-3021-459c-9b4b-b5eda4a5c4a1


Delft University of Technology

3. Li, Xilin (author). Pathological Tremor Detection From Video.

Degree: 2017, Delft University of Technology

A pathological tremor is an involuntary and periodic motion of a body part. The detection and quantification of a pathological tremor are essential for diagnosis… (more)

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APA (6th Edition):

Li, X. (. (2017). Pathological Tremor Detection From Video. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:51469160-2ed5-4df8-a1c5-23e296a13e8d

Chicago Manual of Style (16th Edition):

Li, Xilin (author). “Pathological Tremor Detection From Video.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:51469160-2ed5-4df8-a1c5-23e296a13e8d.

MLA Handbook (7th Edition):

Li, Xilin (author). “Pathological Tremor Detection From Video.” 2017. Web. 27 Feb 2021.

Vancouver:

Li X(. Pathological Tremor Detection From Video. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:51469160-2ed5-4df8-a1c5-23e296a13e8d.

Council of Science Editors:

Li X(. Pathological Tremor Detection From Video. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:51469160-2ed5-4df8-a1c5-23e296a13e8d


Delft University of Technology

4. Sun, Wei (author). Automatic keypoint detecting of wireframe gates.

Degree: 2019, Delft University of Technology

This work applies keypoint detection method to solve gate recognition problem. Unlike regular object detection task, gate recognition problem is made difficult by the fact… (more)

Subjects/Keywords: Deep Learning; Computer Vision; Neural Networks

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APA (6th Edition):

Sun, W. (. (2019). Automatic keypoint detecting of wireframe gates. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f

Chicago Manual of Style (16th Edition):

Sun, Wei (author). “Automatic keypoint detecting of wireframe gates.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f.

MLA Handbook (7th Edition):

Sun, Wei (author). “Automatic keypoint detecting of wireframe gates.” 2019. Web. 27 Feb 2021.

Vancouver:

Sun W(. Automatic keypoint detecting of wireframe gates. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f.

Council of Science Editors:

Sun W(. Automatic keypoint detecting of wireframe gates. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a6d28e5b-89e6-40f3-8ef0-ab0586b6264f


Delft University of Technology

5. Claus, Michele (author). VidCNN - Learning Blind Video Denoising.

Degree: 2018, Delft University of Technology

 We propose a novel Convolutional Neural Network (CNN) for Video Denoising called VidCNN, which is capable to denoise videos without prior knowledge on the noise… (more)

Subjects/Keywords: Deep Learning; Video; Image denoising; Camera; Video Denoising; Convolutional Neural Network

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APA (6th Edition):

Claus, M. (. (2018). VidCNN - Learning Blind Video Denoising. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:993376fe-e181-4c1f-87bf-acd0864d53a9

Chicago Manual of Style (16th Edition):

Claus, Michele (author). “VidCNN - Learning Blind Video Denoising.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:993376fe-e181-4c1f-87bf-acd0864d53a9.

MLA Handbook (7th Edition):

Claus, Michele (author). “VidCNN - Learning Blind Video Denoising.” 2018. Web. 27 Feb 2021.

Vancouver:

Claus M(. VidCNN - Learning Blind Video Denoising. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:993376fe-e181-4c1f-87bf-acd0864d53a9.

Council of Science Editors:

Claus M(. VidCNN - Learning Blind Video Denoising. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:993376fe-e181-4c1f-87bf-acd0864d53a9


Delft University of Technology

6. 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 (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 27, 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. 27 Feb 2021.

Vancouver:

Shi X(. Interpretable Deep Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. 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

7. Zhang, Chengqiu (author). Domain Adaptation networks for noisy image classification.

Degree: 2017, Delft University of Technology

 In this thesis, we propose a novel unsupervised clean-noisy datasets adaptation algorithm based on standard deep learning networks. Specifically, we jointly learn a shared feature… (more)

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APA (6th Edition):

Zhang, C. (. (2017). Domain Adaptation networks for noisy image classification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a62dba04-7e82-4f69-a977-f65563636158

Chicago Manual of Style (16th Edition):

Zhang, Chengqiu (author). “Domain Adaptation networks for noisy image classification.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:a62dba04-7e82-4f69-a977-f65563636158.

MLA Handbook (7th Edition):

Zhang, Chengqiu (author). “Domain Adaptation networks for noisy image classification.” 2017. Web. 27 Feb 2021.

Vancouver:

Zhang C(. Domain Adaptation networks for noisy image classification. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:a62dba04-7e82-4f69-a977-f65563636158.

Council of Science Editors:

Zhang C(. Domain Adaptation networks for noisy image classification. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:a62dba04-7e82-4f69-a977-f65563636158


Delft University of Technology

8. 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 (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 27, 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. 27 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 27]. 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

9. Hommos, Omar (author). Learning Phase-Based Descriptions for Action Recognition.

Degree: 2018, Delft University of Technology

 Action recognition continues to receive significant attention from the research community, with new neural network architectures being developed continuously. Optical flow is by far the… (more)

Subjects/Keywords: Computer Vision; Action Recognition; Deep Learning; Convolutional Neural Networks

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APA (6th Edition):

Hommos, O. (. (2018). Learning Phase-Based Descriptions for Action Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:40a08f3b-5af7-4281-bf0c-9e7e57da6f52

Chicago Manual of Style (16th Edition):

Hommos, Omar (author). “Learning Phase-Based Descriptions for Action Recognition.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:40a08f3b-5af7-4281-bf0c-9e7e57da6f52.

MLA Handbook (7th Edition):

Hommos, Omar (author). “Learning Phase-Based Descriptions for Action Recognition.” 2018. Web. 27 Feb 2021.

Vancouver:

Hommos O(. Learning Phase-Based Descriptions for Action Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:40a08f3b-5af7-4281-bf0c-9e7e57da6f52.

Council of Science Editors:

Hommos O(. Learning Phase-Based Descriptions for Action Recognition. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:40a08f3b-5af7-4281-bf0c-9e7e57da6f52


Delft University of Technology

10. Goes, Sten (author). Learning the scale of image features in Convolutional Neural Networks.

Degree: 2017, Delft University of Technology

The millions of filter weights in Convolutional Neural Networks (CNNs), all have a well-defined and analytical expression for the partial derivative to the loss function.… (more)

Subjects/Keywords: machine learning; deep learning; convolutional neural networks; scale-space; learning feature scale

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APA (6th Edition):

Goes, S. (. (2017). Learning the scale of image features in Convolutional Neural Networks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4ff9b2e3-2679-42f8-80cd-bd2cb884a466

Chicago Manual of Style (16th Edition):

Goes, Sten (author). “Learning the scale of image features in Convolutional Neural Networks.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:4ff9b2e3-2679-42f8-80cd-bd2cb884a466.

MLA Handbook (7th Edition):

Goes, Sten (author). “Learning the scale of image features in Convolutional Neural Networks.” 2017. Web. 27 Feb 2021.

Vancouver:

Goes S(. Learning the scale of image features in Convolutional Neural Networks. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:4ff9b2e3-2679-42f8-80cd-bd2cb884a466.

Council of Science Editors:

Goes S(. Learning the scale of image features in Convolutional Neural Networks. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:4ff9b2e3-2679-42f8-80cd-bd2cb884a466


Delft University of Technology

11. Li, Xin (author). Practical Approaches towards Complete Real-time Gaze Tracking.

Degree: 2019, Delft University of Technology

Visual context plays a key role in many computer vision tasks, and performance of eye/gaze-tracking methods also benefit from it. However, the size of contextual… (more)

Subjects/Keywords: Gaze Estimation; Deep Learning; Computer Vision

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APA (6th Edition):

Li, X. (. (2019). Practical Approaches towards Complete Real-time Gaze Tracking. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fe8a8530-69bb-46f1-99fe-78e75c28c019

Chicago Manual of Style (16th Edition):

Li, Xin (author). “Practical Approaches towards Complete Real-time Gaze Tracking.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:fe8a8530-69bb-46f1-99fe-78e75c28c019.

MLA Handbook (7th Edition):

Li, Xin (author). “Practical Approaches towards Complete Real-time Gaze Tracking.” 2019. Web. 27 Feb 2021.

Vancouver:

Li X(. Practical Approaches towards Complete Real-time Gaze Tracking. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:fe8a8530-69bb-46f1-99fe-78e75c28c019.

Council of Science Editors:

Li X(. Practical Approaches towards Complete Real-time Gaze Tracking. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:fe8a8530-69bb-46f1-99fe-78e75c28c019


Delft University of Technology

12. Napolean, Yeshwanth (author). Estimation of ego-motion velocities from single static images.

Degree: 2018, Delft University of Technology

 Velocity estimation based on visual information is a well- researched topic. Traditional approaches usually rely on how a given feature or features change between successive… (more)

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APA (6th Edition):

Napolean, Y. (. (2018). Estimation of ego-motion velocities from single static images. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131

Chicago Manual of Style (16th Edition):

Napolean, Yeshwanth (author). “Estimation of ego-motion velocities from single static images.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131.

MLA Handbook (7th Edition):

Napolean, Yeshwanth (author). “Estimation of ego-motion velocities from single static images.” 2018. Web. 27 Feb 2021.

Vancouver:

Napolean Y(. Estimation of ego-motion velocities from single static images. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131.

Council of Science Editors:

Napolean Y(. Estimation of ego-motion velocities from single static images. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131


Delft University of Technology

13. Breider, Bas (author). Automatic Recognition of Safety and Performance Related Activities in Motocross.

Degree: 2017, Delft University of Technology

 Motocross is a popular, but dangerous sport: improvements in performance and safety should be made to make it more attractive and less dangerous. By automatically… (more)

Subjects/Keywords: Activity Recognition; Machine Learning; Sport; Motocross

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APA (6th Edition):

Breider, B. (. (2017). Automatic Recognition of Safety and Performance Related Activities in Motocross. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d0c04789-61f5-421a-aaf6-078280c336c7

Chicago Manual of Style (16th Edition):

Breider, Bas (author). “Automatic Recognition of Safety and Performance Related Activities in Motocross.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:d0c04789-61f5-421a-aaf6-078280c336c7.

MLA Handbook (7th Edition):

Breider, Bas (author). “Automatic Recognition of Safety and Performance Related Activities in Motocross.” 2017. Web. 27 Feb 2021.

Vancouver:

Breider B(. Automatic Recognition of Safety and Performance Related Activities in Motocross. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:d0c04789-61f5-421a-aaf6-078280c336c7.

Council of Science Editors:

Breider B(. Automatic Recognition of Safety and Performance Related Activities in Motocross. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:d0c04789-61f5-421a-aaf6-078280c336c7


Delft University of Technology

14. Zheng, Jian (author). Hand-tremor Frequency Estimation in Videos.

Degree: 2018, Delft University of Technology

We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing… (more)

Subjects/Keywords: Video hand-tremor analysis; Phase-based tremor frequency detection; Human tremor datase; Eulerian hand tremors

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APA (6th Edition):

Zheng, J. (. (2018). Hand-tremor Frequency Estimation in Videos. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e32aabd8-6bd4-4dc5-bf82-0508702f67a3

Chicago Manual of Style (16th Edition):

Zheng, Jian (author). “Hand-tremor Frequency Estimation in Videos.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:e32aabd8-6bd4-4dc5-bf82-0508702f67a3.

MLA Handbook (7th Edition):

Zheng, Jian (author). “Hand-tremor Frequency Estimation in Videos.” 2018. Web. 27 Feb 2021.

Vancouver:

Zheng J(. Hand-tremor Frequency Estimation in Videos. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:e32aabd8-6bd4-4dc5-bf82-0508702f67a3.

Council of Science Editors:

Zheng J(. Hand-tremor Frequency Estimation in Videos. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e32aabd8-6bd4-4dc5-bf82-0508702f67a3


Delft University of Technology

15. Ji, Yuanbo (author). Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints.

Degree: 2017, Delft University of Technology

Segmentation of medical images is always a challenge due to complicated anatomical structures and poor image quality. In this paper, aiming to solve dual surfaces… (more)

Subjects/Keywords: Segmentation; Two-layer levelset; Distance constraints

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APA (6th Edition):

Ji, Y. (. (2017). Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ccb9696f-9f6d-4894-b579-34a7fcaaa157

Chicago Manual of Style (16th Edition):

Ji, Yuanbo (author). “Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:ccb9696f-9f6d-4894-b579-34a7fcaaa157.

MLA Handbook (7th Edition):

Ji, Yuanbo (author). “Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints.” 2017. Web. 27 Feb 2021.

Vancouver:

Ji Y(. Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:ccb9696f-9f6d-4894-b579-34a7fcaaa157.

Council of Science Editors:

Ji Y(. Automatic Left Atrial Wall Segmentation from Space MRI via Advanced Two-Layer Level Sets with Distance Constraints. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:ccb9696f-9f6d-4894-b579-34a7fcaaa157


Delft University of Technology

16. Li, Mingxi (author). Efficient Neural Architecture Search for Language Modeling.

Degree: 2019, Delft University of Technology

Neural networks have achieved great success in many difficult learning tasks like image classification, speech recognition and natural language processing. However, neural architectures are hard… (more)

Subjects/Keywords: NAS; Deep learning; Artificial intelligence

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APA (6th Edition):

Li, M. (. (2019). Efficient Neural Architecture Search for Language Modeling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5

Chicago Manual of Style (16th Edition):

Li, Mingxi (author). “Efficient Neural Architecture Search for Language Modeling.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5.

MLA Handbook (7th Edition):

Li, Mingxi (author). “Efficient Neural Architecture Search for Language Modeling.” 2019. Web. 27 Feb 2021.

Vancouver:

Li M(. Efficient Neural Architecture Search for Language Modeling. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5.

Council of Science Editors:

Li M(. Efficient Neural Architecture Search for Language Modeling. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:aa5c948d-43c4-480d-9818-43949c67a3b5


Delft University of Technology

17. 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 February 27, 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. 27 Feb 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 Feb 27]. 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

18. Anand, Kanav (author). Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization.

Degree: 2019, Delft University of Technology

Deep learning is proving to be a useful tool in solving problems from various domains. Despite a rich research activity leading to numerous interesting deep… (more)

Subjects/Keywords: hyperparameter optimization; deep learning; machine learning; user study

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APA (6th Edition):

Anand, K. (. (2019). Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7a9df9fb-5dc4-4d72-a966-45edbb2bc942

Chicago Manual of Style (16th Edition):

Anand, Kanav (author). “Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:7a9df9fb-5dc4-4d72-a966-45edbb2bc942.

MLA Handbook (7th Edition):

Anand, Kanav (author). “Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization.” 2019. Web. 27 Feb 2021.

Vancouver:

Anand K(. Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:7a9df9fb-5dc4-4d72-a966-45edbb2bc942.

Council of Science Editors:

Anand K(. Black Magic in Deep Learning: Understanding the role of humans in hyperparameter optimization. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:7a9df9fb-5dc4-4d72-a966-45edbb2bc942


Delft University of Technology

19. Pathak, Chinmay (author). Exploring normalizing flow for anomaly detection.

Degree: 2019, Delft University of Technology

Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network… (more)

Subjects/Keywords: Anomaly Detection; Outlier detection; Autoencoder; Generative Algorithms; unsupervised learning; one-class classification; GLOW; Normalizing flows

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APA (6th Edition):

Pathak, C. (. (2019). Exploring normalizing flow for anomaly detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852

Chicago Manual of Style (16th Edition):

Pathak, Chinmay (author). “Exploring normalizing flow for anomaly detection.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852.

MLA Handbook (7th Edition):

Pathak, Chinmay (author). “Exploring normalizing flow for anomaly detection.” 2019. Web. 27 Feb 2021.

Vancouver:

Pathak C(. Exploring normalizing flow for anomaly detection. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852.

Council of Science Editors:

Pathak C(. Exploring normalizing flow for anomaly detection. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:f5588df3-626d-42cc-8059-32e5bb16d852


Delft University of Technology

20. SHI, XIAOTONG (author). Anomaly detection and diagnosis in ASML event log using attentional LSTM network.

Degree: 2019, Delft University of Technology

In the ASML test system, all activity events of the test are continuously recorded in event logs, and these logs are intended to help people… (more)

Subjects/Keywords: Anomaly Detection; LSTM; Root cause analysis; ASML

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APA (6th Edition):

SHI, X. (. (2019). Anomaly detection and diagnosis in ASML event log using attentional LSTM network. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9

Chicago Manual of Style (16th Edition):

SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.

MLA Handbook (7th Edition):

SHI, XIAOTONG (author). “Anomaly detection and diagnosis in ASML event log using attentional LSTM network.” 2019. Web. 27 Feb 2021.

Vancouver:

SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9.

Council of Science Editors:

SHI X(. Anomaly detection and diagnosis in ASML event log using attentional LSTM network. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:10964ba3-a16e-492b-90de-e5b866f480d9


Delft University of Technology

21. Liu, Yue (author). Efficient Recurrent Residual Networks Improved by Feature Transfer.

Degree: 2017, Delft University of Technology

 Over the past several years, deep and wide neural networks have achieved great success in many tasks. However, in real life applications, because the gains… (more)

Subjects/Keywords: Residual networks; Recurrent networks; Knowledge transfer

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APA (6th Edition):

Liu, Y. (. (2017). Efficient Recurrent Residual Networks Improved by Feature Transfer. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:04a446a8-546c-455d-8344-948c7e3cdff5

Chicago Manual of Style (16th Edition):

Liu, Yue (author). “Efficient Recurrent Residual Networks Improved by Feature Transfer.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:04a446a8-546c-455d-8344-948c7e3cdff5.

MLA Handbook (7th Edition):

Liu, Yue (author). “Efficient Recurrent Residual Networks Improved by Feature Transfer.” 2017. Web. 27 Feb 2021.

Vancouver:

Liu Y(. Efficient Recurrent Residual Networks Improved by Feature Transfer. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:04a446a8-546c-455d-8344-948c7e3cdff5.

Council of Science Editors:

Liu Y(. Efficient Recurrent Residual Networks Improved by Feature Transfer. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:04a446a8-546c-455d-8344-948c7e3cdff5


Delft University of Technology

22. Lelekas, Ioannis (author). Top-Down Networks: A coarse-to-fine reimagination of CNNs.

Degree: 2020, Delft University of Technology

 Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Top-Down; Fine-to-Coarse; Coarse-to-Fine; Adversarial attacks; Adversarial robustness; Gradcam; Object localization

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APA (6th Edition):

Lelekas, I. (. (2020). Top-Down Networks: A coarse-to-fine reimagination of CNNs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Chicago Manual of Style (16th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

MLA Handbook (7th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Web. 27 Feb 2021.

Vancouver:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

Council of Science Editors:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345


Delft University of Technology

23. Radja, Priyanka (author). Dealing with Ties in Rank Correlation.

Degree: 2018, Delft University of Technology

In the field of Information Retrieval (IR), rankings of systems evaluated under different conditions are often compared to each other. This measure of correspondence between… (more)

Subjects/Keywords: Information Retrieval; Rank Correlation; Ties

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APA (6th Edition):

Radja, P. (. (2018). Dealing with Ties in Rank Correlation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a5707829-748b-405c-b99e-0d0ee0e63a38

Chicago Manual of Style (16th Edition):

Radja, Priyanka (author). “Dealing with Ties in Rank Correlation.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:a5707829-748b-405c-b99e-0d0ee0e63a38.

MLA Handbook (7th Edition):

Radja, Priyanka (author). “Dealing with Ties in Rank Correlation.” 2018. Web. 27 Feb 2021.

Vancouver:

Radja P(. Dealing with Ties in Rank Correlation. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:a5707829-748b-405c-b99e-0d0ee0e63a38.

Council of Science Editors:

Radja P(. Dealing with Ties in Rank Correlation. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:a5707829-748b-405c-b99e-0d0ee0e63a38


Delft University of Technology

24. Uijens, Wouter (author). Activating frequencies: Exploring non-linearities in the Fourier domain.

Degree: 2018, Delft University of Technology

 Convolutional Neural Networks (CNNs) are achieving state of the art performance in computer vision. One downside of CNNs is their computational complexity. One way to… (more)

Subjects/Keywords: Machine Learning; Fourier Transform; Activation Function

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APA (6th Edition):

Uijens, W. (. (2018). Activating frequencies: Exploring non-linearities in the Fourier domain. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf

Chicago Manual of Style (16th Edition):

Uijens, Wouter (author). “Activating frequencies: Exploring non-linearities in the Fourier domain.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf.

MLA Handbook (7th Edition):

Uijens, Wouter (author). “Activating frequencies: Exploring non-linearities in the Fourier domain.” 2018. Web. 27 Feb 2021.

Vancouver:

Uijens W(. Activating frequencies: Exploring non-linearities in the Fourier domain. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf.

Council of Science Editors:

Uijens W(. Activating frequencies: Exploring non-linearities in the Fourier domain. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:b6dfdac6-691d-44a4-bacb-645df5cfdeaf


Delft University of Technology

25. Zhong, Shijian (author). Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.

Degree: 2018, Delft University of Technology

The Train Maintenance Scheduling Problem (TMSP) is a real-world problem that aims at complete maintenance tasks of trains by scheduling their activities on a service… (more)

Subjects/Keywords: Train Maintenance Scheduling Problem; Reactive Agent; Supervised Learning; Neural Networks; Tree Search

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APA (6th Edition):

Zhong, S. (. (2018). Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972

Chicago Manual of Style (16th Edition):

Zhong, Shijian (author). “Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972.

MLA Handbook (7th Edition):

Zhong, Shijian (author). “Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search.” 2018. Web. 27 Feb 2021.

Vancouver:

Zhong S(. Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972.

Council of Science Editors:

Zhong S(. Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:71cf86fd-64a7-4bd0-bcbd-e9635727e972


Delft University of Technology

26. Wen, Xiaoming (author). Learning Scale-Aware Optical Flow.

Degree: 2018, Delft University of Technology

 Optical flow is a representation of projected real-world motion of the object between two consecutive images. The optical flow measures the pixel displacement on the… (more)

Subjects/Keywords: Optica Flow; CNN; Scale-Aware; Derivative

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APA (6th Edition):

Wen, X. (. (2018). Learning Scale-Aware Optical Flow. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c

Chicago Manual of Style (16th Edition):

Wen, Xiaoming (author). “Learning Scale-Aware Optical Flow.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c.

MLA Handbook (7th Edition):

Wen, Xiaoming (author). “Learning Scale-Aware Optical Flow.” 2018. Web. 27 Feb 2021.

Vancouver:

Wen X(. Learning Scale-Aware Optical Flow. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c.

Council of Science Editors:

Wen X(. Learning Scale-Aware Optical Flow. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:7e29ed6a-b1d8-490b-bfa0-b576e6e7887c


Delft University of Technology

27. Priadi Teguh Wibowo, Priadi (author). Automatic Running Event Visualization using Video from Multiple Camera.

Degree: 2019, Delft University of Technology

Visualizing runners trajectory from video data is not straightforward because the video data does not contain the explicit information of which runners appear in the… (more)

Subjects/Keywords: Computer Vision; Deep Learning; Visualization; Person Re-identification; Scene Text Recognition

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APA (6th Edition):

Priadi Teguh Wibowo, P. (. (2019). Automatic Running Event Visualization using Video from Multiple Camera. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174

Chicago Manual of Style (16th Edition):

Priadi Teguh Wibowo, Priadi (author). “Automatic Running Event Visualization using Video from Multiple Camera.” 2019. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174.

MLA Handbook (7th Edition):

Priadi Teguh Wibowo, Priadi (author). “Automatic Running Event Visualization using Video from Multiple Camera.” 2019. Web. 27 Feb 2021.

Vancouver:

Priadi Teguh Wibowo P(. Automatic Running Event Visualization using Video from Multiple Camera. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174.

Council of Science Editors:

Priadi Teguh Wibowo P(. Automatic Running Event Visualization using Video from Multiple Camera. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9b246dbe-2708-4aa4-808b-36b92b040174


Delft University of Technology

28. Chandrasekharan Nair, Sreejith (author). Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition.

Degree: 2017, Delft University of Technology

 Speech is a natural way of communicating that does not require us to develop any new skills in order to be able to interact with… (more)

Subjects/Keywords: Speech recognition; Multimodality; Deep neural networks; Reverberation; Occlusion; Phonemes and Visemes

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APA (6th Edition):

Chandrasekharan Nair, S. (. (2017). Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1ba2f2e8-c9f3-4f88-aeb5-4dacac191d1b

Chicago Manual of Style (16th Edition):

Chandrasekharan Nair, Sreejith (author). “Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:1ba2f2e8-c9f3-4f88-aeb5-4dacac191d1b.

MLA Handbook (7th Edition):

Chandrasekharan Nair, Sreejith (author). “Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition.” 2017. Web. 27 Feb 2021.

Vancouver:

Chandrasekharan Nair S(. Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:1ba2f2e8-c9f3-4f88-aeb5-4dacac191d1b.

Council of Science Editors:

Chandrasekharan Nair S(. Limits on Modeling Compensation in Multimodal DNNs for Audio Visual Speech Recognition. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:1ba2f2e8-c9f3-4f88-aeb5-4dacac191d1b


Delft University of Technology

29. Tetteroo, Jonathan (author). Exploring Convolutional Neural Networks on the ρ-VEX architecture.

Degree: 2018, Delft University of Technology

As machine learning algorithms play an ever increasing role in today's technology, more demands are placed on computational hardware to run these algorithms efficiently. In… (more)

Subjects/Keywords: Convolutional Neural Networks; rVEX; Streaming

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APA (6th Edition):

Tetteroo, J. (. (2018). Exploring Convolutional Neural Networks on the ρ-VEX architecture. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5

Chicago Manual of Style (16th Edition):

Tetteroo, Jonathan (author). “Exploring Convolutional Neural Networks on the ρ-VEX architecture.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5.

MLA Handbook (7th Edition):

Tetteroo, Jonathan (author). “Exploring Convolutional Neural Networks on the ρ-VEX architecture.” 2018. Web. 27 Feb 2021.

Vancouver:

Tetteroo J(. Exploring Convolutional Neural Networks on the ρ-VEX architecture. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5.

Council of Science Editors:

Tetteroo J(. Exploring Convolutional Neural Networks on the ρ-VEX architecture. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:39b1653b-cde7-419b-bcd0-8549b6e34db5


Delft University of Technology

30. Ju, Jihong (author). Learn representations in the presence of segmentation label noises.

Degree: 2017, Delft University of Technology

 Training data for segmentation tasks are often available only on a small scale. Transferring learned representations from pre-trained classification models is therefore widely adopted by… (more)

Subjects/Keywords: Transfer learning; Image segmentation; PU learning

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APA (6th Edition):

Ju, J. (. (2017). Learn representations in the presence of segmentation label noises. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:202633b6-4fb1-463a-a29e-b2f3e2402c00

Chicago Manual of Style (16th Edition):

Ju, Jihong (author). “Learn representations in the presence of segmentation label noises.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:202633b6-4fb1-463a-a29e-b2f3e2402c00.

MLA Handbook (7th Edition):

Ju, Jihong (author). “Learn representations in the presence of segmentation label noises.” 2017. Web. 27 Feb 2021.

Vancouver:

Ju J(. Learn representations in the presence of segmentation label noises. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:202633b6-4fb1-463a-a29e-b2f3e2402c00.

Council of Science Editors:

Ju J(. Learn representations in the presence of segmentation label noises. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:202633b6-4fb1-463a-a29e-b2f3e2402c00

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