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Image/Video Processing

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
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12 September 2017 - 11:29pm
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Slide for icip 2017 and paper 1821, PDF

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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: Oct. 16, 2018.
@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

FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS

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Authors:
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan
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12 September 2017 - 9:22pm
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[1] Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan, "FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1951. Accessed: Oct. 16, 2018.
@article{1951-17,
url = {http://sigport.org/1951},
author = {Sarasi Kankanamge; Clinton Fookes; Sridha Sridharan },
publisher = {IEEE SigPort},
title = {FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS},
year = {2017} }
TY - EJOUR
T1 - FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS
AU - Sarasi Kankanamge; Clinton Fookes; Sridha Sridharan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1951
ER -
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. (2017). FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS. IEEE SigPort. http://sigport.org/1951
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan, 2017. FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS. Available at: http://sigport.org/1951.
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. (2017). "FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS." Web.
1. Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1951

FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS

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Authors:
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan
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12 September 2017 - 9:22pm
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ICIP_PresentationSlides_PaperID_2481.pdf

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[1] Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan, "FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1950. Accessed: Oct. 16, 2018.
@article{1950-17,
url = {http://sigport.org/1950},
author = {Sarasi Kankanamge; Clinton Fookes; Sridha Sridharan },
publisher = {IEEE SigPort},
title = {FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS},
year = {2017} }
TY - EJOUR
T1 - FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS
AU - Sarasi Kankanamge; Clinton Fookes; Sridha Sridharan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1950
ER -
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. (2017). FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS. IEEE SigPort. http://sigport.org/1950
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan, 2017. FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS. Available at: http://sigport.org/1950.
Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. (2017). "FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS." Web.
1. Sarasi Kankanamge, Clinton Fookes, Sridha Sridharan. FACIAL ANALYSIS IN THE WILD WITH LSTM NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1950

LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL


Current object segmentation algorithms are based on the hypothesis that one has access to a very large amount of data. In this paper, we aim to segment objects using only tiny datasets. To this extent, we propose a new automatic part-based object segmentation algorithm for non-deformable and semi-deformable objects in natural backgrounds. We have developed a novel shape descriptor which models the local boundaries of an object's part. This shape descriptor is used in a bag-of-words approach for object detection.

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Authors:
Maxime Tremblay, André Zaccarin
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12 September 2017 - 11:38am
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Poster at ICIP2017

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[1] Maxime Tremblay, André Zaccarin, "LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1945. Accessed: Oct. 16, 2018.
@article{1945-17,
url = {http://sigport.org/1945},
author = {Maxime Tremblay; André Zaccarin },
publisher = {IEEE SigPort},
title = {LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL},
year = {2017} }
TY - EJOUR
T1 - LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL
AU - Maxime Tremblay; André Zaccarin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1945
ER -
Maxime Tremblay, André Zaccarin. (2017). LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL. IEEE SigPort. http://sigport.org/1945
Maxime Tremblay, André Zaccarin, 2017. LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL. Available at: http://sigport.org/1945.
Maxime Tremblay, André Zaccarin. (2017). "LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL." Web.
1. Maxime Tremblay, André Zaccarin. LEARNING TO SEGMENT ON TINY DATASETS: A NEW SHAPE MODEL [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1945

A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images

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Authors:
Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup
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12 September 2017 - 11:00am
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ICIP2017_Heindel_Poster_final.pdf

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[1] Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup, "A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1944. Accessed: Oct. 16, 2018.
@article{1944-17,
url = {http://sigport.org/1944},
author = {Eugen Wige; Felix Fleckenstein; Benjamin Prestele; Alexander Gehlert; and André Kaup },
publisher = {IEEE SigPort},
title = {A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images},
year = {2017} }
TY - EJOUR
T1 - A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images
AU - Eugen Wige; Felix Fleckenstein; Benjamin Prestele; Alexander Gehlert; and André Kaup
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1944
ER -
Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup. (2017). A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images. IEEE SigPort. http://sigport.org/1944
Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup, 2017. A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images. Available at: http://sigport.org/1944.
Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup. (2017). "A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images." Web.
1. Eugen Wige, Felix Fleckenstein, Benjamin Prestele, Alexander Gehlert, and André Kaup. A Low-Complexity Metric for the Estimation of Perceived Chrominance Sub-Sampling Errors in Screen Content Images [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1944

WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP

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Authors:
Zhiqiang Wan, Haibo He
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12 September 2017 - 10:04am
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[1] Zhiqiang Wan, Haibo He, "WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1937. Accessed: Oct. 16, 2018.
@article{1937-17,
url = {http://sigport.org/1937},
author = {Zhiqiang Wan; Haibo He },
publisher = {IEEE SigPort},
title = {WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP},
year = {2017} }
TY - EJOUR
T1 - WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP
AU - Zhiqiang Wan; Haibo He
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1937
ER -
Zhiqiang Wan, Haibo He. (2017). WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP. IEEE SigPort. http://sigport.org/1937
Zhiqiang Wan, Haibo He, 2017. WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP. Available at: http://sigport.org/1937.
Zhiqiang Wan, Haibo He. (2017). "WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP." Web.
1. Zhiqiang Wan, Haibo He. WEAKLY SUPERVISED OBJECT LOCALIZATION WITH DEEP CONVOLUTIONAL NEURAL NETWORK BASED ON SPATIAL PYRAMID SALIENCY MAP [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1937

JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM

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Authors:
Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang
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25 September 2017 - 4:08am
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Tan_ICIP2017_poster_landscape.pdf

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[1] Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang, "JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1935. Accessed: Oct. 16, 2018.
@article{1935-17,
url = {http://sigport.org/1935},
author = {Xiangrong Zeng; Shiming Lai; Yu Liu; Maojun Zhang },
publisher = {IEEE SigPort},
title = {JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM},
year = {2017} }
TY - EJOUR
T1 - JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM
AU - Xiangrong Zeng; Shiming Lai; Yu Liu; Maojun Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1935
ER -
Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang. (2017). JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM. IEEE SigPort. http://sigport.org/1935
Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang, 2017. JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM. Available at: http://sigport.org/1935.
Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang. (2017). "JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM." Web.
1. Xiangrong Zeng, Shiming Lai, Yu Liu, Maojun Zhang. JOINT DEMOSAICING AND DENOISING OF NOISY BAYER IMAGES WITH ADMM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1935

JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES


In this paper, we present a novel image representation approach for classifying emphysema in computed tomography (CT) images of the lung. Our proposed method extends rotation invariant uniform local binary pattern (RIULBP) and local ternary pattern (LTP), which are extensively used in a variety of computer vision applications, into rotation invariant uniform local ternary pattern (RIULTP) with a human perception principle: Weber’s law.

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12 September 2017 - 7:56am
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[1] , "JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1934. Accessed: Oct. 16, 2018.
@article{1934-17,
url = {http://sigport.org/1934},
author = { },
publisher = {IEEE SigPort},
title = {JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES},
year = {2017} }
TY - EJOUR
T1 - JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1934
ER -
. (2017). JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES. IEEE SigPort. http://sigport.org/1934
, 2017. JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES. Available at: http://sigport.org/1934.
. (2017). "JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES." Web.
1. . JOINT WEBER-BASED ROTATION INVARIANT UNIFORM LOCAL TERNARY PATTERN FOR CLASSIFICATION OF PULMONARY EMPHYSEMA IN CT IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1934

LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL


We consider the task of removing the effect of camera shake during a long exposure. Technically, this is a blind deconvolution problem in which both the image and the motion blur have to be jointly inferred. Several algorithms have been proposed till date for removing camera shake that work with one or more images. However, most of these algorithms are computationally expensive and hence cannot be used in real-time.

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Authors:
Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury
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12 September 2017 - 6:11am
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LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL

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[1] Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury, "LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1933. Accessed: Oct. 16, 2018.
@article{1933-17,
url = {http://sigport.org/1933},
author = {Sanjay Ghosh; Satyajit Naik; and Kunal N. Chaudhury },
publisher = {IEEE SigPort},
title = {LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL},
year = {2017} }
TY - EJOUR
T1 - LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL
AU - Sanjay Ghosh; Satyajit Naik; and Kunal N. Chaudhury
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1933
ER -
Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury. (2017). LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL. IEEE SigPort. http://sigport.org/1933
Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury, 2017. LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL. Available at: http://sigport.org/1933.
Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury. (2017). "LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL." Web.
1. Sanjay Ghosh, Satyajit Naik, and Kunal N. Chaudhury. LUCKY DCT AGGREGATION FOR CAMERA SHAKE REMOVAL [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1933

SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES


In this paper, we propose a novel object proposal generation scheme by formulating a graph-based salient edge classification framework that utilizes the edge context. In the proposed method, we construct a Bayesian probabilistic edge map to assign a saliency value to the edgelets by exploiting low level edge features. A Conditional Random Field is then learned to effectively combine these features for edge classification with object/non-object label. We propose an objectness score for the generated windows by analyzing the salient edge density inside the bounding box.

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Authors:
PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON
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12 September 2017 - 6:11am
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SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES

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[1] PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON, "SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1932. Accessed: Oct. 16, 2018.
@article{1932-17,
url = {http://sigport.org/1932},
author = {PRERANA MUKHERJEE; BREJESH LALL; SARVASWA TANDON },
publisher = {IEEE SigPort},
title = {SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES},
year = {2017} }
TY - EJOUR
T1 - SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES
AU - PRERANA MUKHERJEE; BREJESH LALL; SARVASWA TANDON
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1932
ER -
PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON. (2017). SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES. IEEE SigPort. http://sigport.org/1932
PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON, 2017. SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES. Available at: http://sigport.org/1932.
PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON. (2017). "SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES." Web.
1. PRERANA MUKHERJEE, BREJESH LALL, SARVASWA TANDON. SALPROP: SALIENT OBJECT PROPOSALS VIA AGGREGATED EDGE CUES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1932

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