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Image, Video, and Multidimensional Signal Processing

Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video


Spatio-Temporal Mid-level Feature Bank

It is a great challenge to perform high level recognition tasks on videos that are poor in quality. In this paper, we propose a new spatio-temporal mid-level (STEM) feature bank for recognizing human actions in low quality videos. The feature bank comprises of a trio of local spatio-temporal features, i.e. shape, motion and textures, which respectively encode structural, dynamic and statistical information in video. These features are encoded into mid-level representations and aggregated to construct STEM.

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Authors:
Saimunur Rahman, John See
Submitted On:
20 March 2016 - 11:22am
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stem_icassp2016.pdf

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[1] Saimunur Rahman, John See, "Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/884. Accessed: Sep. 21, 2017.
@article{884-16,
url = {http://sigport.org/884},
author = {Saimunur Rahman; John See },
publisher = {IEEE SigPort},
title = {Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video},
year = {2016} }
TY - EJOUR
T1 - Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video
AU - Saimunur Rahman; John See
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/884
ER -
Saimunur Rahman, John See. (2016). Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video. IEEE SigPort. http://sigport.org/884
Saimunur Rahman, John See, 2016. Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video. Available at: http://sigport.org/884.
Saimunur Rahman, John See. (2016). "Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video." Web.
1. Saimunur Rahman, John See. Spatio-Temporal Mid-Level Feature Bank for Action Recognition in Low Quality Video [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/884

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Sep. 21, 2017.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Dcitionary Learning for Poisson Compressed Sensing


Imaging techniques involve counting of photons striking a detector. Due to fluctuations in the counting process, the measured photon counts are known to be corrupted by Poisson noise. In this paper, we propose a blind dictionary learning framework for the reconstruction of photographic image data from Poisson corrupted measurements acquired by a \emph{compressive} camera.

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Authors:
Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade
Submitted On:
19 March 2016 - 1:06pm
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ICASSP_final_poster.pdf

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[1] Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade, "Dcitionary Learning for Poisson Compressed Sensing", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/826. Accessed: Sep. 21, 2017.
@article{826-16,
url = {http://sigport.org/826},
author = {Sukanya Patil; Rajbabu Velmurugan and Ajit Rajwade },
publisher = {IEEE SigPort},
title = {Dcitionary Learning for Poisson Compressed Sensing},
year = {2016} }
TY - EJOUR
T1 - Dcitionary Learning for Poisson Compressed Sensing
AU - Sukanya Patil; Rajbabu Velmurugan and Ajit Rajwade
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/826
ER -
Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade. (2016). Dcitionary Learning for Poisson Compressed Sensing. IEEE SigPort. http://sigport.org/826
Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade, 2016. Dcitionary Learning for Poisson Compressed Sensing. Available at: http://sigport.org/826.
Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade. (2016). "Dcitionary Learning for Poisson Compressed Sensing." Web.
1. Sukanya Patil, Rajbabu Velmurugan and Ajit Rajwade. Dcitionary Learning for Poisson Compressed Sensing [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/826

Tensor Completion via Functional Smooth Component Deflation

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Authors:
Tatsuya Yokota, Andrzej Cichocki
Submitted On:
19 March 2016 - 5:46am
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ICASSP2016_v2.pdf

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[1] Tatsuya Yokota, Andrzej Cichocki, "Tensor Completion via Functional Smooth Component Deflation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/795. Accessed: Sep. 21, 2017.
@article{795-16,
url = {http://sigport.org/795},
author = {Tatsuya Yokota; Andrzej Cichocki },
publisher = {IEEE SigPort},
title = {Tensor Completion via Functional Smooth Component Deflation},
year = {2016} }
TY - EJOUR
T1 - Tensor Completion via Functional Smooth Component Deflation
AU - Tatsuya Yokota; Andrzej Cichocki
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/795
ER -
Tatsuya Yokota, Andrzej Cichocki. (2016). Tensor Completion via Functional Smooth Component Deflation. IEEE SigPort. http://sigport.org/795
Tatsuya Yokota, Andrzej Cichocki, 2016. Tensor Completion via Functional Smooth Component Deflation. Available at: http://sigport.org/795.
Tatsuya Yokota, Andrzej Cichocki. (2016). "Tensor Completion via Functional Smooth Component Deflation." Web.
1. Tatsuya Yokota, Andrzej Cichocki. Tensor Completion via Functional Smooth Component Deflation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/795

DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING


Imaging techniques involve counting of photons striking a detector.
Due to fluctuations in the counting process, the measured
photon counts are known to be corrupted by Poisson
noise. In this paper, we propose a blind dictionary learning
framework for the reconstruction of photographic image data
from Poisson corrupted measurements acquired by a compressive
camera. We exploit the inherent non-negativity of the
data by modeling the dictionary as well as the sparse dictionary
coefficients as non-negative entities, and infer these directly

Paper Details

Authors:
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade
Submitted On:
19 March 2016 - 4:43am
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ICASSP_poster.pdf

(429 downloads)

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[1] Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade, "DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/791. Accessed: Sep. 21, 2017.
@article{791-16,
url = {http://sigport.org/791},
author = {Sukanya Patil; Rajbabu Velmurugan; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING},
year = {2016} }
TY - EJOUR
T1 - DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING
AU - Sukanya Patil; Rajbabu Velmurugan; Ajit Rajwade
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/791
ER -
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. (2016). DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING. IEEE SigPort. http://sigport.org/791
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade, 2016. DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING. Available at: http://sigport.org/791.
Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. (2016). "DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING." Web.
1. Sukanya Patil, Rajbabu Velmurugan, Ajit Rajwade. DICTIONARY LEARNING FOR POISSON COMPRESSED SENSING [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/791

CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION


Chroma scaling

Color pixel encoding optimizes the conversion of linear physical values of light into integer values. The efficiency of such encoding methods depends on a trade-off between the bit-depth used and the visible distortion introduced by quantization. This efficiency for different color pixel encoding approaches has been evaluated in literature, without considering the fact that before transmission to the end-user, color encoded content needs to be compressed using a video codec.

Paper Details

Authors:
Mahsa T. Pourazad, Panos Nasiopoulos
Submitted On:
14 March 2016 - 6:58pm
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Poster_ChrominanceScalingforHDRVideoCompression.pdf

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[1] Mahsa T. Pourazad, Panos Nasiopoulos, "CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/682. Accessed: Sep. 21, 2017.
@article{682-16,
url = {http://sigport.org/682},
author = {Mahsa T. Pourazad; Panos Nasiopoulos },
publisher = {IEEE SigPort},
title = {CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION},
year = {2016} }
TY - EJOUR
T1 - CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION
AU - Mahsa T. Pourazad; Panos Nasiopoulos
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/682
ER -
Mahsa T. Pourazad, Panos Nasiopoulos. (2016). CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION. IEEE SigPort. http://sigport.org/682
Mahsa T. Pourazad, Panos Nasiopoulos, 2016. CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION. Available at: http://sigport.org/682.
Mahsa T. Pourazad, Panos Nasiopoulos. (2016). "CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION." Web.
1. Mahsa T. Pourazad, Panos Nasiopoulos. CHROMA SCALING FOR HIGH DYNAMIC RANGE VIDEO COMPRESSION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/682

From Pixels to Information Recent Advances in Visual Search

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Authors:
Bernd Girod
Submitted On:
29 February 2016 - 11:22pm
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GlobalSIP_Orlando_Dec2015-small.pdf

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[1] Bernd Girod, "From Pixels to Information Recent Advances in Visual Search", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/606. Accessed: Sep. 21, 2017.
@article{606-16,
url = {http://sigport.org/606},
author = {Bernd Girod },
publisher = {IEEE SigPort},
title = {From Pixels to Information Recent Advances in Visual Search},
year = {2016} }
TY - EJOUR
T1 - From Pixels to Information Recent Advances in Visual Search
AU - Bernd Girod
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/606
ER -
Bernd Girod. (2016). From Pixels to Information Recent Advances in Visual Search. IEEE SigPort. http://sigport.org/606
Bernd Girod, 2016. From Pixels to Information Recent Advances in Visual Search. Available at: http://sigport.org/606.
Bernd Girod. (2016). "From Pixels to Information Recent Advances in Visual Search." Web.
1. Bernd Girod. From Pixels to Information Recent Advances in Visual Search [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/606

Subjective and Objective Quality Assessment of Tone- Mapped Images

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Authors:
Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman
Submitted On:
23 February 2016 - 1:44pm
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1570165825.pdf

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[1] Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman, " Subjective and Objective Quality Assessment of Tone- Mapped Images", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/556. Accessed: Sep. 21, 2017.
@article{556-15,
url = {http://sigport.org/556},
author = {Akshai Krishna Manchana; Sai Sheetal Chandra; Sumohana Channappayya; Shanmuganathan Raman },
publisher = {IEEE SigPort},
title = { Subjective and Objective Quality Assessment of Tone- Mapped Images},
year = {2015} }
TY - EJOUR
T1 - Subjective and Objective Quality Assessment of Tone- Mapped Images
AU - Akshai Krishna Manchana; Sai Sheetal Chandra; Sumohana Channappayya; Shanmuganathan Raman
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/556
ER -
Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman. (2015). Subjective and Objective Quality Assessment of Tone- Mapped Images. IEEE SigPort. http://sigport.org/556
Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman, 2015. Subjective and Objective Quality Assessment of Tone- Mapped Images. Available at: http://sigport.org/556.
Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman. (2015). " Subjective and Objective Quality Assessment of Tone- Mapped Images." Web.
1. Akshai Krishna Manchana, Sai Sheetal Chandra, Sumohana Channappayya, Shanmuganathan Raman. Subjective and Objective Quality Assessment of Tone- Mapped Images [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/556

Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks

Paper Details

Authors:
Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya
Submitted On:
23 February 2016 - 1:44pm
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1570165667.pdf

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[1] Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya, " Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/555. Accessed: Sep. 21, 2017.
@article{555-15,
url = {http://sigport.org/555},
author = {Vignesh Sankar; K. V. S. N. L. Manasa Priya; Sumohana Channappayya },
publisher = {IEEE SigPort},
title = { Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks},
year = {2015} }
TY - EJOUR
T1 - Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks
AU - Vignesh Sankar; K. V. S. N. L. Manasa Priya; Sumohana Channappayya
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/555
ER -
Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya. (2015). Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/555
Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya, 2015. Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks. Available at: http://sigport.org/555.
Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya. (2015). " Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks." Web.
1. Vignesh Sankar, K. V. S. N. L. Manasa Priya, Sumohana Channappayya. Face Image Quality Assessment for Face Selection in Surveillance Video using Convolutional Neural Networks [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/555

Eccentricity Effect of Motion Silencing on Naturalistic Videos

Paper Details

Authors:
Lawrence K. Cormack, Alan C. Bovik
Submitted On:
23 February 2016 - 1:44pm
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Lark_GlobalSIP_2015.pdf

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[1] Lawrence K. Cormack, Alan C. Bovik, "Eccentricity Effect of Motion Silencing on Naturalistic Videos", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/554. Accessed: Sep. 21, 2017.
@article{554-15,
url = {http://sigport.org/554},
author = {Lawrence K. Cormack; Alan C. Bovik },
publisher = {IEEE SigPort},
title = {Eccentricity Effect of Motion Silencing on Naturalistic Videos},
year = {2015} }
TY - EJOUR
T1 - Eccentricity Effect of Motion Silencing on Naturalistic Videos
AU - Lawrence K. Cormack; Alan C. Bovik
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/554
ER -
Lawrence K. Cormack, Alan C. Bovik. (2015). Eccentricity Effect of Motion Silencing on Naturalistic Videos. IEEE SigPort. http://sigport.org/554
Lawrence K. Cormack, Alan C. Bovik, 2015. Eccentricity Effect of Motion Silencing on Naturalistic Videos. Available at: http://sigport.org/554.
Lawrence K. Cormack, Alan C. Bovik. (2015). "Eccentricity Effect of Motion Silencing on Naturalistic Videos." Web.
1. Lawrence K. Cormack, Alan C. Bovik. Eccentricity Effect of Motion Silencing on Naturalistic Videos [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/554

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