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Image, Video, and Multidimensional Signal Processing

FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION

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14 September 2017 - 10:21pm
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icip2017-poster-feature-v2.pdf

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[1] , "FEATURE SAMPLING STRATEGIES FOR ACTION RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2068. Accessed: Oct. 19, 2017.
@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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icip2017-poster-loosecut-v2.pdf

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[1] , "LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDED BOXES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2066. Accessed: Oct. 19, 2017.
@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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ICIP2017_poster.pdf

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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: Oct. 19, 2017.
@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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2017ICIP.pdf

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[1] , "A Reduced-Reference Quality Metric for Screen Content Image", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2005. Accessed: Oct. 19, 2017.
@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
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20 September 2017 - 1:23am
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Poster_Color Reduction Based on Human Categorical Perception.pdf

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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: Oct. 19, 2017.
@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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ICIP-Slides.pdf

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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: Oct. 19, 2017.
@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

DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION

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kisang hong
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14 September 2017 - 2:26am
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MA-L6-2_Jung.pdf

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[1] kisang hong, "DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1929. Accessed: Oct. 19, 2017.
@article{1929-17,
url = {http://sigport.org/1929},
author = {kisang hong },
publisher = {IEEE SigPort},
title = {DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION},
year = {2017} }
TY - EJOUR
T1 - DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION
AU - kisang hong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1929
ER -
kisang hong. (2017). DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION. IEEE SigPort. http://sigport.org/1929
kisang hong, 2017. DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION. Available at: http://sigport.org/1929.
kisang hong. (2017). "DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION." Web.
1. kisang hong. DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1929

DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION

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Authors:
kisang hong
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14 September 2017 - 2:28am
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MA-L6-2_Jung.pdf

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[1] kisang hong, "DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1928. Accessed: Oct. 19, 2017.
@article{1928-17,
url = {http://sigport.org/1928},
author = {kisang hong },
publisher = {IEEE SigPort},
title = {DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION},
year = {2017} }
TY - EJOUR
T1 - DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION
AU - kisang hong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1928
ER -
kisang hong. (2017). DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION. IEEE SigPort. http://sigport.org/1928
kisang hong, 2017. DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION. Available at: http://sigport.org/1928.
kisang hong. (2017). "DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION." Web.
1. kisang hong. DIRECT MULTI-SCALE DUAL-STREAM NETWORK FOR PEDESTRIAN DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1928

SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR


The existing single depth image super-resolution (SR)
methods suppose that the image to be interpolated is noise
free. However, the supposition is invalid in practice because
noise will be inevitably introduced in the depth image acquisition
process. In this paper, we address the problem of image
denoising and SR jointly based on designing sparse graphs
that are useful for describing the geometric structures of data
domains. In our method, we first cluster similar patches in a
noisy depth image and compute an average patch. Different

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Authors:
Xianming Liu,Yongbing Zhang,Qionghai Dai
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11 September 2017 - 9:36pm
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Yihui Feng_icip_2017.pdf

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[1] Xianming Liu,Yongbing Zhang,Qionghai Dai, "SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1922. Accessed: Oct. 19, 2017.
@article{1922-17,
url = {http://sigport.org/1922},
author = {Xianming Liu;Yongbing Zhang;Qionghai Dai },
publisher = {IEEE SigPort},
title = {SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR},
year = {2017} }
TY - EJOUR
T1 - SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR
AU - Xianming Liu;Yongbing Zhang;Qionghai Dai
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1922
ER -
Xianming Liu,Yongbing Zhang,Qionghai Dai. (2017). SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR. IEEE SigPort. http://sigport.org/1922
Xianming Liu,Yongbing Zhang,Qionghai Dai, 2017. SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR. Available at: http://sigport.org/1922.
Xianming Liu,Yongbing Zhang,Qionghai Dai. (2017). "SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR." Web.
1. Xianming Liu,Yongbing Zhang,Qionghai Dai. SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1922

CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES


License plate detection is a challenging task when dealing with open environments and images captured from a certain distance by lowcost cameras. In this paper, we propose an approach for detecting license plates based on a convolutional neural network which models a function that produces a score for each image sub-region, allowing us to estimate the locations of the detected license plates by combining the results obtained from sparse overlapping regions.

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Authors:
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu
Submitted On:
11 September 2017 - 2:51pm
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2017-09 - ICIP 2017.pdf

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[1] Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu, "CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1915. Accessed: Oct. 19, 2017.
@article{1915-17,
url = {http://sigport.org/1915},
author = {Francisco Delmar Kurpiel; Rodrigo Minetto; Bogdan Tomoyuki Nassu },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES},
year = {2017} }
TY - EJOUR
T1 - CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES
AU - Francisco Delmar Kurpiel; Rodrigo Minetto; Bogdan Tomoyuki Nassu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1915
ER -
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. (2017). CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES. IEEE SigPort. http://sigport.org/1915
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu, 2017. CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES. Available at: http://sigport.org/1915.
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. (2017). "CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES." Web.
1. Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1915

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