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Image/Video Storage, Retrieval

PERSON RE-IDENTIFICATION USING VISUAL ATTENTION


Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person’s appearance can vary significantly when large variations in view angle, human pose and illumination are involved. The concept of attention is one of the most interesting recent architectural innovations in neural networks. Inspired by that, in this paper we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem.

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Authors:
Hairong Qi
Submitted On:
14 September 2017 - 4:12pm
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Poster

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[1] Hairong Qi, "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2046. Accessed: Feb. 18, 2020.
@article{2046-17,
url = {http://sigport.org/2046},
author = {Hairong Qi },
publisher = {IEEE SigPort},
title = {PERSON RE-IDENTIFICATION USING VISUAL ATTENTION},
year = {2017} }
TY - EJOUR
T1 - PERSON RE-IDENTIFICATION USING VISUAL ATTENTION
AU - Hairong Qi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2046
ER -
Hairong Qi. (2017). PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. IEEE SigPort. http://sigport.org/2046
Hairong Qi, 2017. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION. Available at: http://sigport.org/2046.
Hairong Qi. (2017). "PERSON RE-IDENTIFICATION USING VISUAL ATTENTION." Web.
1. Hairong Qi. PERSON RE-IDENTIFICATION USING VISUAL ATTENTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2046

LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY


Binary hashing is a practical approach for fast, approximate retrieval in large image databases. The goal is to learn a hash function that maps images onto a binary vector such that Hamming distances approximate semantic similarities. The search is then fast by using hardware support for binary operations. Most hashing papers define a complicated objective function that couples the single-bit hash functions.

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Authors:
Ramin Raziperchikolaei, Miguel Carreira-Perpinan
Submitted On:
14 September 2017 - 3:11pm
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icip17a-poster.pdf

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[1] Ramin Raziperchikolaei, Miguel Carreira-Perpinan, "LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2045. Accessed: Feb. 18, 2020.
@article{2045-17,
url = {http://sigport.org/2045},
author = {Ramin Raziperchikolaei; Miguel Carreira-Perpinan },
publisher = {IEEE SigPort},
title = {LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY},
year = {2017} }
TY - EJOUR
T1 - LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY
AU - Ramin Raziperchikolaei; Miguel Carreira-Perpinan
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2045
ER -
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY. IEEE SigPort. http://sigport.org/2045
Ramin Raziperchikolaei, Miguel Carreira-Perpinan, 2017. LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY. Available at: http://sigport.org/2045.
Ramin Raziperchikolaei, Miguel Carreira-Perpinan. (2017). "LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY." Web.
1. Ramin Raziperchikolaei, Miguel Carreira-Perpinan. LEARNING SUPERVISED BINARY HASHING: OPTIMIZATION VS DIVERSITY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2045

ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING


We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.

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Authors:
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Submitted On:
14 September 2017 - 11:24am
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Enhance feature discrimination for Unsupervised hashing - poster.pdf

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[1] Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, "ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2038. Accessed: Feb. 18, 2020.
@article{2038-17,
url = {http://sigport.org/2038},
author = {Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung },
publisher = {IEEE SigPort},
title = {ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING},
year = {2017} }
TY - EJOUR
T1 - ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING
AU - Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2038
ER -
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING. IEEE SigPort. http://sigport.org/2038
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, 2017. ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING. Available at: http://sigport.org/2038.
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). "ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING." Web.
1. Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. ENHANCING FEATURE DISCRIMINATION FOR UNSUPERVISED HASHING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2038

Enhance feature discrimination for Unsupervised hashing


We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.

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Authors:
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Submitted On:
14 September 2017 - 11:17am
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Enhance feature%0D%0Adiscrimination for Unsupervised hashing - poster.pdf

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[1] Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, "Enhance feature discrimination for Unsupervised hashing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2037. Accessed: Feb. 18, 2020.
@article{2037-17,
url = {http://sigport.org/2037},
author = {Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung },
publisher = {IEEE SigPort},
title = {Enhance feature discrimination for Unsupervised hashing},
year = {2017} }
TY - EJOUR
T1 - Enhance feature discrimination for Unsupervised hashing
AU - Tuan Hoang; Thanh-Toan Do; Dang-Khoa Le Tan; Ngai-Man Cheung
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2037
ER -
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). Enhance feature discrimination for Unsupervised hashing. IEEE SigPort. http://sigport.org/2037
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung, 2017. Enhance feature discrimination for Unsupervised hashing. Available at: http://sigport.org/2037.
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. (2017). "Enhance feature discrimination for Unsupervised hashing." Web.
1. Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung. Enhance feature discrimination for Unsupervised hashing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2037

Multi-view deep metric learning for image classification

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Authors:
Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju
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14 September 2017 - 10:53am
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poster_MVDML.pdf

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[1] Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju, "Multi-view deep metric learning for image classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2034. Accessed: Feb. 18, 2020.
@article{2034-17,
url = {http://sigport.org/2034},
author = {Dewei Li; Jingjing Tang; Yingjie Tian; Xuchan Ju },
publisher = {IEEE SigPort},
title = {Multi-view deep metric learning for image classification},
year = {2017} }
TY - EJOUR
T1 - Multi-view deep metric learning for image classification
AU - Dewei Li; Jingjing Tang; Yingjie Tian; Xuchan Ju
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2034
ER -
Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju. (2017). Multi-view deep metric learning for image classification. IEEE SigPort. http://sigport.org/2034
Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju, 2017. Multi-view deep metric learning for image classification. Available at: http://sigport.org/2034.
Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju. (2017). "Multi-view deep metric learning for image classification." Web.
1. Dewei Li, Jingjing Tang, Yingjie Tian, Xuchan Ju. Multi-view deep metric learning for image classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2034

LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL


Matrix factorization based hashing has been very effective in addressing the cross-modal retrieval task. In this work, we propose a novel supervised hashing approach utilizing the concepts of matrix factorization which can seamlessly incorporate the label information. In the proposed approach, the latent factors for each individual modality are generated, which are then converted to the more discriminative label space using modality specific linear transformations.

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Authors:
Devraj Mandal, Soma Biswas
Submitted On:
14 September 2017 - 8:30am
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The poster of the paper.

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[1] Devraj Mandal, Soma Biswas, "LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2023. Accessed: Feb. 18, 2020.
@article{2023-17,
url = {http://sigport.org/2023},
author = {Devraj Mandal; Soma Biswas },
publisher = {IEEE SigPort},
title = {LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL},
year = {2017} }
TY - EJOUR
T1 - LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL
AU - Devraj Mandal; Soma Biswas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2023
ER -
Devraj Mandal, Soma Biswas. (2017). LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL. IEEE SigPort. http://sigport.org/2023
Devraj Mandal, Soma Biswas, 2017. LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL. Available at: http://sigport.org/2023.
Devraj Mandal, Soma Biswas. (2017). "LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL." Web.
1. Devraj Mandal, Soma Biswas. LABEL CONSISTENT MATRIX FACTORIZATION BASED HASHING FOR CROSS-MODAL RETRIEVAL [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2023

Summarization of Human Activity Videos Using a Salient Dictionary


Video summarization has become more prominent during the last decade, due to the massive amount of available digital video content. A video summarization algorithm is typically fed an input video and expected to extract a set of important key-frames which represent the entire content, convey semantic meaning and are significantly more concise than the original input. The most wide-spread approach relies on video frame clustering and extraction of the frames closest to the cluster centroids as key-frames.

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Authors:
Anastasios Tefas, Ioannis Pitas
Submitted On:
13 September 2017 - 11:18am
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Key-frame extraction from human activity videos via salient dictionary learning-based video summarization

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[1] Anastasios Tefas, Ioannis Pitas, "Summarization of Human Activity Videos Using a Salient Dictionary", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1976. Accessed: Feb. 18, 2020.
@article{1976-17,
url = {http://sigport.org/1976},
author = {Anastasios Tefas; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Using a Salient Dictionary},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Using a Salient Dictionary
AU - Anastasios Tefas; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1976
ER -
Anastasios Tefas, Ioannis Pitas. (2017). Summarization of Human Activity Videos Using a Salient Dictionary. IEEE SigPort. http://sigport.org/1976
Anastasios Tefas, Ioannis Pitas, 2017. Summarization of Human Activity Videos Using a Salient Dictionary. Available at: http://sigport.org/1976.
Anastasios Tefas, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Using a Salient Dictionary." Web.
1. Anastasios Tefas, Ioannis Pitas. Summarization of Human Activity Videos Using a Salient Dictionary [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1976

Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics


Video summarization is a timely and rapidly developing research field with broad commercial interest, due to the increasing availability of massive video data. Relevant algorithms face the challenge of needing to achieve a careful balance between summary compactness, enjoyability and content coverage. The specific case of stereoscopic $3$D theatrical films has become more important over the past years, but not received corresponding research attention.

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Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
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13 September 2017 - 11:12am
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Stereoscopic Movie Summarization Conforming to Narrative Characteristics

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1975. Accessed: Feb. 18, 2020.
@article{1975-17,
url = {http://sigport.org/1975},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics},
year = {2017} }
TY - EJOUR
T1 - Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1975
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics. IEEE SigPort. http://sigport.org/1975
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics. Available at: http://sigport.org/1975.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1975

MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING


The multiple types of social media data have abundant information, but learning multi-modal social data is challenging due to data heterogeneity and noise in user-generated data. To address this problem, we propose a multi-view network-based clustering approach that is robust to noise and fully reflects the underlying structure of the comprehensive network. To demonstrate the proposed approach, we experimented with clustering challenging tagged images of landmarks.

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Authors:
So Yeon Kim, Kyung-Ah Sohn
Submitted On:
13 September 2017 - 5:22am
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ICIP2017_poster.pdf

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[1] So Yeon Kim, Kyung-Ah Sohn, "MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1967. Accessed: Feb. 18, 2020.
@article{1967-17,
url = {http://sigport.org/1967},
author = {So Yeon Kim; Kyung-Ah Sohn },
publisher = {IEEE SigPort},
title = {MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING},
year = {2017} }
TY - EJOUR
T1 - MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING
AU - So Yeon Kim; Kyung-Ah Sohn
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1967
ER -
So Yeon Kim, Kyung-Ah Sohn. (2017). MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING. IEEE SigPort. http://sigport.org/1967
So Yeon Kim, Kyung-Ah Sohn, 2017. MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING. Available at: http://sigport.org/1967.
So Yeon Kim, Kyung-Ah Sohn. (2017). "MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING." Web.
1. So Yeon Kim, Kyung-Ah Sohn. MULTI-VIEW NETWORK-BASED SOCIAL-TAGGED LANDMARK IMAGE CLUSTERING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1967

POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION


For 3D object detection and pose estimation, it is crucial to extract distinctive and representative features of the objects and describe them efficiently. Therefore, a large number of 3D feature descriptors has been developed. Among these, Point Feature Histogram RGB (PFHRGB) has been evaluated as showing the best performance for 3D object and category recognition. However, this descriptor is vulnerable to point density variation and produces many false correspondences accordingly.

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Submitted On:
12 September 2017 - 12:20pm
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icip_poster_suakim.pdf

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[1] , "POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1947. Accessed: Feb. 18, 2020.
@article{1947-17,
url = {http://sigport.org/1947},
author = { },
publisher = {IEEE SigPort},
title = {POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION},
year = {2017} }
TY - EJOUR
T1 - POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1947
ER -
. (2017). POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION. IEEE SigPort. http://sigport.org/1947
, 2017. POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION. Available at: http://sigport.org/1947.
. (2017). "POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION." Web.
1. . POINT DENSITY-INVARIANT 3D OBJECT DETECTION AND POSE ESTIMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1947

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