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Image/Video Processing

Guided Image Filtering with Arbitrary Window Function


In this paper, we propose an extension of guided image filtering to support arbitrary window functions. The guided image filtering is a fast edge-preserving filter based on a local linearity assumption. The filter supports not only image smoothing but also edge enhancement and image interpolation. The guided image filter assumes that an input image is a local linear transformation of a guidance image, and the assumption is supported in a local finite region. For realizing the supposition, the guided image filtering consists of a stack of box filtering.

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Authors:
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata
Submitted On:
14 April 2018 - 5:57pm
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icassp_poster_fukushima.pdf

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[1] Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata, "Guided Image Filtering with Arbitrary Window Function", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2857. Accessed: Jul. 16, 2018.
@article{2857-18,
url = {http://sigport.org/2857},
author = {Norishige Fukushima; Kenjiro Sugimoto; Sei-ichiro Kamata },
publisher = {IEEE SigPort},
title = {Guided Image Filtering with Arbitrary Window Function},
year = {2018} }
TY - EJOUR
T1 - Guided Image Filtering with Arbitrary Window Function
AU - Norishige Fukushima; Kenjiro Sugimoto; Sei-ichiro Kamata
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2857
ER -
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. (2018). Guided Image Filtering with Arbitrary Window Function. IEEE SigPort. http://sigport.org/2857
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata, 2018. Guided Image Filtering with Arbitrary Window Function. Available at: http://sigport.org/2857.
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. (2018). "Guided Image Filtering with Arbitrary Window Function." Web.
1. Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. Guided Image Filtering with Arbitrary Window Function [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2857

MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY


We introduce a model-based reconstruction
framework with deep learned (DL) and smoothness regularization
on manifolds (STORM) priors to recover free
breathing and ungated (FBU) cardiac MRI from highly undersampled
measurements. The DL priors enable us to exploit
the local correlations, while the STORM prior enables
us to make use of the extensive non-local similarities that are
subject dependent. We introduce a novel model-based formulation
that allows the seamless integration of deep learning

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Authors:
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob
Submitted On:
14 April 2018 - 1:52pm
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icassp_poster_final.pptx

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[1] Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2845. Accessed: Jul. 16, 2018.
@article{2845-18,
url = {http://sigport.org/2845},
author = {Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob },
publisher = {IEEE SigPort},
title = {MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY },
year = {2018} }
TY - EJOUR
T1 - MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY
AU - Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2845
ER -
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . IEEE SigPort. http://sigport.org/2845
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, 2018. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . Available at: http://sigport.org/2845.
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ." Web.
1. Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2845

INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION


This paper focuses on face spoofing detection using video. The purpose is to find out the best scheme for this task in the end-to-end learning manner. We investigate 4 different types of structure to fully exploit the raw data in its spatial-temporal domain, which are the pure CNN, CNN with 3D convolu-tion, CNN+LSTM and CNN+Conv-LSTM. Moreover, anoth-er stream built on optical flow is also used, and with a proper fusion method, it can improve the accuracy. In experiments, we compare schemes on the raw data in single stream and fusion methods with optical flow in two streams.

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Authors:
Zhonglin Sun, Li Sun, Qingli Li
Submitted On:
14 April 2018 - 1:35pm
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icassp_template.pdf

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[1] Zhonglin Sun, Li Sun, Qingli Li, "INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2844. Accessed: Jul. 16, 2018.
@article{2844-18,
url = {http://sigport.org/2844},
author = {Zhonglin Sun; Li Sun; Qingli Li },
publisher = {IEEE SigPort},
title = {INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION},
year = {2018} }
TY - EJOUR
T1 - INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION
AU - Zhonglin Sun; Li Sun; Qingli Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2844
ER -
Zhonglin Sun, Li Sun, Qingli Li. (2018). INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION. IEEE SigPort. http://sigport.org/2844
Zhonglin Sun, Li Sun, Qingli Li, 2018. INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION. Available at: http://sigport.org/2844.
Zhonglin Sun, Li Sun, Qingli Li. (2018). "INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION." Web.
1. Zhonglin Sun, Li Sun, Qingli Li. INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2844

FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING

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Authors:
Jenq-Neng Hwang
Submitted On:
14 April 2018 - 9:58am
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poster_2018.pdf

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[1] Jenq-Neng Hwang, "FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2832. Accessed: Jul. 16, 2018.
@article{2832-18,
url = {http://sigport.org/2832},
author = {Jenq-Neng Hwang },
publisher = {IEEE SigPort},
title = {FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING},
year = {2018} }
TY - EJOUR
T1 - FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING
AU - Jenq-Neng Hwang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2832
ER -
Jenq-Neng Hwang. (2018). FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING. IEEE SigPort. http://sigport.org/2832
Jenq-Neng Hwang, 2018. FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING. Available at: http://sigport.org/2832.
Jenq-Neng Hwang. (2018). "FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING." Web.
1. Jenq-Neng Hwang. FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2832

Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling

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14 April 2018 - 8:38am
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Depth Super-resolution

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[1] , "Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2826. Accessed: Jul. 16, 2018.
@article{2826-18,
url = {http://sigport.org/2826},
author = { },
publisher = {IEEE SigPort},
title = {Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling},
year = {2018} }
TY - EJOUR
T1 - Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2826
ER -
. (2018). Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling. IEEE SigPort. http://sigport.org/2826
, 2018. Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling. Available at: http://sigport.org/2826.
. (2018). "Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling." Web.
1. . Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2826

Unbiased Distance based Non-local Fuzzy Means

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Authors:
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang
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14 April 2018 - 8:00am
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UDNLFM

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[1] Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang, "Unbiased Distance based Non-local Fuzzy Means", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2822. Accessed: Jul. 16, 2018.
@article{2822-18,
url = {http://sigport.org/2822},
author = {Xiaoyao Li; Yicong Zhou; Jing Zhang; Lianhong Wang },
publisher = {IEEE SigPort},
title = {Unbiased Distance based Non-local Fuzzy Means},
year = {2018} }
TY - EJOUR
T1 - Unbiased Distance based Non-local Fuzzy Means
AU - Xiaoyao Li; Yicong Zhou; Jing Zhang; Lianhong Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2822
ER -
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. (2018). Unbiased Distance based Non-local Fuzzy Means. IEEE SigPort. http://sigport.org/2822
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang, 2018. Unbiased Distance based Non-local Fuzzy Means. Available at: http://sigport.org/2822.
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. (2018). "Unbiased Distance based Non-local Fuzzy Means." Web.
1. Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. Unbiased Distance based Non-local Fuzzy Means [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2822

UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION

Paper Details

Authors:
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata
Submitted On:
14 April 2018 - 7:56am
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2018-04-14 ICASSP Poster.pdf

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[1] Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata, "UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2821. Accessed: Jul. 16, 2018.
@article{2821-18,
url = {http://sigport.org/2821},
author = {Kenjiro Sugimoto; Seisuke Kyochi; Sei-ichiro Kamata },
publisher = {IEEE SigPort},
title = {UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION},
year = {2018} }
TY - EJOUR
T1 - UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION
AU - Kenjiro Sugimoto; Seisuke Kyochi; Sei-ichiro Kamata
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2821
ER -
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. (2018). UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION. IEEE SigPort. http://sigport.org/2821
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata, 2018. UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION. Available at: http://sigport.org/2821.
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. (2018). "UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION." Web.
1. Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2821

Fast Vehicle Detection with Lateral Convolutional Neural Network


Fast Vehicle Detection with Lateral Convolutional Neural Network

Paper Details

Authors:
Chen-Hang HE, Kin-Man LAM
Submitted On:
17 April 2018 - 8:58am
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Lateral-CNN

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Lateral-CNN Slides.pptx

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[1] Chen-Hang HE, Kin-Man LAM, "Fast Vehicle Detection with Lateral Convolutional Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2808. Accessed: Jul. 16, 2018.
@article{2808-18,
url = {http://sigport.org/2808},
author = {Chen-Hang HE; Kin-Man LAM },
publisher = {IEEE SigPort},
title = {Fast Vehicle Detection with Lateral Convolutional Neural Network},
year = {2018} }
TY - EJOUR
T1 - Fast Vehicle Detection with Lateral Convolutional Neural Network
AU - Chen-Hang HE; Kin-Man LAM
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2808
ER -
Chen-Hang HE, Kin-Man LAM. (2018). Fast Vehicle Detection with Lateral Convolutional Neural Network. IEEE SigPort. http://sigport.org/2808
Chen-Hang HE, Kin-Man LAM, 2018. Fast Vehicle Detection with Lateral Convolutional Neural Network. Available at: http://sigport.org/2808.
Chen-Hang HE, Kin-Man LAM. (2018). "Fast Vehicle Detection with Lateral Convolutional Neural Network." Web.
1. Chen-Hang HE, Kin-Man LAM. Fast Vehicle Detection with Lateral Convolutional Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2808

FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY


In this paper, we present an approach for detecting faults within seismic volumes using a saliency detection framework that employs a 3D-FFT local spectra and multi-dimensional plane projections. The projection scheme divides a 3D-FFT local spectrum into three distinct components, each depicting variations along different dimensions of the data. To detect seismic structures oriented at different angles and to capture directional features within 3D volume, we modify the center-surround model to incorporate directional comparisons around each voxel.

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Authors:
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche
Submitted On:
13 April 2018 - 10:21pm
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ICASSP2018_SeisSal_Poster.pdf

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[1] Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche, "FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2784. Accessed: Jul. 16, 2018.
@article{2784-18,
url = {http://sigport.org/2784},
author = {Muhammad Amir Shafiq; Zhiling Long; Haibin Di; Ghassan AlRegib; and Mohammed Deriche },
publisher = {IEEE SigPort},
title = {FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY },
year = {2018} }
TY - EJOUR
T1 - FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY
AU - Muhammad Amir Shafiq; Zhiling Long; Haibin Di; Ghassan AlRegib; and Mohammed Deriche
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2784
ER -
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. (2018). FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY . IEEE SigPort. http://sigport.org/2784
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche, 2018. FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY . Available at: http://sigport.org/2784.
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. (2018). "FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY ." Web.
1. Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2784

FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION


Over the past few years, fast and robust trackers based on Kernelized Correlation Filters have shown top notch performance on the Visual Object Tracking challenge. However there is still scope for obtaining higher performance through the use of reasonable approximations that can easily be shown to work through empirical methods. We study some variants derived from the Discriminative Scale Space Tracker and show significant improvement in tracking performance.

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Authors:
Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S
Submitted On:
13 April 2018 - 2:15pm
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Poster

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[1] Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S, "FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2740. Accessed: Jul. 16, 2018.
@article{2740-18,
url = {http://sigport.org/2740},
author = {Ashwani Kumar Tiwari; Rahul Siripurapu; Yadhunandan U S },
publisher = {IEEE SigPort},
title = {FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION},
year = {2018} }
TY - EJOUR
T1 - FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION
AU - Ashwani Kumar Tiwari; Rahul Siripurapu; Yadhunandan U S
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2740
ER -
Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S. (2018). FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION. IEEE SigPort. http://sigport.org/2740
Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S, 2018. FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION. Available at: http://sigport.org/2740.
Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S. (2018). "FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION." Web.
1. Ashwani Kumar Tiwari, Rahul Siripurapu, Yadhunandan U S. FAST ROBUST TRACKING VIA DOUBLE CORRELATION FILTER FORMULATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2740

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