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Image/Video Processing

MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry

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Authors:
Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak
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14 September 2017 - 11:15am
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MUSeed_ICIP2017_Poster.pdf

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[1] Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak, "MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2036. Accessed: Oct. 19, 2017.
@article{2036-17,
url = {http://sigport.org/2036},
author = {Ke Gao; Tommi White; Kannappan Palaniappan; Michele Warmund; Filiz Bunyak },
publisher = {IEEE SigPort},
title = {MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry},
year = {2017} }
TY - EJOUR
T1 - MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry
AU - Ke Gao; Tommi White; Kannappan Palaniappan; Michele Warmund; Filiz Bunyak
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2036
ER -
Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak. (2017). MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry. IEEE SigPort. http://sigport.org/2036
Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak, 2017. MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry. Available at: http://sigport.org/2036.
Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak. (2017). "MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry." Web.
1. Ke Gao, Tommi White, Kannappan Palaniappan, Michele Warmund, Filiz Bunyak. MUSeed: A Mobile Image Analysis Application for Plant Seed Morphometry [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2036

EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK


This paper proposes an extended conjugate polar Fourier transform (ECPFT), to design iterated radial filter bank (RFB) and directional filter bank (DFB) convenient for accurate multiscale and multidirectional decomposition in discretization over a convolution network. With conjugated symmetric form, ECPFT would convert complex directional wavelets in original spatial domain to real ones in the inverse Fourier domain of ECPFT.

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Authors:
Can Xu,Wenrui Dai,Hongkai Xiong
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14 September 2017 - 11:11am
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poster_ICIP2017.pdf

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[1] Can Xu,Wenrui Dai,Hongkai Xiong, "EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2035. Accessed: Oct. 19, 2017.
@article{2035-17,
url = {http://sigport.org/2035},
author = {Can Xu;Wenrui Dai;Hongkai Xiong },
publisher = {IEEE SigPort},
title = {EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK},
year = {2017} }
TY - EJOUR
T1 - EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK
AU - Can Xu;Wenrui Dai;Hongkai Xiong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2035
ER -
Can Xu,Wenrui Dai,Hongkai Xiong. (2017). EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK. IEEE SigPort. http://sigport.org/2035
Can Xu,Wenrui Dai,Hongkai Xiong, 2017. EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK. Available at: http://sigport.org/2035.
Can Xu,Wenrui Dai,Hongkai Xiong. (2017). "EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK." Web.
1. Can Xu,Wenrui Dai,Hongkai Xiong. EXTENDED CONJUGATE POLAR FOURIER TRANSFORM IN CONVOLUTION NETWORK [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2035

Perceptual metric for color transfer methods


We present a perceptual model for evaluating results from color transfer methods. We conduct a user study, which provides a set of subjective scores for triples of input, target and result images. Then, for each triple, we compute a number of image features, which objectively characterize a color transfer. To describe the relationship between these features and the subjective scores, we build a regression model with random forests. An analysis and a cross-validation show that the predictions of our model are highly accurate.

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Authors:
Olivier Le Meur, Remi Cozot, Kadi Bouatouch
Submitted On:
14 September 2017 - 10:33am
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Perceptual evaluation metric

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[1] Olivier Le Meur, Remi Cozot, Kadi Bouatouch, "Perceptual metric for color transfer methods", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2033. Accessed: Oct. 19, 2017.
@article{2033-17,
url = {http://sigport.org/2033},
author = {Olivier Le Meur; Remi Cozot; Kadi Bouatouch },
publisher = {IEEE SigPort},
title = {Perceptual metric for color transfer methods},
year = {2017} }
TY - EJOUR
T1 - Perceptual metric for color transfer methods
AU - Olivier Le Meur; Remi Cozot; Kadi Bouatouch
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2033
ER -
Olivier Le Meur, Remi Cozot, Kadi Bouatouch. (2017). Perceptual metric for color transfer methods. IEEE SigPort. http://sigport.org/2033
Olivier Le Meur, Remi Cozot, Kadi Bouatouch, 2017. Perceptual metric for color transfer methods. Available at: http://sigport.org/2033.
Olivier Le Meur, Remi Cozot, Kadi Bouatouch. (2017). "Perceptual metric for color transfer methods." Web.
1. Olivier Le Meur, Remi Cozot, Kadi Bouatouch. Perceptual metric for color transfer methods [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2033

FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION


Cascade Deformable Part Models (DPMs) are cascade frameworks to speed up Deformable Part Models (DPMs), which are one of the state-of-the-art solutions for object detection. Its idea is to reject most non-object hypotheses from the early stages of detection process. By investigating the dependency between hypotheses over scales, we introduce a novel pruning method to accelerate Cascade DPM frameworks.

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Authors:
Hung Vu, Khoa Pho, Bac Le
Submitted On:
14 September 2017 - 9:21am
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2360_HungVU_flexible3dCascadeDPM

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[1] Hung Vu, Khoa Pho, Bac Le, "FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2031. Accessed: Oct. 19, 2017.
@article{2031-17,
url = {http://sigport.org/2031},
author = {Hung Vu; Khoa Pho; Bac Le },
publisher = {IEEE SigPort},
title = {FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION},
year = {2017} }
TY - EJOUR
T1 - FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION
AU - Hung Vu; Khoa Pho; Bac Le
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2031
ER -
Hung Vu, Khoa Pho, Bac Le. (2017). FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION. IEEE SigPort. http://sigport.org/2031
Hung Vu, Khoa Pho, Bac Le, 2017. FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION. Available at: http://sigport.org/2031.
Hung Vu, Khoa Pho, Bac Le. (2017). "FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION." Web.
1. Hung Vu, Khoa Pho, Bac Le. FLEXIBLE 3D NEIGHBORHOOD CASCADE DEFORMABLE PART MODELS FOR OBJECT DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2031

ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS


A solution to deploy object detection systems to practical applications is to build cascade frameworks which do threshold comparisons in each stage to efficiently discard a large number of negative objects. For particular applications, these thresholds should be retrained for better effectiveness and the efficiency via training datasets. It means that we have to store labeled datasets permanently or collect huge data (for highquality thresholds) whenever learning new thresholds. Both approaches are inconvenient and expensive in terms of memory and data collection cost.

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Authors:
Khoa Pho, Hung Vu, Bac Le
Submitted On:
14 September 2017 - 8:10pm
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2713_KhoaPho_AdaptiveCascadeThresholdLearning

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[1] Khoa Pho, Hung Vu, Bac Le, "ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2029. Accessed: Oct. 19, 2017.
@article{2029-17,
url = {http://sigport.org/2029},
author = {Khoa Pho; Hung Vu; Bac Le },
publisher = {IEEE SigPort},
title = {ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS},
year = {2017} }
TY - EJOUR
T1 - ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS
AU - Khoa Pho; Hung Vu; Bac Le
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2029
ER -
Khoa Pho, Hung Vu, Bac Le. (2017). ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS. IEEE SigPort. http://sigport.org/2029
Khoa Pho, Hung Vu, Bac Le, 2017. ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS. Available at: http://sigport.org/2029.
Khoa Pho, Hung Vu, Bac Le. (2017). "ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS." Web.
1. Khoa Pho, Hung Vu, Bac Le. ADAPTIVE CASCADE THRESHOLD LEARNING FROM NEGATIVE SAMPLES FOR DEFORMABLE PART MODELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2029

Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery


Wide area motion imagery (WAMI) acquired by an airborne sensor enables continuous monitoring of large urban areas. Reliable vehicle tracking in this imagery remains challenging due to low frame rate and small object size. Many approaches solely rely on motion detections provided by frame differencing or background subtraction. Recent approaches for persistent tracking, i.e. tracking vehicles even if they become stationary, compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context.

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14 September 2017 - 9:14am
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ICIP_2017_Spraul_PMHT_WAMI_v4.pdf

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[1] , "Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2028. Accessed: Oct. 19, 2017.
@article{2028-17,
url = {http://sigport.org/2028},
author = { },
publisher = {IEEE SigPort},
title = {Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery},
year = {2017} }
TY - EJOUR
T1 - Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2028
ER -
. (2017). Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery. IEEE SigPort. http://sigport.org/2028
, 2017. Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery. Available at: http://sigport.org/2028.
. (2017). "Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery." Web.
1. . Persistent Multiple Hypothesis Tracking for Wide Area Motion Imagery [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2028

TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES

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Authors:
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi
Submitted On:
14 September 2017 - 8:55am
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ICIP2017_poster

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[1] Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi, "TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2025. Accessed: Oct. 19, 2017.
@article{2025-17,
url = {http://sigport.org/2025},
author = {Ryo Yamakabe; Yusuke Monno; Masayuki Tanaka; Masatoshi Okutomi },
publisher = {IEEE SigPort},
title = {TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES},
year = {2017} }
TY - EJOUR
T1 - TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES
AU - Ryo Yamakabe; Yusuke Monno; Masayuki Tanaka; Masatoshi Okutomi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2025
ER -
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi. (2017). TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES. IEEE SigPort. http://sigport.org/2025
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi, 2017. TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES. Available at: http://sigport.org/2025.
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi. (2017). "TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES." Web.
1. Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi. TUNABLE COLOR CORRECTION BETWEEN LINEAR AND POLYNOMIAL MODELS FOR NOISY IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2025

Class-specific Poisson denoising by patch-based importance sampling

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Submitted On:
18 September 2017 - 11:25am
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presentation_1.pdf

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presentation_1.pdf

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[1] , "Class-specific Poisson denoising by patch-based importance sampling", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2018. Accessed: Oct. 19, 2017.
@article{2018-17,
url = {http://sigport.org/2018},
author = { },
publisher = {IEEE SigPort},
title = {Class-specific Poisson denoising by patch-based importance sampling},
year = {2017} }
TY - EJOUR
T1 - Class-specific Poisson denoising by patch-based importance sampling
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2018
ER -
. (2017). Class-specific Poisson denoising by patch-based importance sampling. IEEE SigPort. http://sigport.org/2018
, 2017. Class-specific Poisson denoising by patch-based importance sampling. Available at: http://sigport.org/2018.
. (2017). "Class-specific Poisson denoising by patch-based importance sampling." Web.
1. . Class-specific Poisson denoising by patch-based importance sampling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2018

Class-specific image denoising using importance sampling

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Submitted On:
18 September 2017 - 11:21am
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presentation_1.pdf

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presentation_1.pdf

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[1] , "Class-specific image denoising using importance sampling", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2016. Accessed: Oct. 19, 2017.
@article{2016-17,
url = {http://sigport.org/2016},
author = { },
publisher = {IEEE SigPort},
title = {Class-specific image denoising using importance sampling},
year = {2017} }
TY - EJOUR
T1 - Class-specific image denoising using importance sampling
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2016
ER -
. (2017). Class-specific image denoising using importance sampling. IEEE SigPort. http://sigport.org/2016
, 2017. Class-specific image denoising using importance sampling. Available at: http://sigport.org/2016.
. (2017). "Class-specific image denoising using importance sampling." Web.
1. . Class-specific image denoising using importance sampling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2016

TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN


Temporal Action localization is a more challenging vision task than action recognition because videos to be analyzed

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Authors:
Tianwei Lin, Xu Zhao, Zhaoxuan Fan
Submitted On:
14 September 2017 - 6:44am
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Poster-1520

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[1] Tianwei Lin, Xu Zhao, Zhaoxuan Fan, "TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2011. Accessed: Oct. 19, 2017.
@article{2011-17,
url = {http://sigport.org/2011},
author = {Tianwei Lin; Xu Zhao; Zhaoxuan Fan },
publisher = {IEEE SigPort},
title = {TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN},
year = {2017} }
TY - EJOUR
T1 - TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN
AU - Tianwei Lin; Xu Zhao; Zhaoxuan Fan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2011
ER -
Tianwei Lin, Xu Zhao, Zhaoxuan Fan. (2017). TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN. IEEE SigPort. http://sigport.org/2011
Tianwei Lin, Xu Zhao, Zhaoxuan Fan, 2017. TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN. Available at: http://sigport.org/2011.
Tianwei Lin, Xu Zhao, Zhaoxuan Fan. (2017). "TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN." Web.
1. Tianwei Lin, Xu Zhao, Zhaoxuan Fan. TEMPORAL ACTION LOCALIZATION WITH TWO-STREAM SEGMENT-BASED RNN [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2011

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