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Image/Video Processing

Instance Flow Based Online Multiple Object Tracking

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Authors:
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens
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15 September 2017 - 7:41am
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[1] Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, "Instance Flow Based Online Multiple Object Tracking", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2123. Accessed: Oct. 19, 2017.
@article{2123-17,
url = {http://sigport.org/2123},
author = {Sebastian Bullinger; Christoph Bodensteiner; Michael Arens },
publisher = {IEEE SigPort},
title = {Instance Flow Based Online Multiple Object Tracking},
year = {2017} }
TY - EJOUR
T1 - Instance Flow Based Online Multiple Object Tracking
AU - Sebastian Bullinger; Christoph Bodensteiner; Michael Arens
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2123
ER -
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens. (2017). Instance Flow Based Online Multiple Object Tracking. IEEE SigPort. http://sigport.org/2123
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, 2017. Instance Flow Based Online Multiple Object Tracking. Available at: http://sigport.org/2123.
Sebastian Bullinger, Christoph Bodensteiner, Michael Arens. (2017). "Instance Flow Based Online Multiple Object Tracking." Web.
1. Sebastian Bullinger, Christoph Bodensteiner, Michael Arens. Instance Flow Based Online Multiple Object Tracking [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2123

LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES


Semantic segmentation of indoor scene images has a wide range of
applications. However, due to a large number of classes and uneven
distribution in indoor scenes, mislabels are often made when facing
small objects or boundary regions. Technically, contextual infor-
mation may benefit for segmentation results, but has not yet been
exploited sufficiently. In this paper, we propose a learnable contex-
tual regularization model for enhancing the semantic segmentation
results of color indoor scene images. This regularization model is

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Authors:
Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan
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15 September 2017 - 7:33am
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LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES

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[1] Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan , "LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2122. Accessed: Oct. 19, 2017.
@article{2122-17,
url = {http://sigport.org/2122},
author = {Jun Chu ; Xu Xiao; Gaofeng Meng ; Lingfeng Wang and Chunhong Pan },
publisher = {IEEE SigPort},
title = {LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES},
year = {2017} }
TY - EJOUR
T1 - LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES
AU - Jun Chu ; Xu Xiao; Gaofeng Meng ; Lingfeng Wang and Chunhong Pan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2122
ER -
Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan . (2017). LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES. IEEE SigPort. http://sigport.org/2122
Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan , 2017. LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES. Available at: http://sigport.org/2122.
Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan . (2017). "LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES." Web.
1. Jun Chu , Xu Xiao, Gaofeng Meng , Lingfeng Wang and Chunhong Pan . LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2122

Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise


High-fidelity color image restoration is always of high de- manding for high-density noise corrupted images. Such problem becomes more challenging if the degraded image and the expected restored image are of different resolutions, as conventional ‘cascaded: denoising followed by sampling’ and ‘operation on RGB channel independently’ methods induce error amplification and color artifacts.

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Authors:
Ketan Tang, Lu Fang
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15 September 2017 - 6:33am
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[1] Ketan Tang, Lu Fang, "Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2119. Accessed: Oct. 19, 2017.
@article{2119-17,
url = {http://sigport.org/2119},
author = {Ketan Tang; Lu Fang },
publisher = {IEEE SigPort},
title = {Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise},
year = {2017} }
TY - EJOUR
T1 - Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise
AU - Ketan Tang; Lu Fang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2119
ER -
Ketan Tang, Lu Fang. (2017). Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise. IEEE SigPort. http://sigport.org/2119
Ketan Tang, Lu Fang, 2017. Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise. Available at: http://sigport.org/2119.
Ketan Tang, Lu Fang. (2017). "Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise." Web.
1. Ketan Tang, Lu Fang. Cross-scale Color Image Restoration Under High Density Salt-and-pepper Noise [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2119

EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY


Detecting cloud regions in remote sensing image (RSI) is very challenging yet of great importance to meteorological forecasting and other RSI-related applications. Technically, this task is typically implemented as a pixel-level segmentation. However, traditional methods based on handcrafted or low-level cloud features often fail to achieve satisfactory performances from images with bright non-cloud and/or semitransparent cloud regions.

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Authors:
Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan
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15 September 2017 - 5:07am
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Efficient cloud detection using edge-aware network and easy-to-hard training strategy

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[1] Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan, "EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2115. Accessed: Oct. 19, 2017.
@article{2115-17,
url = {http://sigport.org/2115},
author = {Kun Yuan; Gaofeng Meng; Dongcai Cheng; Jun Bai; Shiming Xiang and Chunhong Pan },
publisher = {IEEE SigPort},
title = {EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY},
year = {2017} }
TY - EJOUR
T1 - EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY
AU - Kun Yuan; Gaofeng Meng; Dongcai Cheng; Jun Bai; Shiming Xiang and Chunhong Pan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2115
ER -
Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan. (2017). EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY. IEEE SigPort. http://sigport.org/2115
Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan, 2017. EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY. Available at: http://sigport.org/2115.
Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan. (2017). "EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY." Web.
1. Kun Yuan, Gaofeng Meng, Dongcai Cheng, Jun Bai, Shiming Xiang and Chunhong Pan. EFFICIENT CLOUD DETECTION IN REMOTE SENSING IMAGES USING EDGE-AWARE SEGMENTATION NETWORK AND EASY-TO-HARD TRAINING STRATEGY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2115

Improving the Discrimination Between Foreground and Background for Semantic Segmentation


#2125.pdf

PDF icon ICIP1701 (11 downloads)

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15 September 2017 - 4:49am
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[1] , "Improving the Discrimination Between Foreground and Background for Semantic Segmentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2110. Accessed: Oct. 19, 2017.
@article{2110-17,
url = {http://sigport.org/2110},
author = { },
publisher = {IEEE SigPort},
title = {Improving the Discrimination Between Foreground and Background for Semantic Segmentation},
year = {2017} }
TY - EJOUR
T1 - Improving the Discrimination Between Foreground and Background for Semantic Segmentation
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2110
ER -
. (2017). Improving the Discrimination Between Foreground and Background for Semantic Segmentation. IEEE SigPort. http://sigport.org/2110
, 2017. Improving the Discrimination Between Foreground and Background for Semantic Segmentation. Available at: http://sigport.org/2110.
. (2017). "Improving the Discrimination Between Foreground and Background for Semantic Segmentation." Web.
1. . Improving the Discrimination Between Foreground and Background for Semantic Segmentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2110

Tag Tree Creation of Social Image for Personalized Recommendation


The tags are usually tagged by different users in social image sharing websites, which can indicate image semantic information and imply user's preference. Therefore, the tags can contribute to personalized recommendation of social image. However, the present social image tags models only consider single tag,resulting in the relationships among tags are ignored. In this paper, we propose a novel method to create tag tree of social image for personalized recommendation. Firstly, the tag ranking is realized to remove noisy tags.

1496.pdf

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Authors:
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo
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15 September 2017 - 4:21am
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[1] Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo, "Tag Tree Creation of Social Image for Personalized Recommendation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2102. Accessed: Oct. 19, 2017.
@article{2102-17,
url = {http://sigport.org/2102},
author = {Ying Yang; Jing Zhang; Jihong Liu; Jiafeng Li; Li Zhuo },
publisher = {IEEE SigPort},
title = {Tag Tree Creation of Social Image for Personalized Recommendation},
year = {2017} }
TY - EJOUR
T1 - Tag Tree Creation of Social Image for Personalized Recommendation
AU - Ying Yang; Jing Zhang; Jihong Liu; Jiafeng Li; Li Zhuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2102
ER -
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. (2017). Tag Tree Creation of Social Image for Personalized Recommendation. IEEE SigPort. http://sigport.org/2102
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo, 2017. Tag Tree Creation of Social Image for Personalized Recommendation. Available at: http://sigport.org/2102.
Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. (2017). "Tag Tree Creation of Social Image for Personalized Recommendation." Web.
1. Ying Yang, Jing Zhang, Jihong Liu, Jiafeng Li, Li Zhuo. Tag Tree Creation of Social Image for Personalized Recommendation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2102

COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY


The technology used to detect community structures in graphs, or graph clustering technology, is important in a
wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community

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Authors:
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato
Submitted On:
15 September 2017 - 4:15am
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20170914okuda.pdf

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[1] Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato, "COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2101. Accessed: Oct. 19, 2017.
@article{2101-17,
url = {http://sigport.org/2101},
author = {Shin'ichi Satoh; Shoichiro Iwasawa; Shunsuke Yoshida; Yutaka Kidawara; Yoichi Sato },
publisher = {IEEE SigPort},
title = {COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY},
year = {2017} }
TY - EJOUR
T1 - COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY
AU - Shin'ichi Satoh; Shoichiro Iwasawa; Shunsuke Yoshida; Yutaka Kidawara; Yoichi Sato
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2101
ER -
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. (2017). COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY. IEEE SigPort. http://sigport.org/2101
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato, 2017. COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY. Available at: http://sigport.org/2101.
Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. (2017). "COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY." Web.
1. Shin'ichi Satoh, Shoichiro Iwasawa, Shunsuke Yoshida, Yutaka Kidawara, Yoichi Sato. COMMUNITY DETECTION USING RANDOM-WALK SIMILARITY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2101

Deep CNN with colorLines model for unmarked road segmentation


Road detection from a monocular camera is a perception module in any advanced driver assistance or autonomous driving system. Traditional techniques work reasonably well for this problem when the roads are well maintained and the boundaries are clearly marked. However, in many developing countries or even for the rural areas in the developed countries, the assumption does not hold which leads to failure of such techniques.

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Authors:
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee
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15 September 2017 - 4:09am
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[1] Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee, "Deep CNN with colorLines model for unmarked road segmentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2099. Accessed: Oct. 19, 2017.
@article{2099-17,
url = {http://sigport.org/2099},
author = {Shashank Yadav; Suvam Patra; Chetan Arora; Subhashis Banerjee },
publisher = {IEEE SigPort},
title = {Deep CNN with colorLines model for unmarked road segmentation},
year = {2017} }
TY - EJOUR
T1 - Deep CNN with colorLines model for unmarked road segmentation
AU - Shashank Yadav; Suvam Patra; Chetan Arora; Subhashis Banerjee
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2099
ER -
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. (2017). Deep CNN with colorLines model for unmarked road segmentation. IEEE SigPort. http://sigport.org/2099
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee, 2017. Deep CNN with colorLines model for unmarked road segmentation. Available at: http://sigport.org/2099.
Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. (2017). "Deep CNN with colorLines model for unmarked road segmentation." Web.
1. Shashank Yadav, Suvam Patra, Chetan Arora, Subhashis Banerjee. Deep CNN with colorLines model for unmarked road segmentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2099

Non-Rigid Image Deformation Algorithm Based on MRLS-TPS


In this paper, we propose a novel closed-form transformation estimation method based on moving regularized least squares optimization with thin-plate spline (MRLS-TPS) for non-rigid image deformation. The method takes the user-controlled point-offset-vectors as the input data, and estimates the spatial transformation about the two control point sets for each pixel. To achieve a realistic deformation, we formulates the transformation estimation as a vector-field interpolation problem by a moving regularized least squares method.

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Authors:
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma
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15 September 2017 - 4:02am
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[1] Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma, "Non-Rigid Image Deformation Algorithm Based on MRLS-TPS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2095. Accessed: Oct. 19, 2017.
@article{2095-17,
url = {http://sigport.org/2095},
author = {Huabing Zhou; Yuyu Kuang; Zhenghong Yu; Shiqiang Ren; Anna Dai; Yanduo Zhang; Tao Lu; Jiayi Ma },
publisher = {IEEE SigPort},
title = {Non-Rigid Image Deformation Algorithm Based on MRLS-TPS},
year = {2017} }
TY - EJOUR
T1 - Non-Rigid Image Deformation Algorithm Based on MRLS-TPS
AU - Huabing Zhou; Yuyu Kuang; Zhenghong Yu; Shiqiang Ren; Anna Dai; Yanduo Zhang; Tao Lu; Jiayi Ma
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2095
ER -
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. (2017). Non-Rigid Image Deformation Algorithm Based on MRLS-TPS. IEEE SigPort. http://sigport.org/2095
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma, 2017. Non-Rigid Image Deformation Algorithm Based on MRLS-TPS. Available at: http://sigport.org/2095.
Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. (2017). "Non-Rigid Image Deformation Algorithm Based on MRLS-TPS." Web.
1. Huabing Zhou, Yuyu Kuang, Zhenghong Yu, Shiqiang Ren, Anna Dai, Yanduo Zhang, Tao Lu, Jiayi Ma. Non-Rigid Image Deformation Algorithm Based on MRLS-TPS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2095

Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system

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Authors:
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang
Submitted On:
15 September 2017 - 3:59am
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[1] Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang, "Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2094. Accessed: Oct. 19, 2017.
@article{2094-17,
url = {http://sigport.org/2094},
author = {Lin Zhang; Ying Fu; Yinqiang Zheng; Hua Huang },
publisher = {IEEE SigPort},
title = {Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system},
year = {2017} }
TY - EJOUR
T1 - Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system
AU - Lin Zhang; Ying Fu; Yinqiang Zheng; Hua Huang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2094
ER -
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. (2017). Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system. IEEE SigPort. http://sigport.org/2094
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang, 2017. Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system. Available at: http://sigport.org/2094.
Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. (2017). "Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system." Web.
1. Lin Zhang, Ying Fu, Yinqiang Zheng, Hua Huang. Camera spectral sensitivity, illumination and spectral reflectance estimation for a hybrid hyperspectral image capture system [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2094

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