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Image/Video Storage, Retrieval

CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION


This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode de-

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Authors:
Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa
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10 May 2019 - 10:09am
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[1] Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa, "CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4339. Accessed: Sep. 20, 2020.
@article{4339-19,
url = {http://sigport.org/4339},
author = { Yuhei Kaneko;Shogo Muramatsu;Hiroyasu Yasuda;Kiyoshi Hayasaka;Yu Otake;Shunsuke Ono;Masahiro Yukawa },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION},
year = {2019} }
TY - EJOUR
T1 - CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION
AU - Yuhei Kaneko;Shogo Muramatsu;Hiroyasu Yasuda;Kiyoshi Hayasaka;Yu Otake;Shunsuke Ono;Masahiro Yukawa
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4339
ER -
Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa. (2019). CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION. IEEE SigPort. http://sigport.org/4339
Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa, 2019. CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION. Available at: http://sigport.org/4339.
Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa. (2019). "CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION." Web.
1. Yuhei Kaneko,Shogo Muramatsu,Hiroyasu Yasuda,Kiyoshi Hayasaka,Yu Otake,Shunsuke Ono,Masahiro Yukawa. CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4339

Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks


The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is heavily dependent on the learned representation.

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Authors:
Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue
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10 May 2019 - 9:23am
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[1] Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue, "Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4327. Accessed: Sep. 20, 2020.
@article{4327-19,
url = {http://sigport.org/4327},
author = {Yuefeng Chen; Yuhong Li; Tao Xiong; Yuan He; Hui Xue },
publisher = {IEEE SigPort},
title = {Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks},
year = {2019} }
TY - EJOUR
T1 - Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks
AU - Yuefeng Chen; Yuhong Li; Tao Xiong; Yuan He; Hui Xue
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4327
ER -
Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue. (2019). Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks. IEEE SigPort. http://sigport.org/4327
Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue, 2019. Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks. Available at: http://sigport.org/4327.
Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue. (2019). "Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks." Web.
1. Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue. Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4327

Learning Search Path for Region-Level Image Matching

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Authors:
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino
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10 May 2019 - 6:36am
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[1] Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , "Learning Search Path for Region-Level Image Matching", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4300. Accessed: Sep. 20, 2020.
@article{4300-19,
url = {http://sigport.org/4300},
author = {Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino },
publisher = {IEEE SigPort},
title = {Learning Search Path for Region-Level Image Matching},
year = {2019} }
TY - EJOUR
T1 - Learning Search Path for Region-Level Image Matching
AU - Onkar Krishna; Go Irie; Xiaomeng Wu; Takahito Kawanishi; Kunio Kashino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4300
ER -
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). Learning Search Path for Region-Level Image Matching. IEEE SigPort. http://sigport.org/4300
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino , 2019. Learning Search Path for Region-Level Image Matching. Available at: http://sigport.org/4300.
Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . (2019). "Learning Search Path for Region-Level Image Matching." Web.
1. Onkar Krishna, Go Irie, Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino . Learning Search Path for Region-Level Image Matching [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4300

PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING


Guided image filter is one of the most commonly used ways to refine transmission maps. However, since this filter transfers the structures of the guidance image to the filtering output, when the guidance image is the input image itself, even small textures in the input image will cause the change of transmission, which is obviously contrary to the principle that transmission changes only when scene depth changes. In this paper, saliency detection, which simulates the way human eyes work, is introduced into haze removal to tackle the above issue.

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10 May 2019 - 6:30am
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[1] , "PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4299. Accessed: Sep. 20, 2020.
@article{4299-19,
url = {http://sigport.org/4299},
author = { },
publisher = {IEEE SigPort},
title = {PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING},
year = {2019} }
TY - EJOUR
T1 - PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4299
ER -
. (2019). PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING. IEEE SigPort. http://sigport.org/4299
, 2019. PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING. Available at: http://sigport.org/4299.
. (2019). "PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING." Web.
1. . PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4299

Image Reflection Removal Using The Wasserstein Generative Adversarial Network


Imaging through a semi-transparent material such as glass often suffers from the reflection problem, which degrades the image quality. Reflection removal is a challenging task since it is severely ill-posed. Traditional methods, while all require long computation time on minimizing different objective functions with huge matrices, do not necessarily give satisfactory performance. In this paper, we propose a novel deep-learning based method to allow fast removal of reflection.

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Authors:
Tingtian Li, Daniel P.K. Lun
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9 May 2019 - 10:41am
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[1] Tingtian Li, Daniel P.K. Lun, "Image Reflection Removal Using The Wasserstein Generative Adversarial Network", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4190. Accessed: Sep. 20, 2020.
@article{4190-19,
url = {http://sigport.org/4190},
author = {Tingtian Li; Daniel P.K. Lun },
publisher = {IEEE SigPort},
title = {Image Reflection Removal Using The Wasserstein Generative Adversarial Network},
year = {2019} }
TY - EJOUR
T1 - Image Reflection Removal Using The Wasserstein Generative Adversarial Network
AU - Tingtian Li; Daniel P.K. Lun
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4190
ER -
Tingtian Li, Daniel P.K. Lun. (2019). Image Reflection Removal Using The Wasserstein Generative Adversarial Network. IEEE SigPort. http://sigport.org/4190
Tingtian Li, Daniel P.K. Lun, 2019. Image Reflection Removal Using The Wasserstein Generative Adversarial Network. Available at: http://sigport.org/4190.
Tingtian Li, Daniel P.K. Lun. (2019). "Image Reflection Removal Using The Wasserstein Generative Adversarial Network." Web.
1. Tingtian Li, Daniel P.K. Lun. Image Reflection Removal Using The Wasserstein Generative Adversarial Network [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4190

MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION

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Authors:
Lihuo He, Xinbo Gao, Yuanfei Huang
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8 May 2019 - 9:06am
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[1] Lihuo He, Xinbo Gao, Yuanfei Huang, "MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4090. Accessed: Sep. 20, 2020.
@article{4090-19,
url = {http://sigport.org/4090},
author = {Lihuo He; Xinbo Gao; Yuanfei Huang },
publisher = {IEEE SigPort},
title = {MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION},
year = {2019} }
TY - EJOUR
T1 - MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION
AU - Lihuo He; Xinbo Gao; Yuanfei Huang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4090
ER -
Lihuo He, Xinbo Gao, Yuanfei Huang. (2019). MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION. IEEE SigPort. http://sigport.org/4090
Lihuo He, Xinbo Gao, Yuanfei Huang, 2019. MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION. Available at: http://sigport.org/4090.
Lihuo He, Xinbo Gao, Yuanfei Huang. (2019). "MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION." Web.
1. Lihuo He, Xinbo Gao, Yuanfei Huang. MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4090

DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION


Deep convolutional neural networks (CNNs) are nowadays achieving significant leaps in different pattern recognition tasks including action recognition. Current CNNs are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. CNNs also rely on max/average pooling which reduces dimensionality of output layers and hence attenuates their sensitivity to the availability of labeled data.

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Authors:
Ahmed Mazari, Hichem Sahbi
Submitted On:
7 May 2019 - 5:45pm
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[1] Ahmed Mazari, Hichem Sahbi, "DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3961. Accessed: Sep. 20, 2020.
@article{3961-19,
url = {http://sigport.org/3961},
author = {Ahmed Mazari; Hichem Sahbi },
publisher = {IEEE SigPort},
title = {DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION
AU - Ahmed Mazari; Hichem Sahbi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3961
ER -
Ahmed Mazari, Hichem Sahbi. (2019). DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION. IEEE SigPort. http://sigport.org/3961
Ahmed Mazari, Hichem Sahbi, 2019. DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION. Available at: http://sigport.org/3961.
Ahmed Mazari, Hichem Sahbi. (2019). "DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION." Web.
1. Ahmed Mazari, Hichem Sahbi. DEEP TEMPORAL PYRAMID DESIGN FOR ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3961

USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL


With benefits of fast query speed and low storage cost,hashing-based image retrieval approaches have garnered considerable attention from the research community. In this pa-per, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database.

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Authors:
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi
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24 November 2018 - 12:05am
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[1] Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi, "USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3765. Accessed: Sep. 20, 2020.
@article{3765-18,
url = {http://sigport.org/3765},
author = {Veeru Talreja; Fariborz Taherkhani; Matthew C. Valenti; Nasser M. Nasrabadi },
publisher = {IEEE SigPort},
title = {USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL},
year = {2018} }
TY - EJOUR
T1 - USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL
AU - Veeru Talreja; Fariborz Taherkhani; Matthew C. Valenti; Nasser M. Nasrabadi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3765
ER -
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi. (2018). USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/3765
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi, 2018. USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL. Available at: http://sigport.org/3765.
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi. (2018). "USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL." Web.
1. Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti, Nasser M. Nasrabadi. USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3765

FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS


This paper proposes a fast technique for matching a query image to numerous database images under geometric variations in rotation, scale, and translation. Our proposed method extracts the Fourier-Mellin phase features from the images for invariant matching. The online matching process in our method is fast because it directly determines identification based on the correlation value between those features without the geometric alignment.

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Authors:
Toru Takahashi, Kengo Makino, Yuta Kudo
Submitted On:
20 November 2018 - 2:22am
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[1] Toru Takahashi, Kengo Makino, Yuta Kudo , "FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3693. Accessed: Sep. 20, 2020.
@article{3693-18,
url = {http://sigport.org/3693},
author = {Toru Takahashi; Kengo Makino; Yuta Kudo },
publisher = {IEEE SigPort},
title = {FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS},
year = {2018} }
TY - EJOUR
T1 - FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS
AU - Toru Takahashi; Kengo Makino; Yuta Kudo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3693
ER -
Toru Takahashi, Kengo Makino, Yuta Kudo . (2018). FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS. IEEE SigPort. http://sigport.org/3693
Toru Takahashi, Kengo Makino, Yuta Kudo , 2018. FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS. Available at: http://sigport.org/3693.
Toru Takahashi, Kengo Makino, Yuta Kudo . (2018). "FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS." Web.
1. Toru Takahashi, Kengo Makino, Yuta Kudo . FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3693

Model Corrected Low Rank Ptychography


In this paper, we introduce a novel algorithmic framework for sub-diffractive super-resolution imaging of dynamic, time varying targets. We extend recent works in low rank Fourier ptychographic imaging, to incorporate model-correction schemes, which correct for errors propagated due to inaccuracies in fitting an exact low rank model to the target video acquired. Through our algorithm, we are able to demonstrate superior reconstruction quality of video from phaseless Fourier ptychographic measurements, at low sample complexities, as compared to conventional ptychographic setups.

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Authors:
Chinmay Hegde, Namrata Vaswani
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8 October 2018 - 2:21pm
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[1] Chinmay Hegde, Namrata Vaswani, "Model Corrected Low Rank Ptychography", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3628. Accessed: Sep. 20, 2020.
@article{3628-18,
url = {http://sigport.org/3628},
author = {Chinmay Hegde; Namrata Vaswani },
publisher = {IEEE SigPort},
title = {Model Corrected Low Rank Ptychography},
year = {2018} }
TY - EJOUR
T1 - Model Corrected Low Rank Ptychography
AU - Chinmay Hegde; Namrata Vaswani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3628
ER -
Chinmay Hegde, Namrata Vaswani. (2018). Model Corrected Low Rank Ptychography. IEEE SigPort. http://sigport.org/3628
Chinmay Hegde, Namrata Vaswani, 2018. Model Corrected Low Rank Ptychography. Available at: http://sigport.org/3628.
Chinmay Hegde, Namrata Vaswani. (2018). "Model Corrected Low Rank Ptychography." Web.
1. Chinmay Hegde, Namrata Vaswani. Model Corrected Low Rank Ptychography [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3628

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