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Pattern recognition and classification (MLR-PATT)

Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance


Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data—each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data.

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Authors:
Honglei Zhang, Moncef Gabbouj
Submitted On:
7 October 2018 - 1:32am
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poster_ICIP_2018.pdf

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[1] Honglei Zhang, Moncef Gabbouj, "Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3582. Accessed: Nov. 16, 2018.
@article{3582-18,
url = {http://sigport.org/3582},
author = {Honglei Zhang; Moncef Gabbouj },
publisher = {IEEE SigPort},
title = {Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance},
year = {2018} }
TY - EJOUR
T1 - Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance
AU - Honglei Zhang; Moncef Gabbouj
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3582
ER -
Honglei Zhang, Moncef Gabbouj. (2018). Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance. IEEE SigPort. http://sigport.org/3582
Honglei Zhang, Moncef Gabbouj, 2018. Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance. Available at: http://sigport.org/3582.
Honglei Zhang, Moncef Gabbouj. (2018). "Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance." Web.
1. Honglei Zhang, Moncef Gabbouj. Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3582

Fully Convolutional Siamese Networks for Change Detection


This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images.

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Authors:
Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch
Submitted On:
5 October 2018 - 5:03am
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_ICIP2018__Poster.pdf

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[1] Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, "Fully Convolutional Siamese Networks for Change Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3524. Accessed: Nov. 16, 2018.
@article{3524-18,
url = {http://sigport.org/3524},
author = {Rodrigo Caye Daudt; Bertrand Le Saux; Alexandre Boulch },
publisher = {IEEE SigPort},
title = {Fully Convolutional Siamese Networks for Change Detection},
year = {2018} }
TY - EJOUR
T1 - Fully Convolutional Siamese Networks for Change Detection
AU - Rodrigo Caye Daudt; Bertrand Le Saux; Alexandre Boulch
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3524
ER -
Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. (2018). Fully Convolutional Siamese Networks for Change Detection. IEEE SigPort. http://sigport.org/3524
Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, 2018. Fully Convolutional Siamese Networks for Change Detection. Available at: http://sigport.org/3524.
Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. (2018). "Fully Convolutional Siamese Networks for Change Detection." Web.
1. Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. Fully Convolutional Siamese Networks for Change Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3524

CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS


Noisy labels modeling makes a convolutional neural network (CNN) more robust for the image classification problem. However, current noisy labels modeling methods usually require an expectation-maximization (EM) based procedure to optimize the parameters, which is computationally expensive. In this paper, we utilize a fast annealing training method to speed up the CNN training in every M-step.

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Authors:
Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia
Submitted On:
8 October 2018 - 5:30am
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MA.L1.5_2605_CAT_CNN_NL_v4.pdf

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[1] Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia, "CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3506. Accessed: Nov. 16, 2018.
@article{3506-18,
url = {http://sigport.org/3506},
author = {Jiawei Li; Tao Dai; Qingtao Tang; Yeli Xing; Shu-Tao Xia },
publisher = {IEEE SigPort},
title = {CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS},
year = {2018} }
TY - EJOUR
T1 - CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS
AU - Jiawei Li; Tao Dai; Qingtao Tang; Yeli Xing; Shu-Tao Xia
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3506
ER -
Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia. (2018). CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS. IEEE SigPort. http://sigport.org/3506
Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia, 2018. CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS. Available at: http://sigport.org/3506.
Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia. (2018). "CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS." Web.
1. Jiawei Li, Tao Dai, Qingtao Tang, Yeli Xing, Shu-Tao Xia. CYCLIC ANNEALING TRAINING CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION WITH NOISY LABELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3506

S3D: Stacking Segmental P3D for Action Quality Assessment


Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.

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Authors:
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran
Submitted On:
5 October 2018 - 2:08am
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AI Referee: Score Olympic Games

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[1] Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, "S3D: Stacking Segmental P3D for Action Quality Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3501. Accessed: Nov. 16, 2018.
@article{3501-18,
url = {http://sigport.org/3501},
author = {Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran },
publisher = {IEEE SigPort},
title = {S3D: Stacking Segmental P3D for Action Quality Assessment},
year = {2018} }
TY - EJOUR
T1 - S3D: Stacking Segmental P3D for Action Quality Assessment
AU - Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3501
ER -
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). S3D: Stacking Segmental P3D for Action Quality Assessment. IEEE SigPort. http://sigport.org/3501
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, 2018. S3D: Stacking Segmental P3D for Action Quality Assessment. Available at: http://sigport.org/3501.
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). "S3D: Stacking Segmental P3D for Action Quality Assessment." Web.
1. Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. S3D: Stacking Segmental P3D for Action Quality Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3501

MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK

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Authors:
JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang
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5 October 2018 - 1:53am
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Poster-ICIP-jkg-2.pdf

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[1] JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang, "MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3498. Accessed: Nov. 16, 2018.
@article{3498-18,
url = {http://sigport.org/3498},
author = {JingyangZhang; Kaige Jia; PengshuaiYang; FeiQiao; Qi Wei; XinjunLiu; HuazhongYang },
publisher = {IEEE SigPort},
title = {MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK
AU - JingyangZhang; Kaige Jia; PengshuaiYang; FeiQiao; Qi Wei; XinjunLiu; HuazhongYang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3498
ER -
JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang. (2018). MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK. IEEE SigPort. http://sigport.org/3498
JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang, 2018. MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK. Available at: http://sigport.org/3498.
JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang. (2018). "MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK." Web.
1. JingyangZhang, Kaige Jia, PengshuaiYang, FeiQiao, Qi Wei, XinjunLiu, HuazhongYang. MINTIN: MAXOUT-BASED AND INPUT-NORMALIZED TRANSFORMATION INVARIANT NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3498

DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION


In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights.

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Authors:
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin
Submitted On:
4 October 2018 - 11:16am
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poster_ICIP.pdf

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[1] Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin, "DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3431. Accessed: Nov. 16, 2018.
@article{3431-18,
url = {http://sigport.org/3431},
author = {Fiza Murtaza; Muhammad Haroon Yousaf; Sergio A Velastin },
publisher = {IEEE SigPort},
title = {DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION
AU - Fiza Murtaza; Muhammad Haroon Yousaf; Sergio A Velastin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3431
ER -
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. (2018). DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION. IEEE SigPort. http://sigport.org/3431
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin, 2018. DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION. Available at: http://sigport.org/3431.
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. (2018). "DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION." Web.
1. Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3431

GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES


Prostate cancer is one of the types of cancer with the highest incidence in humans. In particular, prostate cancer is the main cause of death from cancer in men over 70 years of age. The automatic analysis of histological images is nowadays a key factor for helping doctors in the diagnosis task. In this paper, we present granulometries as a novel image descriptor to identify abnormal patterns in the prostatic tissue. The morphological alteration suffered by the main structures of pathological glands are registered by the proposed descriptor and achieved in a feature vector.

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Authors:
Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales
Submitted On:
4 October 2018 - 10:46am
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ICIP_Poster_2018.pdf

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[1] Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales, "GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3425. Accessed: Nov. 16, 2018.
@article{3425-18,
url = {http://sigport.org/3425},
author = {Á. E. Esteban; A. Colomer; V. Naranjo; M. A. Sales },
publisher = {IEEE SigPort},
title = {GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES},
year = {2018} }
TY - EJOUR
T1 - GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES
AU - Á. E. Esteban; A. Colomer; V. Naranjo; M. A. Sales
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3425
ER -
Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales. (2018). GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES. IEEE SigPort. http://sigport.org/3425
Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales, 2018. GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES. Available at: http://sigport.org/3425.
Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales. (2018). "GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES." Web.
1. Á. E. Esteban, A. Colomer, V. Naranjo, M. A. Sales. GRANULOMETRY-BASED DESCRIPTOR FOR PATHOLOGICAL TISSUE DISCRIMINATION IN HISTOPATHOLOGICAL IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3425

KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION

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Authors:
Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao
Submitted On:
4 October 2018 - 10:22am
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2018icipposter2.pdf

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[1] Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao, "KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3422. Accessed: Nov. 16, 2018.
@article{3422-18,
url = {http://sigport.org/3422},
author = {Wang zhikai; Zhang chongyan; Luo wu; Lin weiyao },
publisher = {IEEE SigPort},
title = {KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION
AU - Wang zhikai; Zhang chongyan; Luo wu; Lin weiyao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3422
ER -
Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao. (2018). KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION. IEEE SigPort. http://sigport.org/3422
Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao, 2018. KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION. Available at: http://sigport.org/3422.
Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao. (2018). "KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION." Web.
1. Wang zhikai, Zhang chongyan, Luo wu, Lin weiyao. KEY JOINTS SELECTION AND SPATIOTEMPORAL MINING FOR SKELETON-BASEDACTION RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3422

GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION


Flooding is one of the most harmful natural disasters, as it poses danger to both buildings and human lives. Therefore, it is fundamental to monitor these disasters to define prevention strategies and help authorities in damage control. With the wide use of portable devices (e.g., smartphones), there is an increase of the documentation and communication of flood events in social media. However, the use of these data in monitoring systems is not straightforward and depends on the creation of effective recognition strategies.

Paper Details

Authors:
Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres
Submitted On:
4 October 2018 - 10:03am
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poster_icip_landscape.pdf

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[1] Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres, "GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3413. Accessed: Nov. 16, 2018.
@article{3413-18,
url = {http://sigport.org/3413},
author = {Rafael de O. Werneck; Icaro C. Dourado; Samuel G. Fadel; Salvatore Tabbone; Ricardo da S. Torres },
publisher = {IEEE SigPort},
title = {GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION},
year = {2018} }
TY - EJOUR
T1 - GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION
AU - Rafael de O. Werneck; Icaro C. Dourado; Samuel G. Fadel; Salvatore Tabbone; Ricardo da S. Torres
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3413
ER -
Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres. (2018). GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION. IEEE SigPort. http://sigport.org/3413
Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres, 2018. GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION. Available at: http://sigport.org/3413.
Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres. (2018). "GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION." Web.
1. Rafael de O. Werneck, Icaro C. Dourado, Samuel G. Fadel, Salvatore Tabbone, Ricardo da S. Torres. GRAPH-BASED EARLY-FUSION FOR FLOOD DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3413

Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry


The Viterbi algorithm and its pruning variant, are some of the most frequently used algorithms in communications and speech recognition. There has been extended research on improving the algorithms’ computational complexity, however work trying to interpret their nonlinear structure and geometry has been limited. In this work we analyse the Viterbi algorithm in the field of tropical (min-plus) algebra, and we utilize its pruning variant in order to define a polytope. Then, we interpret certain faces of the polytope as the most probable states of the algorithm.

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Authors:
Petros Maragos
Submitted On:
22 June 2018 - 8:25am
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Poster

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[1] Petros Maragos, "Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3292. Accessed: Nov. 16, 2018.
@article{3292-18,
url = {http://sigport.org/3292},
author = {Petros Maragos },
publisher = {IEEE SigPort},
title = {Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry},
year = {2018} }
TY - EJOUR
T1 - Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry
AU - Petros Maragos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3292
ER -
Petros Maragos. (2018). Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry. IEEE SigPort. http://sigport.org/3292
Petros Maragos, 2018. Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry. Available at: http://sigport.org/3292.
Petros Maragos. (2018). "Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry." Web.
1. Petros Maragos. Analysis of the Viterbi Algorithm Using Tropical Algebra and Geometry [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3292

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