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Image, Video, and Multidimensional Signal Processing

Anomaly Detection in Thermal Images Using Deep Neural Networks

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Authors:
Cai Lile, Li Yiqun
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15 September 2017 - 1:53am
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[1] Cai Lile, Li Yiqun, "Anomaly Detection in Thermal Images Using Deep Neural Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2085. Accessed: Nov. 19, 2018.
@article{2085-17,
url = {http://sigport.org/2085},
author = {Cai Lile; Li Yiqun },
publisher = {IEEE SigPort},
title = {Anomaly Detection in Thermal Images Using Deep Neural Networks},
year = {2017} }
TY - EJOUR
T1 - Anomaly Detection in Thermal Images Using Deep Neural Networks
AU - Cai Lile; Li Yiqun
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2085
ER -
Cai Lile, Li Yiqun. (2017). Anomaly Detection in Thermal Images Using Deep Neural Networks. IEEE SigPort. http://sigport.org/2085
Cai Lile, Li Yiqun, 2017. Anomaly Detection in Thermal Images Using Deep Neural Networks. Available at: http://sigport.org/2085.
Cai Lile, Li Yiqun. (2017). "Anomaly Detection in Thermal Images Using Deep Neural Networks." Web.
1. Cai Lile, Li Yiqun. Anomaly Detection in Thermal Images Using Deep Neural Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2085

ICIP 2017 Poster Paper 3060


Top-down attention plays an important role in guidance of human attention in real-world scenarios, but less efforts in computational modeling of visual attention has been put on it. Inspired by the mechanisms of top-down attention in human visual perception, we propose a multi-layer linear model of top-down attention to modulate bottom-up saliency maps actively. The first layer is a linear regression model which combines the bottom-up saliency maps on various visual features and objects.

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Authors:
Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao
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15 September 2017 - 1:33am
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[1] Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao, "ICIP 2017 Poster Paper 3060", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2083. Accessed: Nov. 19, 2018.
@article{2083-17,
url = {http://sigport.org/2083},
author = {Keng-Teck Ma; Liyuan Li; Peilun Dai; Joo-Hwee Lim; Chenyao Shen; Qi Zhao },
publisher = {IEEE SigPort},
title = {ICIP 2017 Poster Paper 3060},
year = {2017} }
TY - EJOUR
T1 - ICIP 2017 Poster Paper 3060
AU - Keng-Teck Ma; Liyuan Li; Peilun Dai; Joo-Hwee Lim; Chenyao Shen; Qi Zhao
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2083
ER -
Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao. (2017). ICIP 2017 Poster Paper 3060. IEEE SigPort. http://sigport.org/2083
Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao, 2017. ICIP 2017 Poster Paper 3060. Available at: http://sigport.org/2083.
Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao. (2017). "ICIP 2017 Poster Paper 3060." Web.
1. Keng-Teck Ma, Liyuan Li, Peilun Dai, Joo-Hwee Lim, Chenyao Shen, Qi Zhao. ICIP 2017 Poster Paper 3060 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2083

Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization


As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place.

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Authors:
Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer
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14 September 2017 - 10:48pm
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[1] Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer, "Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2074. Accessed: Nov. 19, 2018.
@article{2074-17,
url = {http://sigport.org/2074},
author = {Joel Brogan; Paolo Bestagini; Aparna Bharati; Allan Pinto; Daniel Moreira; Kevin Bowyer; Patrick Flynn; Anderson Rocha; Walter Scheirer },
publisher = {IEEE SigPort},
title = {Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization},
year = {2017} }
TY - EJOUR
T1 - Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
AU - Joel Brogan; Paolo Bestagini; Aparna Bharati; Allan Pinto; Daniel Moreira; Kevin Bowyer; Patrick Flynn; Anderson Rocha; Walter Scheirer
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2074
ER -
Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer. (2017). Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization. IEEE SigPort. http://sigport.org/2074
Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer, 2017. Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization. Available at: http://sigport.org/2074.
Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer. (2017). "Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization." Web.
1. Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer. Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2074

U-Phylogeny: Undirected Provenance Graph Construction in the Wild


Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media. Nonetheless, most recent image phylogeny solutions cannot properly address cases of forged composite images with multiple donors, an area known as multiple parenting phylogeny (MPP). This paper presents a preliminary undirected graph construction solution for MPP, without any strict assumptions.

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Authors:
Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha
Submitted On:
14 September 2017 - 10:24pm
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ICIP17_UPhy_Presentation.pptx

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[1] Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, "U-Phylogeny: Undirected Provenance Graph Construction in the Wild", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2069. Accessed: Nov. 19, 2018.
@article{2069-17,
url = {http://sigport.org/2069},
author = {Aparna Bharati; Daniel Moreira; Allan Pinto; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha },
publisher = {IEEE SigPort},
title = {U-Phylogeny: Undirected Provenance Graph Construction in the Wild},
year = {2017} }
TY - EJOUR
T1 - U-Phylogeny: Undirected Provenance Graph Construction in the Wild
AU - Aparna Bharati; Daniel Moreira; Allan Pinto; Joel Brogan; Kevin Bowyer; Patrick Flynn; Walter Scheirer; Anderson Rocha
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2069
ER -
Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). U-Phylogeny: Undirected Provenance Graph Construction in the Wild. IEEE SigPort. http://sigport.org/2069
Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha, 2017. U-Phylogeny: Undirected Provenance Graph Construction in the Wild. Available at: http://sigport.org/2069.
Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. (2017). "U-Phylogeny: Undirected Provenance Graph Construction in the Wild." Web.
1. Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick Flynn, Walter Scheirer, Anderson Rocha. U-Phylogeny: Undirected Provenance Graph Construction in the Wild [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2069

FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION

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14 September 2017 - 10:21pm
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[1] , "FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2068. Accessed: Nov. 19, 2018.
@article{2068-17,
url = {http://sigport.org/2068},
author = { },
publisher = {IEEE SigPort},
title = {FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2068
ER -
. (2017). FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION. IEEE SigPort. http://sigport.org/2068
, 2017. FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION. Available at: http://sigport.org/2068.
. (2017). "FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION." Web.
1. . FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2068

LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES

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14 September 2017 - 10:16pm
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[1] , "LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2066. Accessed: Nov. 19, 2018.
@article{2066-17,
url = {http://sigport.org/2066},
author = { },
publisher = {IEEE SigPort},
title = {LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES},
year = {2017} }
TY - EJOUR
T1 - LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2066
ER -
. (2017). LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES. IEEE SigPort. http://sigport.org/2066
, 2017. LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES. Available at: http://sigport.org/2066.
. (2017). "LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES." Web.
1. . LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2066

TAD16K: An Enhanced Benchmark for Autonomous Driving


Although promising results have been achieved in the areas of object detection and classification, few works have provided an end-to-end solution to the perception problems in the autonomous driving field. In this paper, we make two contributions. Firstly, we fully enhanced our previously released TT100K benchmark and provide 16,817 elaborately labeled Tencent Street View panoramas.

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Authors:
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su
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14 September 2017 - 6:10am
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[1] Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su, "TAD16K: An Enhanced Benchmark for Autonomous Driving", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2007. Accessed: Nov. 19, 2018.
@article{2007-17,
url = {http://sigport.org/2007},
author = {Yuming Li; Jue Wang; Tengfei Xing; Tianlu Liu; Chengjun Li; Kuifeng Su },
publisher = {IEEE SigPort},
title = {TAD16K: An Enhanced Benchmark for Autonomous Driving},
year = {2017} }
TY - EJOUR
T1 - TAD16K: An Enhanced Benchmark for Autonomous Driving
AU - Yuming Li; Jue Wang; Tengfei Xing; Tianlu Liu; Chengjun Li; Kuifeng Su
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2007
ER -
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. (2017). TAD16K: An Enhanced Benchmark for Autonomous Driving. IEEE SigPort. http://sigport.org/2007
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su, 2017. TAD16K: An Enhanced Benchmark for Autonomous Driving. Available at: http://sigport.org/2007.
Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. (2017). "TAD16K: An Enhanced Benchmark for Autonomous Driving." Web.
1. Yuming Li, Jue Wang, Tengfei Xing, Tianlu Liu, Chengjun Li, Kuifeng Su. TAD16K: An Enhanced Benchmark for Autonomous Driving [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2007

A Reduced-Reference Quality Metric for Screen Content Image

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14 September 2017 - 5:26am
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[1] , "A Reduced-Reference Quality Metric for Screen Content Image", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2005. Accessed: Nov. 19, 2018.
@article{2005-17,
url = {http://sigport.org/2005},
author = { },
publisher = {IEEE SigPort},
title = {A Reduced-Reference Quality Metric for Screen Content Image},
year = {2017} }
TY - EJOUR
T1 - A Reduced-Reference Quality Metric for Screen Content Image
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2005
ER -
. (2017). A Reduced-Reference Quality Metric for Screen Content Image. IEEE SigPort. http://sigport.org/2005
, 2017. A Reduced-Reference Quality Metric for Screen Content Image. Available at: http://sigport.org/2005.
. (2017). "A Reduced-Reference Quality Metric for Screen Content Image." Web.
1. . A Reduced-Reference Quality Metric for Screen Content Image [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2005

Color Reduction based on Categorical Perception


This paper addresses the problem of color reduction which aims at computing a compact representation of a color coordinate
system. By capitalizing on studies that have suggested the existence of eleven focal colors, we conducted subjective
experiments which exploited the categorical nature of human color perception. This paper describes a novel color reduction

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Authors:
Ahmad Al-Kabbany, Di Pang
Submitted On:
20 September 2017 - 1:23am
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[1] Ahmad Al-Kabbany, Di Pang, "Color Reduction based on Categorical Perception", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1972. Accessed: Nov. 19, 2018.
@article{1972-17,
url = {http://sigport.org/1972},
author = {Ahmad Al-Kabbany; Di Pang },
publisher = {IEEE SigPort},
title = {Color Reduction based on Categorical Perception},
year = {2017} }
TY - EJOUR
T1 - Color Reduction based on Categorical Perception
AU - Ahmad Al-Kabbany; Di Pang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1972
ER -
Ahmad Al-Kabbany, Di Pang. (2017). Color Reduction based on Categorical Perception. IEEE SigPort. http://sigport.org/1972
Ahmad Al-Kabbany, Di Pang, 2017. Color Reduction based on Categorical Perception. Available at: http://sigport.org/1972.
Ahmad Al-Kabbany, Di Pang. (2017). "Color Reduction based on Categorical Perception." Web.
1. Ahmad Al-Kabbany, Di Pang. Color Reduction based on Categorical Perception [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1972

FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS


Real-time identification of facial expressions is an important topic in the area of human computer interaction and pattern recognition. The research has gained significant attention in recent years. However, many challenges still exist. This is because an individual might display different expressions at different times even for the same mood. Expressions can also be influenced by health. Our proposed framework aims to capture unique information related to facial expressions from salient patches.

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20 September 2017 - 8:57am
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[1] , " FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1955. Accessed: Nov. 19, 2018.
@article{1955-17,
url = {http://sigport.org/1955},
author = { },
publisher = {IEEE SigPort},
title = { FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS},
year = {2017} }
TY - EJOUR
T1 - FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1955
ER -
. (2017). FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS. IEEE SigPort. http://sigport.org/1955
, 2017. FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS. Available at: http://sigport.org/1955.
. (2017). " FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS." Web.
1. . FACIAL EXPRESSION RECOGNITION USING SVM CLASSIFICATION ON MIC-MACRO PATTERNS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1955

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