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Image/Video Processing

Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster


Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network.

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Authors:
Yuewei Na, Siyang Li, C.-C. Jay Kuo
Submitted On:
14 November 2017 - 10:11pm
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GlobalSIP2017_Segmentation-aided_Text_Detection

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[1] Yuewei Na, Siyang Li, C.-C. Jay Kuo, "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2302. Accessed: Dec. 18, 2017.
@article{2302-17,
url = {http://sigport.org/2302},
author = {Yuewei Na; Siyang Li; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster},
year = {2017} }
TY - EJOUR
T1 - Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster
AU - Yuewei Na; Siyang Li; C.-C. Jay Kuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2302
ER -
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster. IEEE SigPort. http://sigport.org/2302
Yuewei Na, Siyang Li, C.-C. Jay Kuo, 2017. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster. Available at: http://sigport.org/2302.
Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster." Web.
1. Yuewei Na, Siyang Li, C.-C. Jay Kuo. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Poster [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2302

FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE


Visible (VS) and near infra-red (NIR) band sensors provide digital
images that capture complementary spectral radiations from a
scene. Since NIR radiations propagate well through haze, mist, or
fog, the captured NIR image contains better scene details compared
to the VS image in such cases. However, NIR radiations are material
dependent and provide little information about color or texture of
the scene’s objects. To exploit the complementary details provided
by VS and NIR images, we propose a fusion approach that adaptively

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Authors:
Ahmed Elliethy, Hussein Aly
Submitted On:
10 November 2017 - 12:44pm
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[1] Ahmed Elliethy, Hussein Aly, "FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2297. Accessed: Dec. 18, 2017.
@article{2297-17,
url = {http://sigport.org/2297},
author = {Ahmed Elliethy; Hussein Aly },
publisher = {IEEE SigPort},
title = {FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE},
year = {2017} }
TY - EJOUR
T1 - FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE
AU - Ahmed Elliethy; Hussein Aly
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2297
ER -
Ahmed Elliethy, Hussein Aly. (2017). FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE. IEEE SigPort. http://sigport.org/2297
Ahmed Elliethy, Hussein Aly, 2017. FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE. Available at: http://sigport.org/2297.
Ahmed Elliethy, Hussein Aly. (2017). "FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE." Web.
1. Ahmed Elliethy, Hussein Aly. FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2297

FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE


Visible (VS) and near infra-red (NIR) band sensors provide digital
images that capture complementary spectral radiations from a
scene. Since NIR radiations propagate well through haze, mist, or
fog, the captured NIR image contains better scene details compared
to the VS image in such cases. However, NIR radiations are material
dependent and provide little information about color or texture of
the scene’s objects. To exploit the complementary details provided
by VS and NIR images, we propose a fusion approach that adaptively

Paper Details

Authors:
Ahmed Elliethy, Hussein Aly
Submitted On:
10 November 2017 - 12:44pm
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presentation

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[1] Ahmed Elliethy, Hussein Aly, "FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2296. Accessed: Dec. 18, 2017.
@article{2296-17,
url = {http://sigport.org/2296},
author = {Ahmed Elliethy; Hussein Aly },
publisher = {IEEE SigPort},
title = {FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE},
year = {2017} }
TY - EJOUR
T1 - FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE
AU - Ahmed Elliethy; Hussein Aly
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2296
ER -
Ahmed Elliethy, Hussein Aly. (2017). FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE. IEEE SigPort. http://sigport.org/2296
Ahmed Elliethy, Hussein Aly, 2017. FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE. Available at: http://sigport.org/2296.
Ahmed Elliethy, Hussein Aly. (2017). "FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE." Web.
1. Ahmed Elliethy, Hussein Aly. FAST NEAR INFRARED FUSION-BASED ADAPTIVE ENHANCEMENT OF VISIBLE [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2296

Hybrid eye center localization using cascaded regression and robust circle fitting


We propose a new cascaded regressor for eye center detection. Previous methods start from a face or an eye detector and use either advanced features or powerful regressors for eye center localization, but not both. Instead, we detect the eyes more accurately using an existing facial feature alignment method. We improve the robustness of localization by using both advanced features and powerful regression machinery. Finally, unlike most other methods that do not refine the regression results, we make the localization more accurate by adding a robust circle fitting post-processing step.

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Authors:
Alex Levinshtein, Edmund Phung, Parham Aarabi
Submitted On:
10 November 2017 - 10:48am
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GlobalSIP2017_IrisDetection.pdf

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[1] Alex Levinshtein, Edmund Phung, Parham Aarabi, "Hybrid eye center localization using cascaded regression and robust circle fitting", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2294. Accessed: Dec. 18, 2017.
@article{2294-17,
url = {http://sigport.org/2294},
author = {Alex Levinshtein; Edmund Phung; Parham Aarabi },
publisher = {IEEE SigPort},
title = {Hybrid eye center localization using cascaded regression and robust circle fitting},
year = {2017} }
TY - EJOUR
T1 - Hybrid eye center localization using cascaded regression and robust circle fitting
AU - Alex Levinshtein; Edmund Phung; Parham Aarabi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2294
ER -
Alex Levinshtein, Edmund Phung, Parham Aarabi. (2017). Hybrid eye center localization using cascaded regression and robust circle fitting. IEEE SigPort. http://sigport.org/2294
Alex Levinshtein, Edmund Phung, Parham Aarabi, 2017. Hybrid eye center localization using cascaded regression and robust circle fitting. Available at: http://sigport.org/2294.
Alex Levinshtein, Edmund Phung, Parham Aarabi. (2017). "Hybrid eye center localization using cascaded regression and robust circle fitting." Web.
1. Alex Levinshtein, Edmund Phung, Parham Aarabi. Hybrid eye center localization using cascaded regression and robust circle fitting [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2294

SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION


We propose a super-resolution method to convert spatial resolution from 2K to 8K to utilize existing 2K HDTV video for 8K UHDTV broadcasting. The proposed method uses image registration for wavelet multi-scale bands of 2K video frames considering future hardware implementation to real-time processing. This image registration consists of alignment and assignment procedures. The wavelet multi-scale bands are extracted using wavelet decomposition of 2K video frames. The alignment is processed from 2K video frames to its wavelet low-frequency bands.

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Authors:
Yasutaka Matsuo, Shinichi Sakaida
Submitted On:
10 November 2017 - 6:44am
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[1] Yasutaka Matsuo, Shinichi Sakaida, "SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2287. Accessed: Dec. 18, 2017.
@article{2287-17,
url = {http://sigport.org/2287},
author = {Yasutaka Matsuo; Shinichi Sakaida },
publisher = {IEEE SigPort},
title = {SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION},
year = {2017} }
TY - EJOUR
T1 - SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION
AU - Yasutaka Matsuo; Shinichi Sakaida
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2287
ER -
Yasutaka Matsuo, Shinichi Sakaida. (2017). SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION. IEEE SigPort. http://sigport.org/2287
Yasutaka Matsuo, Shinichi Sakaida, 2017. SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION. Available at: http://sigport.org/2287.
Yasutaka Matsuo, Shinichi Sakaida. (2017). "SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION." Web.
1. Yasutaka Matsuo, Shinichi Sakaida. SUPER-RESOLUTION FOR 2K/8K TELEVISION USING WAVELET-BASED IMAGE REGISTRATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2287

A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION


An efficient video dehazing system based on the dark channel prior (DCP) is proposed. For the proposed system, the original DCP-based dehazing method is modified in order to achieve a fast execution of the airlight estimation without noticeable degradation of the dehazing quality. The proposed system is implemented in a heterogeneous multi-processor system-on-chip, for which the overall dehazing process is described in OpenCL and synthesized into the custom circuitry targeting an FPGA.

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Authors:
Tae-Hwan Kim
Submitted On:
9 November 2017 - 10:27pm
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GlobalSIP 2017 presentation.pdf

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[1] Tae-Hwan Kim, "A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2281. Accessed: Dec. 18, 2017.
@article{2281-17,
url = {http://sigport.org/2281},
author = {Tae-Hwan Kim },
publisher = {IEEE SigPort},
title = {A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION},
year = {2017} }
TY - EJOUR
T1 - A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION
AU - Tae-Hwan Kim
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2281
ER -
Tae-Hwan Kim. (2017). A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION. IEEE SigPort. http://sigport.org/2281
Tae-Hwan Kim, 2017. A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION. Available at: http://sigport.org/2281.
Tae-Hwan Kim. (2017). "A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION." Web.
1. Tae-Hwan Kim. A VIDEO DEHAZING SYSTEM BASED ON FAST AIRLIGHT ESTIMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2281

CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER

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Authors:
Zhenyu Liu,Xiangyang Ji,Dongsheng Wang
Submitted On:
12 November 2017 - 4:48am
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[1] Zhenyu Liu,Xiangyang Ji,Dongsheng Wang, "CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2276. Accessed: Dec. 18, 2017.
@article{2276-17,
url = {http://sigport.org/2276},
author = {Zhenyu Liu;Xiangyang Ji;Dongsheng Wang },
publisher = {IEEE SigPort},
title = {CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER},
year = {2017} }
TY - EJOUR
T1 - CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER
AU - Zhenyu Liu;Xiangyang Ji;Dongsheng Wang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2276
ER -
Zhenyu Liu,Xiangyang Ji,Dongsheng Wang. (2017). CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER. IEEE SigPort. http://sigport.org/2276
Zhenyu Liu,Xiangyang Ji,Dongsheng Wang, 2017. CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER. Available at: http://sigport.org/2276.
Zhenyu Liu,Xiangyang Ji,Dongsheng Wang. (2017). "CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER." Web.
1. Zhenyu Liu,Xiangyang Ji,Dongsheng Wang. CNN ORIENTED FAST PU MODE DECISION FOR HEVC HARDWIRED INTRA ENCODER [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2276

Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation


Motion Estimation is an important module in the HEVC encoder.
It is divided into Integer-pixel Motion Estimation followed
by Fractional-pixel motion Estimation for coding efficiency
improvement. In this paper, a new algorithm is proposed
to estimate the best fractional-pixel motion vector. It
is based on a mathematical model using the matching error
costs of the integer pixel locations around the best integer
candidate. The proposed algorithm is implemented in HEVC
standard software (HM-16.9). The experimental results show

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Authors:
Emad Badry, Ahmed Shalaby, Mohammed S. Sayed
Submitted On:
12 November 2017 - 6:48pm
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FAST FRACTIONAL-PIXEL MOTION ESTIMATION USING LAGRANGIAN-BASED ERROR SURFACE INTERPOLATION

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[1] Emad Badry, Ahmed Shalaby, Mohammed S. Sayed, "Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2269. Accessed: Dec. 18, 2017.
@article{2269-17,
url = {http://sigport.org/2269},
author = {Emad Badry; Ahmed Shalaby; Mohammed S. Sayed },
publisher = {IEEE SigPort},
title = {Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation},
year = {2017} }
TY - EJOUR
T1 - Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation
AU - Emad Badry; Ahmed Shalaby; Mohammed S. Sayed
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2269
ER -
Emad Badry, Ahmed Shalaby, Mohammed S. Sayed. (2017). Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation. IEEE SigPort. http://sigport.org/2269
Emad Badry, Ahmed Shalaby, Mohammed S. Sayed, 2017. Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation. Available at: http://sigport.org/2269.
Emad Badry, Ahmed Shalaby, Mohammed S. Sayed. (2017). "Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation." Web.
1. Emad Badry, Ahmed Shalaby, Mohammed S. Sayed. Fast Fractional-Pixel Motion Estimation using Lagrangian-Based Error Surface Interpolation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2269

HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH


Rate-constrained motion estimation (RCME) is the most computationally intensive task of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the dependencies in deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing at a frame level.

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Authors:
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez
Submitted On:
26 September 2017 - 8:28am
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ICIP2017-Poster-Hojati.pdf

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[1] Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez, "HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2247. Accessed: Dec. 18, 2017.
@article{2247-17,
url = {http://sigport.org/2247},
author = {Esmaeil Hojati; Jean-François Franche; Stéphane Coulombe; Carlos Vázquez },
publisher = {IEEE SigPort},
title = {HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH},
year = {2017} }
TY - EJOUR
T1 - HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH
AU - Esmaeil Hojati; Jean-François Franche; Stéphane Coulombe; Carlos Vázquez
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2247
ER -
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. (2017). HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH. IEEE SigPort. http://sigport.org/2247
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez, 2017. HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH. Available at: http://sigport.org/2247.
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. (2017). "HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH." Web.
1. Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2247

Plant Leaf Segmentation for Estimating Phenotypic Traits


In this paper we propose a method to segment individual leaves of crop plants from UAV imagery for the purposes of deriving phenotypic properties of the plant. The crop plant used in our study is sorghum Sorghum bicolor (L.) Moench. Phenotyping is a set of methodologies for analyzing and obtaining characteristic traits of a plant. In a phenotypic study, leaves are often used to estimate traits such as individual leaf area and Leaf Area Index (LAI). Our approach is to segment the leaves in polar coordinates using the plant center as the origin.

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Authors:
Yuhao Chen, Christopher Boomsma, Edward Delp
Submitted On:
25 September 2017 - 6:49pm
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poster_icip2017.pdf

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[1] Yuhao Chen, Christopher Boomsma, Edward Delp, "Plant Leaf Segmentation for Estimating Phenotypic Traits", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2245. Accessed: Dec. 18, 2017.
@article{2245-17,
url = {http://sigport.org/2245},
author = {Yuhao Chen; Christopher Boomsma; Edward Delp },
publisher = {IEEE SigPort},
title = {Plant Leaf Segmentation for Estimating Phenotypic Traits},
year = {2017} }
TY - EJOUR
T1 - Plant Leaf Segmentation for Estimating Phenotypic Traits
AU - Yuhao Chen; Christopher Boomsma; Edward Delp
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2245
ER -
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). Plant Leaf Segmentation for Estimating Phenotypic Traits. IEEE SigPort. http://sigport.org/2245
Yuhao Chen, Christopher Boomsma, Edward Delp, 2017. Plant Leaf Segmentation for Estimating Phenotypic Traits. Available at: http://sigport.org/2245.
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). "Plant Leaf Segmentation for Estimating Phenotypic Traits." Web.
1. Yuhao Chen, Christopher Boomsma, Edward Delp. Plant Leaf Segmentation for Estimating Phenotypic Traits [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2245

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