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ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING


In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

Paper Details

Authors:
Pratik Singh, Gayadhar Pradhan
Submitted On:
13 April 2018 - 12:49am
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[1] Pratik Singh, Gayadhar Pradhan, "EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2591. Accessed: Apr. 21, 2018.
@article{2591-18,
url = {http://sigport.org/2591},
author = {Pratik Singh; Gayadhar Pradhan },
publisher = {IEEE SigPort},
title = {EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING},
year = {2018} }
TY - EJOUR
T1 - EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING
AU - Pratik Singh; Gayadhar Pradhan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2591
ER -
Pratik Singh, Gayadhar Pradhan. (2018). EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING. IEEE SigPort. http://sigport.org/2591
Pratik Singh, Gayadhar Pradhan, 2018. EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING. Available at: http://sigport.org/2591.
Pratik Singh, Gayadhar Pradhan. (2018). "EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING." Web.
1. Pratik Singh, Gayadhar Pradhan. EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2591

EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING


In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

Paper Details

Authors:
Pratik Singh, Gayadhar Pradhan
Submitted On:
13 April 2018 - 12:49am
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ICASSP2018_pratik_ppt

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[1] Pratik Singh, Gayadhar Pradhan, "EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2590. Accessed: Apr. 21, 2018.
@article{2590-18,
url = {http://sigport.org/2590},
author = {Pratik Singh; Gayadhar Pradhan },
publisher = {IEEE SigPort},
title = {EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING},
year = {2018} }
TY - EJOUR
T1 - EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING
AU - Pratik Singh; Gayadhar Pradhan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2590
ER -
Pratik Singh, Gayadhar Pradhan. (2018). EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING. IEEE SigPort. http://sigport.org/2590
Pratik Singh, Gayadhar Pradhan, 2018. EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING. Available at: http://sigport.org/2590.
Pratik Singh, Gayadhar Pradhan. (2018). "EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING." Web.
1. Pratik Singh, Gayadhar Pradhan. EXPLORING THE NON-LOCAL SIMILARITY PRESENT IN VARIATIONAL MODE FUNCTIONS FOR EFFECTIVE ECG DENOISING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2590

ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION

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Authors:
Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad
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13 April 2018 - 12:39am
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ICASSP_2018_poster_final.pdf

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[1] Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad, "ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2588. Accessed: Apr. 21, 2018.
@article{2588-18,
url = {http://sigport.org/2588},
author = {Hemant K. Kathania; S. Shahnawazuddin ; Nagaraj Adiga and Waquar Ahmad },
publisher = {IEEE SigPort},
title = {ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION
AU - Hemant K. Kathania; S. Shahnawazuddin ; Nagaraj Adiga and Waquar Ahmad
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2588
ER -
Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad. (2018). ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION. IEEE SigPort. http://sigport.org/2588
Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad, 2018. ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION. Available at: http://sigport.org/2588.
Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad. (2018). "ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION." Web.
1. Hemant K. Kathania, S. Shahnawazuddin , Nagaraj Adiga and Waquar Ahmad. ROLE OF PROSODIC FEATURES ON CHILDREN’S SPEECH RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2588

CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION


The greedy decoding method used in the conventional sequence-to-sequence models is prone to producing a model with a compounding
of errors, mainly because it makes inferences in a fixed order, regardless of whether or not the model’s previous guesses are correct.
We propose a non-sequential greedy decoding method that generalizes the greedy decoding schemes proposed in the past. The proposed
method determines not only which token to consider, but also which position in the output sequence to infer at each inference step.

Paper Details

Authors:
Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park
Submitted On:
13 April 2018 - 12:22am
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NSGD_poster_at_ICASSP2018_v1.1.pdf

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[1] Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park, "CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2586. Accessed: Apr. 21, 2018.
@article{2586-18,
url = {http://sigport.org/2586},
author = {Moon-jung Chae; Kyubyong Park; Jinhyun Bang; Soobin Suh; Jonghyuk Park; Namju Kim; Jonghun Park },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION},
year = {2018} }
TY - EJOUR
T1 - CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION
AU - Moon-jung Chae; Kyubyong Park; Jinhyun Bang; Soobin Suh; Jonghyuk Park; Namju Kim; Jonghun Park
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2586
ER -
Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park. (2018). CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION. IEEE SigPort. http://sigport.org/2586
Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park, 2018. CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION. Available at: http://sigport.org/2586.
Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park. (2018). "CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION." Web.
1. Moon-jung Chae, Kyubyong Park, Jinhyun Bang, Soobin Suh, Jonghyuk Park, Namju Kim, Jonghun Park. CONVOLUTIONAL SEQUENCE TO SEQUENCE MODEL WITH NON-SEQUENTIAL GREEDY DECODING FOR GRAPHEME TO PHONEME CONVERSION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2586

WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN


Recovering audio-visual synchronization is an important task in the field of visual speech processing.

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Authors:
Toshiki Kikuchi, Yuko Ozasa
Submitted On:
13 April 2018 - 12:19am
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Presentation Slides

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[1] Toshiki Kikuchi, Yuko Ozasa, "WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2585. Accessed: Apr. 21, 2018.
@article{2585-18,
url = {http://sigport.org/2585},
author = {Toshiki Kikuchi; Yuko Ozasa },
publisher = {IEEE SigPort},
title = {WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN},
year = {2018} }
TY - EJOUR
T1 - WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN
AU - Toshiki Kikuchi; Yuko Ozasa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2585
ER -
Toshiki Kikuchi, Yuko Ozasa. (2018). WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN. IEEE SigPort. http://sigport.org/2585
Toshiki Kikuchi, Yuko Ozasa, 2018. WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN. Available at: http://sigport.org/2585.
Toshiki Kikuchi, Yuko Ozasa. (2018). "WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN." Web.
1. Toshiki Kikuchi, Yuko Ozasa. WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2585

COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK


In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2.

Paper Details

Authors:
Sungkyun Chang, Sangkeun Choe, Kyogu Lee
Submitted On:
13 April 2018 - 12:15am
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ICASSP POSTER_수정8.pdf

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[1] Sungkyun Chang, Sangkeun Choe, Kyogu Lee , "COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2584. Accessed: Apr. 21, 2018.
@article{2584-18,
url = {http://sigport.org/2584},
author = {Sungkyun Chang; Sangkeun Choe; Kyogu Lee },
publisher = {IEEE SigPort},
title = {COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK },
year = {2018} }
TY - EJOUR
T1 - COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK
AU - Sungkyun Chang; Sangkeun Choe; Kyogu Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2584
ER -
Sungkyun Chang, Sangkeun Choe, Kyogu Lee . (2018). COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK . IEEE SigPort. http://sigport.org/2584
Sungkyun Chang, Sangkeun Choe, Kyogu Lee , 2018. COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK . Available at: http://sigport.org/2584.
Sungkyun Chang, Sangkeun Choe, Kyogu Lee . (2018). "COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK ." Web.
1. Sungkyun Chang, Sangkeun Choe, Kyogu Lee . COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2584

COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK


In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2.

Paper Details

Authors:
Sungkyun Chang, Sangkeun Choe, Kyogu Lee
Submitted On:
13 April 2018 - 12:15am
Short Link:
Type:
Event:
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Paper Code:
Document Year:
Cite

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ICASSP POSTER_수정8.pdf

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[1] Sungkyun Chang, Sangkeun Choe, Kyogu Lee , "COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2583. Accessed: Apr. 21, 2018.
@article{2583-18,
url = {http://sigport.org/2583},
author = {Sungkyun Chang; Sangkeun Choe; Kyogu Lee },
publisher = {IEEE SigPort},
title = {COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK },
year = {2018} }
TY - EJOUR
T1 - COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK
AU - Sungkyun Chang; Sangkeun Choe; Kyogu Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2583
ER -
Sungkyun Chang, Sangkeun Choe, Kyogu Lee . (2018). COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK . IEEE SigPort. http://sigport.org/2583
Sungkyun Chang, Sangkeun Choe, Kyogu Lee , 2018. COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK . Available at: http://sigport.org/2583.
Sungkyun Chang, Sangkeun Choe, Kyogu Lee . (2018). "COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK ." Web.
1. Sungkyun Chang, Sangkeun Choe, Kyogu Lee . COVER SONG IDENTIFICATION USING SONG-TO-SONG CROSS-SIMILARITY MATRIX WITH CONVOLUTIONAL NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2583

NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE

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Authors:
Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen
Submitted On:
13 April 2018 - 12:15am
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Feifan Guan_ICASSP2018_Paper#1018.pdf

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[1] Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen , "NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2581. Accessed: Apr. 21, 2018.
@article{2581-18,
url = {http://sigport.org/2581},
author = {Gangyi Jiang; Yang Song; Mei Yu; Zongju Peng; Fen Chen },
publisher = {IEEE SigPort},
title = {NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE},
year = {2018} }
TY - EJOUR
T1 - NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE
AU - Gangyi Jiang; Yang Song; Mei Yu; Zongju Peng; Fen Chen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2581
ER -
Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen . (2018). NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE. IEEE SigPort. http://sigport.org/2581
Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen , 2018. NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE. Available at: http://sigport.org/2581.
Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen . (2018). "NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE." Web.
1. Gangyi Jiang, Yang Song, Mei Yu, Zongju Peng, Fen Chen . NO-REFERENCE HDR IMAGE QUALITY ASSESSMENT METHOD BASED ON TENSOR SPACE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2581

Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies


We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from ℓq,1 norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination.

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Authors:
Muhammad Naveed Tabassum and Esa Ollila
Submitted On:
13 April 2018 - 12:03am
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[1] Muhammad Naveed Tabassum and Esa Ollila, "Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2580. Accessed: Apr. 21, 2018.
@article{2580-18,
url = {http://sigport.org/2580},
author = {Muhammad Naveed Tabassum and Esa Ollila },
publisher = {IEEE SigPort},
title = {Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies},
year = {2018} }
TY - EJOUR
T1 - Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
AU - Muhammad Naveed Tabassum and Esa Ollila
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2580
ER -
Muhammad Naveed Tabassum and Esa Ollila. (2018). Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies. IEEE SigPort. http://sigport.org/2580
Muhammad Naveed Tabassum and Esa Ollila, 2018. Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies. Available at: http://sigport.org/2580.
Muhammad Naveed Tabassum and Esa Ollila. (2018). "Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies." Web.
1. Muhammad Naveed Tabassum and Esa Ollila. Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2580

SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS


In this paper, we propose single depth image super-resolution using convolutional neural networks (CNN). We adopt CNN to acquire a high-quality edge map from the input low-resolution (LR) depth image. We use the high-quality edge map as the weight of the regularization term in a total variation (TV) model for super-resolution. First, we interpolate the LR depth image using bicubic interpolation and extract its low-quality edge map. Then, we get the high-quality edge map from the low-quality one using CNN.

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Authors:
Cheolkon Jung
Submitted On:
12 April 2018 - 11:53pm
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ICASSP2018poster_Depth_rev_final .pdf

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[1] Cheolkon Jung, "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2577. Accessed: Apr. 21, 2018.
@article{2577-18,
url = {http://sigport.org/2577},
author = {Cheolkon Jung },
publisher = {IEEE SigPort},
title = {SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS },
year = {2018} }
TY - EJOUR
T1 - SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS
AU - Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2577
ER -
Cheolkon Jung. (2018). SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . IEEE SigPort. http://sigport.org/2577
Cheolkon Jung, 2018. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . Available at: http://sigport.org/2577.
Cheolkon Jung. (2018). "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ." Web.
1. Cheolkon Jung. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2577

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