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Image, Video, and Multidimensional Signal Processing

Immersive Optical-See-Through Augmented Reality (Keynote Talk)


Immersive Optical-See-Through Augmented Reality. Augmented Reality has been getting ready for the last 20 years, and is finally becoming real, powered by progress in enabling technologies such as graphics, vision, sensors, and displays. In this talk I’ll provide a personal retrospective on my journey, working on all those enablers, getting ready for the coming AR revolution. At Meta, we are working on immersive optical-see-through AR headset, as well as the full software stack. We’ll discuss the differences of optical vs.

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Authors:
Kari Pulli
Submitted On:
22 December 2017 - 1:30pm
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ICIP_2017_Meta_AR_small.pdf

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[1] Kari Pulli, "Immersive Optical-See-Through Augmented Reality (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2261. Accessed: Apr. 04, 2020.
@article{2261-17,
url = {http://sigport.org/2261},
author = {Kari Pulli },
publisher = {IEEE SigPort},
title = {Immersive Optical-See-Through Augmented Reality (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Immersive Optical-See-Through Augmented Reality (Keynote Talk)
AU - Kari Pulli
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2261
ER -
Kari Pulli. (2017). Immersive Optical-See-Through Augmented Reality (Keynote Talk). IEEE SigPort. http://sigport.org/2261
Kari Pulli, 2017. Immersive Optical-See-Through Augmented Reality (Keynote Talk). Available at: http://sigport.org/2261.
Kari Pulli. (2017). "Immersive Optical-See-Through Augmented Reality (Keynote Talk)." Web.
1. Kari Pulli. Immersive Optical-See-Through Augmented Reality (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2261

Efficient Storage of Images onto DNA Using Vector Quantization


The archiving of digital data is becoming very challenging as conventional electronic devices wear out in time leaving at stake any data that has been stored in them. Therefore, data migration is necessary every 5-10 years. A great percentage of this stored data is "cold", which means that it is very rarely accessed but needs to be safely stored into back-up drives for security and compliance reasons. Unfortunately, the maintenance and replacement of back-up tape drives in big data centers is very expensive both in terms of money and energy.

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Authors:
Melpomeni Dimopoulou, Marc Antonini
Submitted On:
2 April 2020 - 5:05am
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Dimopoulou_DCC_poster.pdf

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[1] Melpomeni Dimopoulou, Marc Antonini, "Efficient Storage of Images onto DNA Using Vector Quantization", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5085. Accessed: Apr. 04, 2020.
@article{5085-20,
url = {http://sigport.org/5085},
author = {Melpomeni Dimopoulou; Marc Antonini },
publisher = {IEEE SigPort},
title = {Efficient Storage of Images onto DNA Using Vector Quantization},
year = {2020} }
TY - EJOUR
T1 - Efficient Storage of Images onto DNA Using Vector Quantization
AU - Melpomeni Dimopoulou; Marc Antonini
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5085
ER -
Melpomeni Dimopoulou, Marc Antonini. (2020). Efficient Storage of Images onto DNA Using Vector Quantization. IEEE SigPort. http://sigport.org/5085
Melpomeni Dimopoulou, Marc Antonini, 2020. Efficient Storage of Images onto DNA Using Vector Quantization. Available at: http://sigport.org/5085.
Melpomeni Dimopoulou, Marc Antonini. (2020). "Efficient Storage of Images onto DNA Using Vector Quantization." Web.
1. Melpomeni Dimopoulou, Marc Antonini. Efficient Storage of Images onto DNA Using Vector Quantization [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5085

MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS

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31 March 2020 - 7:58am
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DCC2020_POSTER_SANCHEZ.pdf

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[1] , "MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5080. Accessed: Apr. 04, 2020.
@article{5080-20,
url = {http://sigport.org/5080},
author = { },
publisher = {IEEE SigPort},
title = {MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS},
year = {2020} }
TY - EJOUR
T1 - MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5080
ER -
. (2020). MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS. IEEE SigPort. http://sigport.org/5080
, 2020. MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS. Available at: http://sigport.org/5080.
. (2020). "MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS." Web.
1. . MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5080

Implicit Geometry Partition for Point Cloud Compression

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Authors:
Xiang Zhang, Wen Gao, Shan Liu
Submitted On:
31 March 2020 - 1:01am
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DCC2020-179-Implicit Geometry Partition for Point Cloud Compression.pdf

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[1] Xiang Zhang, Wen Gao, Shan Liu, "Implicit Geometry Partition for Point Cloud Compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5065. Accessed: Apr. 04, 2020.
@article{5065-20,
url = {http://sigport.org/5065},
author = {Xiang Zhang; Wen Gao; Shan Liu },
publisher = {IEEE SigPort},
title = {Implicit Geometry Partition for Point Cloud Compression},
year = {2020} }
TY - EJOUR
T1 - Implicit Geometry Partition for Point Cloud Compression
AU - Xiang Zhang; Wen Gao; Shan Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5065
ER -
Xiang Zhang, Wen Gao, Shan Liu. (2020). Implicit Geometry Partition for Point Cloud Compression. IEEE SigPort. http://sigport.org/5065
Xiang Zhang, Wen Gao, Shan Liu, 2020. Implicit Geometry Partition for Point Cloud Compression. Available at: http://sigport.org/5065.
Xiang Zhang, Wen Gao, Shan Liu. (2020). "Implicit Geometry Partition for Point Cloud Compression." Web.
1. Xiang Zhang, Wen Gao, Shan Liu. Implicit Geometry Partition for Point Cloud Compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5065

Linear Model based Geometry Coding for Lidar Acquired Point Clouds

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Authors:
Xiang Zhang, Wen Gao, Shan Liu
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30 March 2020 - 11:51am
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DCC2020_180_Linear_Model_for_PCC__poster_.pdf

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[1] Xiang Zhang, Wen Gao, Shan Liu, "Linear Model based Geometry Coding for Lidar Acquired Point Clouds", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5064. Accessed: Apr. 04, 2020.
@article{5064-20,
url = {http://sigport.org/5064},
author = {Xiang Zhang; Wen Gao; Shan Liu },
publisher = {IEEE SigPort},
title = {Linear Model based Geometry Coding for Lidar Acquired Point Clouds},
year = {2020} }
TY - EJOUR
T1 - Linear Model based Geometry Coding for Lidar Acquired Point Clouds
AU - Xiang Zhang; Wen Gao; Shan Liu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5064
ER -
Xiang Zhang, Wen Gao, Shan Liu. (2020). Linear Model based Geometry Coding for Lidar Acquired Point Clouds. IEEE SigPort. http://sigport.org/5064
Xiang Zhang, Wen Gao, Shan Liu, 2020. Linear Model based Geometry Coding for Lidar Acquired Point Clouds. Available at: http://sigport.org/5064.
Xiang Zhang, Wen Gao, Shan Liu. (2020). "Linear Model based Geometry Coding for Lidar Acquired Point Clouds." Web.
1. Xiang Zhang, Wen Gao, Shan Liu. Linear Model based Geometry Coding for Lidar Acquired Point Clouds [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5064

Spectral Video Compression Using Convolutional Sparse Coding

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Authors:
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello
Submitted On:
26 March 2020 - 11:38am
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DCCcbarajas2020v2.pdf

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[1] Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello, "Spectral Video Compression Using Convolutional Sparse Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5034. Accessed: Apr. 04, 2020.
@article{5034-20,
url = {http://sigport.org/5034},
author = {Crisostomo Barajas-Solano; Juan-Pablo Ramirez; Henry Arguello },
publisher = {IEEE SigPort},
title = {Spectral Video Compression Using Convolutional Sparse Coding},
year = {2020} }
TY - EJOUR
T1 - Spectral Video Compression Using Convolutional Sparse Coding
AU - Crisostomo Barajas-Solano; Juan-Pablo Ramirez; Henry Arguello
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5034
ER -
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. (2020). Spectral Video Compression Using Convolutional Sparse Coding. IEEE SigPort. http://sigport.org/5034
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello, 2020. Spectral Video Compression Using Convolutional Sparse Coding. Available at: http://sigport.org/5034.
Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. (2020). "Spectral Video Compression Using Convolutional Sparse Coding." Web.
1. Crisostomo Barajas-Solano, Juan-Pablo Ramirez, Henry Arguello. Spectral Video Compression Using Convolutional Sparse Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5034

Luma Mapping with Chroma Scaling in Versatile Video Coding


This paper describes a new video coding tool in the Versatile Video Coding standard (VVC) named as luma mapping with chroma scaling (LMCS). Experimental compression performance results for LMCS and non-normative examples for deriving LMCS parameter values are also provided. LMCS has two main components: 1) a process for mapping input luma code values to a new set of code values for use inside the coding loop; and 2) a luma-dependent process for scaling chroma residue values.

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Authors:
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo
Submitted On:
23 March 2020 - 11:59pm
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DCC2020_ID189_LMCS_present.pdf

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[1] Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo, "Luma Mapping with Chroma Scaling in Versatile Video Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5026. Accessed: Apr. 04, 2020.
@article{5026-20,
url = {http://sigport.org/5026},
author = {Taoran Lu; Peng Yin; Sean McCarthy; Walt Husak; Tao Chen; Edouard Francois; Christophe Chevance; Franck Hiron; Jie Chen; Ru-Ling Liao; Yan Ye; Jiancong Luo },
publisher = {IEEE SigPort},
title = {Luma Mapping with Chroma Scaling in Versatile Video Coding},
year = {2020} }
TY - EJOUR
T1 - Luma Mapping with Chroma Scaling in Versatile Video Coding
AU - Taoran Lu; Peng Yin; Sean McCarthy; Walt Husak; Tao Chen; Edouard Francois; Christophe Chevance; Franck Hiron; Jie Chen; Ru-Ling Liao; Yan Ye; Jiancong Luo
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5026
ER -
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. (2020). Luma Mapping with Chroma Scaling in Versatile Video Coding. IEEE SigPort. http://sigport.org/5026
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo, 2020. Luma Mapping with Chroma Scaling in Versatile Video Coding. Available at: http://sigport.org/5026.
Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. (2020). "Luma Mapping with Chroma Scaling in Versatile Video Coding." Web.
1. Taoran Lu, Peng Yin, Sean McCarthy, Walt Husak, Tao Chen, Edouard Francois, Christophe Chevance, Franck Hiron, Jie Chen, Ru-Ling Liao, Yan Ye, Jiancong Luo. Luma Mapping with Chroma Scaling in Versatile Video Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5026

Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients


Segmenting a document image into text-lines and words finds applications in many research areas of DIA(Document Image Analysis) such as OCR, Word Spotting, and document retrieval. However, carrying out segmentation operation directly in the compressed document images is still an unexplored and challenging research area. Since JPEG is most widely accepted compression algorithm, this research paper attempts to segment a JPEG compressed printed text document image into text-lines and words, without fully decompressing the image.

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Authors:
P Nagabhushan, Watanabe Osamu
Submitted On:
18 March 2020 - 11:51pm
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DCC2020 Paper ID 181

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[1] P Nagabhushan, Watanabe Osamu, "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5001. Accessed: Apr. 04, 2020.
@article{5001-20,
url = {http://sigport.org/5001},
author = {P Nagabhushan; Watanabe Osamu },
publisher = {IEEE SigPort},
title = {Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients},
year = {2020} }
TY - EJOUR
T1 - Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients
AU - P Nagabhushan; Watanabe Osamu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5001
ER -
P Nagabhushan, Watanabe Osamu. (2020). Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. IEEE SigPort. http://sigport.org/5001
P Nagabhushan, Watanabe Osamu, 2020. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients. Available at: http://sigport.org/5001.
P Nagabhushan, Watanabe Osamu. (2020). "Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients." Web.
1. P Nagabhushan, Watanabe Osamu. Segmentation of Text-Lines and Words from JPEG Compressed Printed Text Documents Using DCT Coefficients [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5001

ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION


In the treatment of epilepsy with intracranial electroencephalogram(iEEG), the recognition accuracy is low, and it is
difficult to find the correlation between channels because of the large amount of channel numbers and time series data. In
order to solve these problems, we propose a novel EEG feature prepresentation method for seizure detection based on the
Log Mel-Filterbank energy feature. We propose to adapt the Mel-Filterbank energy to EEG features with logrithm transform

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Authors:
Danyang Wang, Yuchun Fang, Yifan Li
Submitted On:
16 February 2020 - 7:54pm
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Poster for ICASSP2020 Paper4161

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[1] Danyang Wang, Yuchun Fang, Yifan Li, "ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4991. Accessed: Apr. 04, 2020.
@article{4991-20,
url = {http://sigport.org/4991},
author = {Danyang Wang; Yuchun Fang; Yifan Li },
publisher = {IEEE SigPort},
title = {ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION},
year = {2020} }
TY - EJOUR
T1 - ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION
AU - Danyang Wang; Yuchun Fang; Yifan Li
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4991
ER -
Danyang Wang, Yuchun Fang, Yifan Li. (2020). ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION. IEEE SigPort. http://sigport.org/4991
Danyang Wang, Yuchun Fang, Yifan Li, 2020. ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION. Available at: http://sigport.org/4991.
Danyang Wang, Yuchun Fang, Yifan Li. (2020). "ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION." Web.
1. Danyang Wang, Yuchun Fang, Yifan Li. ENHANCE FEATURE REPRESENTATION OF ELECTROENCEPHALOGRAM FOR SEIZURE DETECTION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4991

JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING

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Authors:
Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li
Submitted On:
23 September 2019 - 4:37pm
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icip 2019 poster: multi-view image clustering

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[1] Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li, "JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4819. Accessed: Apr. 04, 2020.
@article{4819-19,
url = {http://sigport.org/4819},
author = {Zhiqiang Lu; Hao Tang; Yan Yan; Songhao Zhu; Xiao-Yuan Jing; Zuoyong Li },
publisher = {IEEE SigPort},
title = {JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING},
year = {2019} }
TY - EJOUR
T1 - JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING
AU - Zhiqiang Lu; Hao Tang; Yan Yan; Songhao Zhu; Xiao-Yuan Jing; Zuoyong Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4819
ER -
Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li. (2019). JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING. IEEE SigPort. http://sigport.org/4819
Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li, 2019. JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING. Available at: http://sigport.org/4819.
Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li. (2019). "JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING." Web.
1. Zhiqiang Lu, Hao Tang, Yan Yan, Songhao Zhu, Xiao-Yuan Jing, Zuoyong Li. JOINT LEARNING OF SELF-REPRESENTATION AND INDICATOR FOR MULTI-VIEW IMAGE CLUSTERING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4819

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