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Image/Video Processing

Blind Image Deblurring via Reweighted Graph Total Variation

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Authors:
Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao
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19 April 2018 - 11:59pm
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ICASSP2018_2.pdf

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[1] Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao, "Blind Image Deblurring via Reweighted Graph Total Variation ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3065. Accessed: Jul. 18, 2018.
@article{3065-18,
url = {http://sigport.org/3065},
author = {Yuanchao Bai; Gene Cheung; Xianming Liu; Wen Gao },
publisher = {IEEE SigPort},
title = {Blind Image Deblurring via Reweighted Graph Total Variation },
year = {2018} }
TY - EJOUR
T1 - Blind Image Deblurring via Reweighted Graph Total Variation
AU - Yuanchao Bai; Gene Cheung; Xianming Liu; Wen Gao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3065
ER -
Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao. (2018). Blind Image Deblurring via Reweighted Graph Total Variation . IEEE SigPort. http://sigport.org/3065
Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao, 2018. Blind Image Deblurring via Reweighted Graph Total Variation . Available at: http://sigport.org/3065.
Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao. (2018). "Blind Image Deblurring via Reweighted Graph Total Variation ." Web.
1. Yuanchao Bai, Gene Cheung, Xianming Liu, Wen Gao. Blind Image Deblurring via Reweighted Graph Total Variation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3065

CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES

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19 April 2018 - 11:35pm
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ICASSP 2018

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[1] , "CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3062. Accessed: Jul. 18, 2018.
@article{3062-18,
url = {http://sigport.org/3062},
author = { },
publisher = {IEEE SigPort},
title = {CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES},
year = {2018} }
TY - EJOUR
T1 - CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3062
ER -
. (2018). CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES. IEEE SigPort. http://sigport.org/3062
, 2018. CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES. Available at: http://sigport.org/3062.
. (2018). "CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES." Web.
1. . CALIBRATING CAMERAS IN POOR-CONDITIONED PITCH-BASED SPORTS GAMES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3062

IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION

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Authors:
Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou
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19 April 2018 - 11:35pm
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the slides for image alignment

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[1] Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou, "IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3061. Accessed: Jul. 18, 2018.
@article{3061-18,
url = {http://sigport.org/3061},
author = {Yue Jiao; Jingyu Yang; Huanjing Yue; Kun Li; Chunping Hou },
publisher = {IEEE SigPort},
title = {IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION
AU - Yue Jiao; Jingyu Yang; Huanjing Yue; Kun Li; Chunping Hou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3061
ER -
Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou. (2018). IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION. IEEE SigPort. http://sigport.org/3061
Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou, 2018. IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION. Available at: http://sigport.org/3061.
Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou. (2018). "IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION." Web.
1. Yue Jiao, Jingyu Yang, Huanjing Yue, Kun Li, Chunping Hou. IMAGE ALIGNMENT VIA MULTI-MODEL GEOMETRIC FIFTING AND HIERARCHICAL HOMOGRAPHY ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3061

SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION

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Authors:
Xiantong Zhen; Qiujing Zhang
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19 April 2018 - 7:17pm
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zlei_icassp2018.pdf

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[1] Xiantong Zhen; Qiujing Zhang, "SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3039. Accessed: Jul. 18, 2018.
@article{3039-18,
url = {http://sigport.org/3039},
author = {Xiantong Zhen; Qiujing Zhang },
publisher = {IEEE SigPort},
title = {SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION},
year = {2018} }
TY - EJOUR
T1 - SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION
AU - Xiantong Zhen; Qiujing Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3039
ER -
Xiantong Zhen; Qiujing Zhang. (2018). SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION. IEEE SigPort. http://sigport.org/3039
Xiantong Zhen; Qiujing Zhang, 2018. SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION. Available at: http://sigport.org/3039.
Xiantong Zhen; Qiujing Zhang. (2018). "SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION." Web.
1. Xiantong Zhen; Qiujing Zhang. SPATIAL ENSEMBLE KERNEL LEARNING FOR SCENE CLASSIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3039

Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation


In this paper, we present one saliency-based feature selection and tracking strategy in the feature-based stereoscopic panoramic video generation system. Many existing stereoscopic video composition approaches aims at producing high-quality panoramas from multiple input cameras; however, most of them directly operate image alignment on those originally detected features without any refinement or optimization. The standard global feature extraction threshold always fails to guarantee stitching correctness of all human interested regions.

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Authors:
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld
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19 April 2018 - 5:06pm
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ICASSP_2018_20180416.pdf

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[1] Haoyu Wang, Daniel J. Sandin, Dan Schonfeld, "Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3028. Accessed: Jul. 18, 2018.
@article{3028-18,
url = {http://sigport.org/3028},
author = {Haoyu Wang; Daniel J. Sandin; Dan Schonfeld },
publisher = {IEEE SigPort},
title = {Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation},
year = {2018} }
TY - EJOUR
T1 - Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation
AU - Haoyu Wang; Daniel J. Sandin; Dan Schonfeld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3028
ER -
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. (2018). Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation. IEEE SigPort. http://sigport.org/3028
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld, 2018. Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation. Available at: http://sigport.org/3028.
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. (2018). "Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation." Web.
1. Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3028

A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT


Multi-image alignment, bringing a group of images into common register, is an ubiquitous problem and the first step of many applications in a wide variety of domains. As a result, a great amount of effort is being invested in developing efficient multi-image alignment algorithms. Little has been done, however, to answer fundamental practical questions such as: what is the comparative performance of existing methods? is there still room for improvement? under which conditions should one technique be preferred over another?

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Authors:
Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro
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19 April 2018 - 4:38pm
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ICASSP2018-multiimage-alignment-v2.2.pdf

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[1] Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro, "A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3022. Accessed: Jul. 18, 2018.
@article{3022-18,
url = {http://sigport.org/3022},
author = {Cecilia Aguerrebere; Mauricio Delbracio; Alberto Bartesaghi; Guillermo Sapiro },
publisher = {IEEE SigPort},
title = {A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT},
year = {2018} }
TY - EJOUR
T1 - A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT
AU - Cecilia Aguerrebere; Mauricio Delbracio; Alberto Bartesaghi; Guillermo Sapiro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3022
ER -
Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro. (2018). A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT. IEEE SigPort. http://sigport.org/3022
Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro, 2018. A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT. Available at: http://sigport.org/3022.
Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro. (2018). "A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT." Web.
1. Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro. A PRACTICAL GUIDE TO MULTI-IMAGE ALIGNMENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3022

MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS


Embedding textured images in manifolds reveals latent information regarding texture structure and allows useful analysis of these high dimensional images in a low dimensional space. We present a framework for analysis and synthesis of natural stochastic textures (NST) which constitute an important subset of textures that are modelled as realizations of random processes. The randomness of NST differentiates them from other types of images and requires a dedicated method for analysis and synthesis. We demonstrate several applications of this framework.

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Authors:
Ido Zachevsky, Yehoshua Y. Zeevi
Submitted On:
19 April 2018 - 3:37pm
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ido-z-icassp18-talk.pdf

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[1] Ido Zachevsky, Yehoshua Y. Zeevi, "MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3013. Accessed: Jul. 18, 2018.
@article{3013-18,
url = {http://sigport.org/3013},
author = {Ido Zachevsky; Yehoshua Y. Zeevi },
publisher = {IEEE SigPort},
title = {MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS},
year = {2018} }
TY - EJOUR
T1 - MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS
AU - Ido Zachevsky; Yehoshua Y. Zeevi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3013
ER -
Ido Zachevsky, Yehoshua Y. Zeevi. (2018). MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS. IEEE SigPort. http://sigport.org/3013
Ido Zachevsky, Yehoshua Y. Zeevi, 2018. MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS. Available at: http://sigport.org/3013.
Ido Zachevsky, Yehoshua Y. Zeevi. (2018). "MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS." Web.
1. Ido Zachevsky, Yehoshua Y. Zeevi. MANIFOLD-BASED ANALYSIS OF NATURAL STOCHASTIC TEXTURES WITH APPLICATION IN TEXTURE SYNTHESIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3013

A sparse coding framework for gaze prediction in egocentric video

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Authors:
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe
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18 April 2018 - 10:23pm
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20180418ICASSP_official_.pdf

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[1] Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, "A sparse coding framework for gaze prediction in egocentric video", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2977. Accessed: Jul. 18, 2018.
@article{2977-18,
url = {http://sigport.org/2977},
author = {Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe },
publisher = {IEEE SigPort},
title = {A sparse coding framework for gaze prediction in egocentric video},
year = {2018} }
TY - EJOUR
T1 - A sparse coding framework for gaze prediction in egocentric video
AU - Yujie Li; Atsunori Kanemura; Hideki Asoh; Taiki Miyanishi; Motoaki Kawanabe
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2977
ER -
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2018). A sparse coding framework for gaze prediction in egocentric video. IEEE SigPort. http://sigport.org/2977
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe, 2018. A sparse coding framework for gaze prediction in egocentric video. Available at: http://sigport.org/2977.
Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. (2018). "A sparse coding framework for gaze prediction in egocentric video." Web.
1. Yujie Li, Atsunori Kanemura, Hideki Asoh, Taiki Miyanishi, Motoaki Kawanabe. A sparse coding framework for gaze prediction in egocentric video [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2977

A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS


This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents’ motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents’ displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models.

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Authors:
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni
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18 April 2018 - 10:40am
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SS-L2.5 A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS.pdf

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[1] Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni, "A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2964. Accessed: Jul. 18, 2018.
@article{2964-18,
url = {http://sigport.org/2964},
author = {Mohamad Baydoun; Mahdyar Ravanbakhsh; Damian Campo; Pablo Marin; David Martin; Lucio Marcenaro; Andrea Cavallaro; Carlo S. Regazzoni },
publisher = {IEEE SigPort},
title = {A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS},
year = {2018} }
TY - EJOUR
T1 - A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS
AU - Mohamad Baydoun; Mahdyar Ravanbakhsh; Damian Campo; Pablo Marin; David Martin; Lucio Marcenaro; Andrea Cavallaro; Carlo S. Regazzoni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2964
ER -
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. (2018). A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS. IEEE SigPort. http://sigport.org/2964
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni, 2018. A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS. Available at: http://sigport.org/2964.
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. (2018). "A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS." Web.
1. Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2964

A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving


Object detection is a fundamental process in traffic management systems and self-driving cars. Deformable part model (DPM) is a popular and competitive detector for its high precision. This paper presents a programmable, low power hardware implementation of DPM based object detection for real-time applications.

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Authors:
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi
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14 April 2018 - 11:50pm
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Alaa-Poster-36Wx39H.pdf

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[1] Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi, "A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2873. Accessed: Jul. 18, 2018.
@article{2873-18,
url = {http://sigport.org/2873},
author = {Oladiran G. Olaleye; Bappaditya Dey; Kasem Khalil; Magdy A. Bayoumi },
publisher = {IEEE SigPort},
title = {A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving},
year = {2018} }
TY - EJOUR
T1 - A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving
AU - Oladiran G. Olaleye; Bappaditya Dey; Kasem Khalil; Magdy A. Bayoumi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2873
ER -
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. (2018). A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving. IEEE SigPort. http://sigport.org/2873
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi, 2018. A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving. Available at: http://sigport.org/2873.
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. (2018). "A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving." Web.
1. Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2873

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