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Image/Video Processing

LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION


We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression.

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Authors:
Koustav Mullick, Anoop M. Namboodiri
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13 September 2017 - 1:33pm
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koustav-icip-poster.pdf

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[1] Koustav Mullick, Anoop M. Namboodiri, "LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1980. Accessed: Jan. 17, 2019.
@article{1980-17,
url = {http://sigport.org/1980},
author = {Koustav Mullick; Anoop M. Namboodiri },
publisher = {IEEE SigPort},
title = {LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION
AU - Koustav Mullick; Anoop M. Namboodiri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1980
ER -
Koustav Mullick, Anoop M. Namboodiri. (2017). LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION. IEEE SigPort. http://sigport.org/1980
Koustav Mullick, Anoop M. Namboodiri, 2017. LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION. Available at: http://sigport.org/1980.
Koustav Mullick, Anoop M. Namboodiri. (2017). "LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION." Web.
1. Koustav Mullick, Anoop M. Namboodiri. LEARNING DEEP AND COMPACT MODELS FOR GESTURE RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1980

Circle Detection by Arc-support Line Segments


Circle detection is fundamental in both object detection and high accuracy localization in visual control systems. We propose a novel method for circle detection by analysing and refining arc-support line segments. The key idea is to use line segment detector to extract the arc-support line segments which are likely to make up the circle, instead of all line segments. Each couple of line segments is analyzed to form a valid pair and followed by generating initial circle set.

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Authors:
Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu
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29 November 2017 - 5:21am
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Circle detection, arc-support, polarity analysis, line segment, circle fitting

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[1] Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu, "Circle Detection by Arc-support Line Segments", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1978. Accessed: Jan. 17, 2019.
@article{1978-17,
url = {http://sigport.org/1978},
author = {Changsheng Lu; Siyu Xia; Wanming Huang; Ming Shao and Yun Fu },
publisher = {IEEE SigPort},
title = {Circle Detection by Arc-support Line Segments},
year = {2017} }
TY - EJOUR
T1 - Circle Detection by Arc-support Line Segments
AU - Changsheng Lu; Siyu Xia; Wanming Huang; Ming Shao and Yun Fu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1978
ER -
Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu. (2017). Circle Detection by Arc-support Line Segments. IEEE SigPort. http://sigport.org/1978
Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu, 2017. Circle Detection by Arc-support Line Segments. Available at: http://sigport.org/1978.
Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu. (2017). "Circle Detection by Arc-support Line Segments." Web.
1. Changsheng Lu, Siyu Xia, Wanming Huang, Ming Shao and Yun Fu. Circle Detection by Arc-support Line Segments [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1978

UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM

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Authors:
Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo
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13 September 2017 - 12:04pm
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ICIP_poster_0913.pdf

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[1] Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo, "UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1977. Accessed: Jan. 17, 2019.
@article{1977-17,
url = {http://sigport.org/1977},
author = {Xiang Zhang; Wenju Zhang; Xuhui Huang; Naiyang Guan; Zhigang Luo },
publisher = {IEEE SigPort},
title = {UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM},
year = {2017} }
TY - EJOUR
T1 - UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM
AU - Xiang Zhang; Wenju Zhang; Xuhui Huang; Naiyang Guan; Zhigang Luo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1977
ER -
Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo. (2017). UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM. IEEE SigPort. http://sigport.org/1977
Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo, 2017. UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM. Available at: http://sigport.org/1977.
Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo. (2017). "UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM." Web.
1. Xiang Zhang, Wenju Zhang, Xuhui Huang, Naiyang Guan, Zhigang Luo. UNSUPERVISED DOMAIN ADAPTATION WITH JOINT SUPERVISED SPARSE CODING AND DISCRIMINATIVE REGULARIZATION TERM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1977

BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS


Blind image deblurring (BID) is an ill-posed inverse problem, usually addressed by imposing prior knowledge on the (unknown) image and on the blurring filter. Most of the work on BID has focused on natural images, using image priors based on statistical properties of generic natural images. However, in many applications, it is known that the image being recovered belongs to some specific class (e.g., text, face, fingerprints), and exploiting this knowledge allows obtaining more accurate priors.

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Authors:
Marina Ljubenovic, Mario A. T. Figueiredo
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13 September 2017 - 8:29am
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Presentation_Blind image deblurring using class adapted image priors .pdf

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[1] Marina Ljubenovic, Mario A. T. Figueiredo, "BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1973. Accessed: Jan. 17, 2019.
@article{1973-17,
url = {http://sigport.org/1973},
author = {Marina Ljubenovic; Mario A. T. Figueiredo },
publisher = {IEEE SigPort},
title = {BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS},
year = {2017} }
TY - EJOUR
T1 - BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS
AU - Marina Ljubenovic; Mario A. T. Figueiredo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1973
ER -
Marina Ljubenovic, Mario A. T. Figueiredo. (2017). BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS. IEEE SigPort. http://sigport.org/1973
Marina Ljubenovic, Mario A. T. Figueiredo, 2017. BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS. Available at: http://sigport.org/1973.
Marina Ljubenovic, Mario A. T. Figueiredo. (2017). "BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS." Web.
1. Marina Ljubenovic, Mario A. T. Figueiredo. BLIND IMAGE DEBLURRING USING CLASS-ADAPTED IMAGE PRIORS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1973

Landmark Based Head Pose Estimation Benchmark and Method


Head pose estimation can help in understanding human behavior or to improve head pose invariance in various face analysis applications. Ready-to-use pose estimators are available with several facial landmark trackers, but their accuracy is commonly unknown. Following the goal to find the best landmark based pose estimator, we introduce a new database (called SyLaHP), propose a new benchmark protocol, and describe and implement a method to learn a pose estimator on top of any landmark detector (called HPFL).

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Authors:
Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi
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13 September 2017 - 7:51am
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Poster.pdf

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[1] Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi, "Landmark Based Head Pose Estimation Benchmark and Method", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1971. Accessed: Jan. 17, 2019.
@article{1971-17,
url = {http://sigport.org/1971},
author = {Philipp Werner; Frerk Saxen; Ayoub Al-Hamadi },
publisher = {IEEE SigPort},
title = {Landmark Based Head Pose Estimation Benchmark and Method},
year = {2017} }
TY - EJOUR
T1 - Landmark Based Head Pose Estimation Benchmark and Method
AU - Philipp Werner; Frerk Saxen; Ayoub Al-Hamadi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1971
ER -
Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi. (2017). Landmark Based Head Pose Estimation Benchmark and Method. IEEE SigPort. http://sigport.org/1971
Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi, 2017. Landmark Based Head Pose Estimation Benchmark and Method. Available at: http://sigport.org/1971.
Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi. (2017). "Landmark Based Head Pose Estimation Benchmark and Method." Web.
1. Philipp Werner, Frerk Saxen, Ayoub Al-Hamadi. Landmark Based Head Pose Estimation Benchmark and Method [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1971

Tiny Head Pose Classification by Bodily Cues


The head pose is an important cue for computer vision. Traditionally considered in human computer interaction applications,
it becomes very hard to model in surveillance scenarios, due to the tiny head size. Additionally, no public dataset contains continuous head pose annotations in open scenery, making the challenge even harder to face. Here we present a
framework based on Faster RCNN, which introduces a branch in the network architecture related to the head pose estimation.

Paper Details

Authors:
Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani
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14 September 2017 - 5:40am
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ICIP1701

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[1] Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani, "Tiny Head Pose Classification by Bodily Cues", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1968. Accessed: Jan. 17, 2019.
@article{1968-17,
url = {http://sigport.org/1968},
author = {Irtiza Hasan; Theodore Tsesmelis; Fabio Galasso ; Alessio Del Bue; Marco Cristani },
publisher = {IEEE SigPort},
title = {Tiny Head Pose Classification by Bodily Cues},
year = {2017} }
TY - EJOUR
T1 - Tiny Head Pose Classification by Bodily Cues
AU - Irtiza Hasan; Theodore Tsesmelis; Fabio Galasso ; Alessio Del Bue; Marco Cristani
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1968
ER -
Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani. (2017). Tiny Head Pose Classification by Bodily Cues. IEEE SigPort. http://sigport.org/1968
Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani, 2017. Tiny Head Pose Classification by Bodily Cues. Available at: http://sigport.org/1968.
Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani. (2017). "Tiny Head Pose Classification by Bodily Cues." Web.
1. Irtiza Hasan, Theodore Tsesmelis, Fabio Galasso , Alessio Del Bue, Marco Cristani. Tiny Head Pose Classification by Bodily Cues [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1968

A New High Precision Eye Center LocalizationTechnique


Eyes represent the most distinctive features of the human face, while their position and movements are a significant source of information about the cognitive and affective state of humans. Precise eye center localization constitutes a challenging problem in many human-computer interaction applications. In this work, an automatic, non-intrusive method is introduced for the precise eye center localization,based on a modified version of the Fast Radial Symmetry Transform.

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Authors:
Nikolaos Poulopoulos, Emmanouil Psarakis
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13 September 2017 - 4:10am
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A new high precision eye center localization technique.pdf

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[1] Nikolaos Poulopoulos, Emmanouil Psarakis, "A New High Precision Eye Center LocalizationTechnique", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1963. Accessed: Jan. 17, 2019.
@article{1963-17,
url = {http://sigport.org/1963},
author = {Nikolaos Poulopoulos; Emmanouil Psarakis },
publisher = {IEEE SigPort},
title = {A New High Precision Eye Center LocalizationTechnique},
year = {2017} }
TY - EJOUR
T1 - A New High Precision Eye Center LocalizationTechnique
AU - Nikolaos Poulopoulos; Emmanouil Psarakis
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1963
ER -
Nikolaos Poulopoulos, Emmanouil Psarakis. (2017). A New High Precision Eye Center LocalizationTechnique. IEEE SigPort. http://sigport.org/1963
Nikolaos Poulopoulos, Emmanouil Psarakis, 2017. A New High Precision Eye Center LocalizationTechnique. Available at: http://sigport.org/1963.
Nikolaos Poulopoulos, Emmanouil Psarakis. (2017). "A New High Precision Eye Center LocalizationTechnique." Web.
1. Nikolaos Poulopoulos, Emmanouil Psarakis. A New High Precision Eye Center LocalizationTechnique [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1963

Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons


In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map.

Paper Details

Authors:
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib
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13 September 2017 - 12:20am
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Oral Presentation

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[1] Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib, "Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1960. Accessed: Jan. 17, 2019.
@article{1960-17,
url = {http://sigport.org/1960},
author = {Muhammad Amir Shafiq; Zhiling Long; Tariq Alshawi; Ghassan AlRegib },
publisher = {IEEE SigPort},
title = {Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons},
year = {2017} }
TY - EJOUR
T1 - Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons
AU - Muhammad Amir Shafiq; Zhiling Long; Tariq Alshawi; Ghassan AlRegib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1960
ER -
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. (2017). Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons. IEEE SigPort. http://sigport.org/1960
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib, 2017. Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons. Available at: http://sigport.org/1960.
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. (2017). "Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons." Web.
1. Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1960

Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons


In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map.

Paper Details

Authors:
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib
Submitted On:
13 September 2017 - 12:20am
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Oral Presentation

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[1] Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib, "Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1959. Accessed: Jan. 17, 2019.
@article{1959-17,
url = {http://sigport.org/1959},
author = {Muhammad Amir Shafiq; Zhiling Long; Tariq Alshawi; Ghassan AlRegib },
publisher = {IEEE SigPort},
title = {Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons},
year = {2017} }
TY - EJOUR
T1 - Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons
AU - Muhammad Amir Shafiq; Zhiling Long; Tariq Alshawi; Ghassan AlRegib
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1959
ER -
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. (2017). Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons. IEEE SigPort. http://sigport.org/1959
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib, 2017. Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons. Available at: http://sigport.org/1959.
Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. (2017). "Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons." Web.
1. Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib. Saliency Detection for Seismic Applications Using Multi-dimensional Spectral Projections and Directional Comparisons [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1959

Focus Prior Estimation for Salient Object Detection


In the past five years, salient object detection has become one of the hot topics in the field of computer vision. Focus is a naturally strong indicator for the salient object detection task, but is not well studied. A novel method is proposed in this paper to estimate the focus prior map for an arbitrary image. Different from the current edge density estimation based methods, the proposed method is based on the sparse defocus dictionary learning at a newly designed dataset. The focus strength is measured by the number of non-zero coefficients of the dictionary atoms.

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Authors:
Xiujun Zhang, Wenbin Zou, Chen Xu
Submitted On:
12 September 2017 - 11:29pm
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Slide for icip 2017 and paper 1821, PDF

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Slide for icip 2017 and paper 1821, pptx

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Poster for icip 2017 and paper 1821

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[1] Xiujun Zhang, Wenbin Zou, Chen Xu, "Focus Prior Estimation for Salient Object Detection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1956. Accessed: Jan. 17, 2019.
@article{1956-17,
url = {http://sigport.org/1956},
author = {Xiujun Zhang; Wenbin Zou; Chen Xu },
publisher = {IEEE SigPort},
title = {Focus Prior Estimation for Salient Object Detection},
year = {2017} }
TY - EJOUR
T1 - Focus Prior Estimation for Salient Object Detection
AU - Xiujun Zhang; Wenbin Zou; Chen Xu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1956
ER -
Xiujun Zhang, Wenbin Zou, Chen Xu. (2017). Focus Prior Estimation for Salient Object Detection. IEEE SigPort. http://sigport.org/1956
Xiujun Zhang, Wenbin Zou, Chen Xu, 2017. Focus Prior Estimation for Salient Object Detection. Available at: http://sigport.org/1956.
Xiujun Zhang, Wenbin Zou, Chen Xu. (2017). "Focus Prior Estimation for Salient Object Detection." Web.
1. Xiujun Zhang, Wenbin Zou, Chen Xu. Focus Prior Estimation for Salient Object Detection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1956

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