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Machine Learning for Signal Processing

Robust Matrix Completion via Alternating Projection


Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem.

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19 June 2017 - 11:39pm
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[1] , "Robust Matrix Completion via Alternating Projection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1798. Accessed: Jun. 24, 2017.
@article{1798-17,
url = {http://sigport.org/1798},
author = { },
publisher = {IEEE SigPort},
title = {Robust Matrix Completion via Alternating Projection},
year = {2017} }
TY - EJOUR
T1 - Robust Matrix Completion via Alternating Projection
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1798
ER -
. (2017). Robust Matrix Completion via Alternating Projection. IEEE SigPort. http://sigport.org/1798
, 2017. Robust Matrix Completion via Alternating Projection. Available at: http://sigport.org/1798.
. (2017). "Robust Matrix Completion via Alternating Projection." Web.
1. . Robust Matrix Completion via Alternating Projection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1798

AFFECT RECOGNITION FROM LIP ARTICULATIONS

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23 March 2017 - 1:40pm
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Poster_ICASSP17_Rizwan.pdf

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[1] , "AFFECT RECOGNITION FROM LIP ARTICULATIONS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1781. Accessed: Jun. 24, 2017.
@article{1781-17,
url = {http://sigport.org/1781},
author = { },
publisher = {IEEE SigPort},
title = {AFFECT RECOGNITION FROM LIP ARTICULATIONS},
year = {2017} }
TY - EJOUR
T1 - AFFECT RECOGNITION FROM LIP ARTICULATIONS
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1781
ER -
. (2017). AFFECT RECOGNITION FROM LIP ARTICULATIONS. IEEE SigPort. http://sigport.org/1781
, 2017. AFFECT RECOGNITION FROM LIP ARTICULATIONS. Available at: http://sigport.org/1781.
. (2017). "AFFECT RECOGNITION FROM LIP ARTICULATIONS." Web.
1. . AFFECT RECOGNITION FROM LIP ARTICULATIONS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1781

Disjunctive Normal Shape Boltzmann Machine


Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however, has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper, we propose a new shape model based on Shape Boltzmann Machine and Disjunctive Normal Shape Model which we call Disjunctive Normal Shape Boltzmann Machine (DNSBM).

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Authors:
Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin
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13 March 2017 - 3:59pm
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erdil_ICASSP17_presentation.pdf

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[1] Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin, "Disjunctive Normal Shape Boltzmann Machine", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1760. Accessed: Jun. 24, 2017.
@article{1760-17,
url = {http://sigport.org/1760},
author = {Ertunc Erdil; Fitsum Mesadi; Tolga Tasdizen; Mujdat Cetin },
publisher = {IEEE SigPort},
title = {Disjunctive Normal Shape Boltzmann Machine},
year = {2017} }
TY - EJOUR
T1 - Disjunctive Normal Shape Boltzmann Machine
AU - Ertunc Erdil; Fitsum Mesadi; Tolga Tasdizen; Mujdat Cetin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1760
ER -
Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin. (2017). Disjunctive Normal Shape Boltzmann Machine. IEEE SigPort. http://sigport.org/1760
Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin, 2017. Disjunctive Normal Shape Boltzmann Machine. Available at: http://sigport.org/1760.
Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin. (2017). "Disjunctive Normal Shape Boltzmann Machine." Web.
1. Ertunc Erdil, Fitsum Mesadi, Tolga Tasdizen, Mujdat Cetin. Disjunctive Normal Shape Boltzmann Machine [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1760

A Distributed Constrained-Form Support Vector Machine


Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with their neighbors in the network. Our strategy is based on two algorithms: the Douglas-Rachford algorithm and the projection-gradient method.

Poster.pdf

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Authors:
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross
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9 March 2017 - 12:26pm
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Poster.pdf

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[1] François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, "A Distributed Constrained-Form Support Vector Machine ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1723. Accessed: Jun. 24, 2017.
@article{1723-17,
url = {http://sigport.org/1723},
author = {François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross },
publisher = {IEEE SigPort},
title = {A Distributed Constrained-Form Support Vector Machine },
year = {2017} }
TY - EJOUR
T1 - A Distributed Constrained-Form Support Vector Machine
AU - François D. Côté; Ioannis N. Psaromiligkos; Warren J. Gross
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1723
ER -
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2017). A Distributed Constrained-Form Support Vector Machine . IEEE SigPort. http://sigport.org/1723
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross, 2017. A Distributed Constrained-Form Support Vector Machine . Available at: http://sigport.org/1723.
François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. (2017). "A Distributed Constrained-Form Support Vector Machine ." Web.
1. François D. Côté, Ioannis N. Psaromiligkos, Warren J. Gross. A Distributed Constrained-Form Support Vector Machine [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1723

PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE


Non-negative matrix factorization (NMF) approximates a given matrix as a product of two non-negative matrix factors. Multiplicative algorithms deliver reliable results, but they show slow convergence for high-dimensional data and may be stuck away from local minima. Gradient descent methods have better behavior, but only apply to smooth losses. For non-smooth losses such as the Kullback-Leibler (KL) loss, surprisingly, these methods are lacking.

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Felipe Yanez, Francis Bach
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8 March 2017 - 8:23pm
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First-order method for non-negative matrix factorization with the Kullback-Leibler loss.

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[1] Felipe Yanez, Francis Bach, "PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1714. Accessed: Jun. 24, 2017.
@article{1714-17,
url = {http://sigport.org/1714},
author = {Felipe Yanez; Francis Bach },
publisher = {IEEE SigPort},
title = {PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE},
year = {2017} }
TY - EJOUR
T1 - PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE
AU - Felipe Yanez; Francis Bach
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1714
ER -
Felipe Yanez, Francis Bach. (2017). PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE. IEEE SigPort. http://sigport.org/1714
Felipe Yanez, Francis Bach, 2017. PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE. Available at: http://sigport.org/1714.
Felipe Yanez, Francis Bach. (2017). "PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE." Web.
1. Felipe Yanez, Francis Bach. PRIMAL-DUAL ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION WITH THE KULLBACK-LEIBLER DIVERGENCE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1714

Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms


Ecologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals’ mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations.

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Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro
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8 March 2017 - 7:34pm
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Tomasini - ICASSP 2017 - Poster

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[1] Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro, "Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1713. Accessed: Jun. 24, 2017.
@article{1713-17,
url = {http://sigport.org/1713},
author = {Katrina Smart; Ronaldo Menezes; Mark Bush; Eraldo Ribeiro },
publisher = {IEEE SigPort},
title = {Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms},
year = {2017} }
TY - EJOUR
T1 - Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms
AU - Katrina Smart; Ronaldo Menezes; Mark Bush; Eraldo Ribeiro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1713
ER -
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. (2017). Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms. IEEE SigPort. http://sigport.org/1713
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro, 2017. Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms. Available at: http://sigport.org/1713.
Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. (2017). "Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms." Web.
1. Katrina Smart, Ronaldo Menezes, Mark Bush, Eraldo Ribeiro. Automated Robust Anuran Classification by Extracting Elliptical Feature Pairs from Audio Spectrograms [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1713

A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES


This paper demonstrates the ability to accurately detect the movement state of Madagascar hissing cockroaches equipped with a custom board containing a five degree of freedom inertial measurement unit. The cockroach moves freely through an unobstructed arena while wirelessly transmitting its accelerometer and gyroscope data. Multiple window sizes, features, and classifiers are assessed. An in-depth analysis of the classification results is performed to better understand the strengths and weaknesses of the classifier and feature set.

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Authors:
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton
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5 March 2017 - 11:22pm
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ICASSP2017_final.pptx

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[1] Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton, "A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1644. Accessed: Jun. 24, 2017.
@article{1644-17,
url = {http://sigport.org/1644},
author = {Jeremy Cole; Farrokh Mohammadzadeh; Christopher Bollinger; Tahmid Latif; Alper Bozkurt; and Edgar Lobaton },
publisher = {IEEE SigPort},
title = {A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES},
year = {2017} }
TY - EJOUR
T1 - A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES
AU - Jeremy Cole; Farrokh Mohammadzadeh; Christopher Bollinger; Tahmid Latif; Alper Bozkurt; and Edgar Lobaton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1644
ER -
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. (2017). A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES. IEEE SigPort. http://sigport.org/1644
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton, 2017. A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES. Available at: http://sigport.org/1644.
Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. (2017). "A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES." Web.
1. Jeremy Cole, Farrokh Mohammadzadeh, Christopher Bollinger, Tahmid Latif, Alper Bozkurt, and Edgar Lobaton. A STUDY ON MOTION MODE IDENTIFICATION FOR CYBORG ROACHES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1644

MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION

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Authors:
Afshin Abdi, Faramarz Fekri
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5 March 2017 - 1:36pm
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Stream_poster.pdf

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[1] Afshin Abdi, Faramarz Fekri, "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1638. Accessed: Jun. 24, 2017.
@article{1638-17,
url = {http://sigport.org/1638},
author = {Afshin Abdi; Faramarz Fekri },
publisher = {IEEE SigPort},
title = {MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION},
year = {2017} }
TY - EJOUR
T1 - MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION
AU - Afshin Abdi; Faramarz Fekri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1638
ER -
Afshin Abdi, Faramarz Fekri. (2017). MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. IEEE SigPort. http://sigport.org/1638
Afshin Abdi, Faramarz Fekri, 2017. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION. Available at: http://sigport.org/1638.
Afshin Abdi, Faramarz Fekri. (2017). "MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION." Web.
1. Afshin Abdi, Faramarz Fekri. MIXTURE SOURCE IDENTIFICATION IN NON-STATIONARY DATA STREAMS WITH APPLICATIONS IN COMPRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1638

A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES


Affect prediction is a classical problem and has recently garnered special interest in multimedia applications. Affect prediction in movies is one such domain, potentially aiding the design as well as the impact analysis of movies.Given the large diversity in movies (such as different genres and languages), obtaining a comprehensive movie dataset for modeling affect is challenging while models trained on smaller datasets may not generalize. In this paper, we address the problem of continuous affect ratings with the availability of limited in-domain data resources.

SigPort.zip

Package icon SigPort.zip (58 downloads)

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Authors:
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan
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1 March 2017 - 9:17am
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SigPort.zip

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[1] Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1554. Accessed: Jun. 24, 2017.
@article{1554-17,
url = {http://sigport.org/1554},
author = {Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES},
year = {2017} }
TY - EJOUR
T1 - A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES
AU - Sabyasachee Baruah; Rahul Gupta; Shrikanth Narayanan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1554
ER -
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. IEEE SigPort. http://sigport.org/1554
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan, 2017. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES. Available at: http://sigport.org/1554.
Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. (2017). "A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES." Web.
1. Sabyasachee Baruah, Rahul Gupta, Shrikanth Narayanan. A KNOWLEDGE TRANSFER AND BOOSTING APPROACH TO THE PREDICTION OF AFFECT IN MOVIES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1554

Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification

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28 February 2017 - 4:29am
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ICASSP-Final.pdf

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[1] , "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1492. Accessed: Jun. 24, 2017.
@article{1492-17,
url = {http://sigport.org/1492},
author = { },
publisher = {IEEE SigPort},
title = {Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification},
year = {2017} }
TY - EJOUR
T1 - Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1492
ER -
. (2017). Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. IEEE SigPort. http://sigport.org/1492
, 2017. Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification. Available at: http://sigport.org/1492.
. (2017). "Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification." Web.
1. . Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1492

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