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ICASSP 2018

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS

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12 April 2018 - 11:54am
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[1] , "FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2420. Accessed: May. 25, 2019.
@article{2420-18,
url = {http://sigport.org/2420},
author = { },
publisher = {IEEE SigPort},
title = {FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS},
year = {2018} }
TY - EJOUR
T1 - FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2420
ER -
. (2018). FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS. IEEE SigPort. http://sigport.org/2420
, 2018. FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS. Available at: http://sigport.org/2420.
. (2018). "FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS." Web.
1. . FAST DISTRIBUTED SUBSPACE PROJECTION VIA GRAPH FILTERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2420

GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS


In this paper, we explore the use of graph-basedtransforms to capture correlation in light fields. We consider a scheme in which view synthesis is used as a first step to exploit inter-view correlation. Local graph-based transforms (GT) are then considered for energy compaction of the residue signals. The structure of the local graphs is derived from a coherent super-pixel over-segmentation of the different views. The GT is computed and applied in a separable manner with a first spatial unweighted transform followed by an inter-view GT.

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Authors:
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot
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12 April 2018 - 11:48am
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[1] Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot, "GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2419. Accessed: May. 25, 2019.
@article{2419-18,
url = {http://sigport.org/2419},
author = {Mira Rizkallah; Xin Su; Thomas Maugey and Christine Guillemot },
publisher = {IEEE SigPort},
title = {GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS},
year = {2018} }
TY - EJOUR
T1 - GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS
AU - Mira Rizkallah; Xin Su; Thomas Maugey and Christine Guillemot
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2419
ER -
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. (2018). GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS. IEEE SigPort. http://sigport.org/2419
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot, 2018. GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS. Available at: http://sigport.org/2419.
Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. (2018). "GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS." Web.
1. Mira Rizkallah, Xin Su, Thomas Maugey and Christine Guillemot. GRAPH-BASED TRANSFORMS FOR PREDICTIVE LIGHT FIELD COMPRESSION BASED ON SUPER-PIXELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2419

PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS


Numerical simulations offer a feasible alternative to the direct acoustic measurement of individual head-related transfer functions (HRTFs). For the acquisition of high quality 3D surface scans, as required for these simulations, several approaches exist. In this paper, we systematically analyze the variations between different approaches and evaluate the influence of the accuracy of 3D scans on the resulting simulated HRTFs. To assess this effect, HRTFs were numerically simulated based on 3D scans of the head and pinna of the FABIAN dummy head generated with 6 different methods.

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Authors:
Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl
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12 April 2018 - 11:46am
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[1] Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl, "PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2418. Accessed: May. 25, 2019.
@article{2418-18,
url = {http://sigport.org/2418},
author = {Fabian Brinkmann; Stine Harder; Robert Pelzer; Peter Grosche; Rasmus R. Paulsen and Stefan Weinzierl },
publisher = {IEEE SigPort},
title = {PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS},
year = {2018} }
TY - EJOUR
T1 - PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS
AU - Fabian Brinkmann; Stine Harder; Robert Pelzer; Peter Grosche; Rasmus R. Paulsen and Stefan Weinzierl
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2418
ER -
Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl. (2018). PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS. IEEE SigPort. http://sigport.org/2418
Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl, 2018. PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS. Available at: http://sigport.org/2418.
Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl. (2018). "PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS." Web.
1. Fabian Brinkmann, Stine Harder, Robert Pelzer, Peter Grosche, Rasmus R. Paulsen and Stefan Weinzierl. PERCEPTUALLY MOTIVATED ANALYSIS OF NUMERICALLY SIMULATED HEAD-RELATED TRANSFER FUNCTIONS GENERATED BY VARIOUS 3D SURFACE SCANNING SYSTEMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2418

Hands-on in Signal Processing Education at Technische Universität Darmstadt


This paper is meant to share our experience on signal processing hands-on opportunities within the formal engineering education at Technische Universität Darmstadt. It is our strong belief that undergraduate students should be offered hands-on opportunities from the very beginning of their studies until their graduation. We describe our projects, lectures and seminars that we provide undergraduate students to gain hands-on experience inside signal processing along the time line of the curriculum.

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Authors:
Tim Schäck, Michael Muma, Abdelhak M. Zoubir
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12 April 2018 - 11:52am
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[1] Tim Schäck, Michael Muma, Abdelhak M. Zoubir, "Hands-on in Signal Processing Education at Technische Universität Darmstadt", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2417. Accessed: May. 25, 2019.
@article{2417-18,
url = {http://sigport.org/2417},
author = {Tim Schäck; Michael Muma; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {Hands-on in Signal Processing Education at Technische Universität Darmstadt},
year = {2018} }
TY - EJOUR
T1 - Hands-on in Signal Processing Education at Technische Universität Darmstadt
AU - Tim Schäck; Michael Muma; Abdelhak M. Zoubir
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2417
ER -
Tim Schäck, Michael Muma, Abdelhak M. Zoubir. (2018). Hands-on in Signal Processing Education at Technische Universität Darmstadt. IEEE SigPort. http://sigport.org/2417
Tim Schäck, Michael Muma, Abdelhak M. Zoubir, 2018. Hands-on in Signal Processing Education at Technische Universität Darmstadt. Available at: http://sigport.org/2417.
Tim Schäck, Michael Muma, Abdelhak M. Zoubir. (2018). "Hands-on in Signal Processing Education at Technische Universität Darmstadt." Web.
1. Tim Schäck, Michael Muma, Abdelhak M. Zoubir. Hands-on in Signal Processing Education at Technische Universität Darmstadt [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2417

AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES


Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thus, both modalities are necessary for diagnosis. Since both techniques expose the patient to radiation, it would be desirable to get the same information about metabolic activity contained in the PET image from a CT scan only.

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Authors:
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang
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12 April 2018 - 11:42am
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[1] Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang, "AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2414. Accessed: May. 25, 2019.
@article{2414-18,
url = {http://sigport.org/2414},
author = {Annika Liebgott; Sergios Gatidis; Florian Liebgott; Konstantin Nikolaou; Bin Yang },
publisher = {IEEE SigPort},
title = {AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES},
year = {2018} }
TY - EJOUR
T1 - AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES
AU - Annika Liebgott; Sergios Gatidis; Florian Liebgott; Konstantin Nikolaou; Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2414
ER -
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. (2018). AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES. IEEE SigPort. http://sigport.org/2414
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang, 2018. AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES. Available at: http://sigport.org/2414.
Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. (2018). "AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES." Web.
1. Annika Liebgott, Sergios Gatidis, Florian Liebgott, Konstantin Nikolaou, Bin Yang. AUTOMATED DETECTION OF HIGH FDG UPTAKE REGIONS IN CT IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2414

ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION


Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end text-dependent speaker recognition system. We explore different topologies and their variants of the attention layer, and compare different pooling methods on the attention weights.

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Authors:
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan
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12 April 2018 - 11:42am
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[1] F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan, "ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2413. Accessed: May. 25, 2019.
@article{2413-18,
url = {http://sigport.org/2413},
author = {F A Rezaur Rahman Chowdhury; Quan Wang; Ignacio Lopez Moreno; Li Wan },
publisher = {IEEE SigPort},
title = {ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION},
year = {2018} }
TY - EJOUR
T1 - ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION
AU - F A Rezaur Rahman Chowdhury; Quan Wang; Ignacio Lopez Moreno; Li Wan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2413
ER -
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. (2018). ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/2413
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan, 2018. ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION. Available at: http://sigport.org/2413.
F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. (2018). "ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION." Web.
1. F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan. ATTENTION-BASED MODELS FOR TEXT-DEPENDENT SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2413

Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space


In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space.

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Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori
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12 April 2018 - 11:50am
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Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space

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[1] Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2412. Accessed: May. 25, 2019.
@article{2412-18,
url = {http://sigport.org/2412},
author = {Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori },
publisher = {IEEE SigPort},
title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
year = {2018} }
TY - EJOUR
T1 - Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
AU - Steven Van Vaerenbergh; Ignacio Santamaría; Víctor Elvira; Matteo Salvatori
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2412
ER -
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. IEEE SigPort. http://sigport.org/2412
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori, 2018. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space. Available at: http://sigport.org/2412.
Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. (2018). "Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space." Web.
1. Steven Van Vaerenbergh, Ignacio Santamaría, Víctor Elvira, Matteo Salvatori. Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2412

Sparse overcomplete denoising: aggregation versus global optimization

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Authors:
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg
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12 April 2018 - 11:41am
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[1] Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg, "Sparse overcomplete denoising: aggregation versus global optimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2411. Accessed: May. 25, 2019.
@article{2411-18,
url = {http://sigport.org/2411},
author = {Diego Carrera; Giacomo Boracchi; Alessandro Foi; Brendt Wohlberg },
publisher = {IEEE SigPort},
title = {Sparse overcomplete denoising: aggregation versus global optimization},
year = {2018} }
TY - EJOUR
T1 - Sparse overcomplete denoising: aggregation versus global optimization
AU - Diego Carrera; Giacomo Boracchi; Alessandro Foi; Brendt Wohlberg
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2411
ER -
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. (2018). Sparse overcomplete denoising: aggregation versus global optimization. IEEE SigPort. http://sigport.org/2411
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg, 2018. Sparse overcomplete denoising: aggregation versus global optimization. Available at: http://sigport.org/2411.
Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. (2018). "Sparse overcomplete denoising: aggregation versus global optimization." Web.
1. Diego Carrera, Giacomo Boracchi, Alessandro Foi, Brendt Wohlberg. Sparse overcomplete denoising: aggregation versus global optimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2411

Improving the Capacity of Very Deep Networks with Maxout Units


Deep neural networks inherently have large representational power for approximating complex target functions. However,

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Authors:
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten
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12 April 2018 - 11:43am
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[1] Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten, "Improving the Capacity of Very Deep Networks with Maxout Units", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2410. Accessed: May. 25, 2019.
@article{2410-18,
url = {http://sigport.org/2410},
author = {Oyebade Oyedotun; Abd El Rahman Shabayek; Djamila Aouada; Björn Ottersten },
publisher = {IEEE SigPort},
title = {Improving the Capacity of Very Deep Networks with Maxout Units},
year = {2018} }
TY - EJOUR
T1 - Improving the Capacity of Very Deep Networks with Maxout Units
AU - Oyebade Oyedotun; Abd El Rahman Shabayek; Djamila Aouada; Björn Ottersten
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2410
ER -
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. (2018). Improving the Capacity of Very Deep Networks with Maxout Units. IEEE SigPort. http://sigport.org/2410
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten, 2018. Improving the Capacity of Very Deep Networks with Maxout Units. Available at: http://sigport.org/2410.
Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. (2018). "Improving the Capacity of Very Deep Networks with Maxout Units." Web.
1. Oyebade Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Björn Ottersten. Improving the Capacity of Very Deep Networks with Maxout Units [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2410

Hard Shadows Removal Using An Approximate Illumination Invariant


Hard shadows detection and removal from foreground masks is a challenging step in change detection. This paper gives a simple and effective method to address hard shadows. There are inside portion and boundary portion in hard shadows. Pixel-wise neighborhood ratio is calculated to remove the most of inside shadow points. For the boundaries of shadow regions, we take advantage of color constancy to eliminate the edges of hard shadows and obtain relative accurate objects contours. Then, morphology processing is explored to enhance the integrity of objects.

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Authors:
Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao
Submitted On:
20 April 2018 - 1:49am
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[1] Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao, "Hard Shadows Removal Using An Approximate Illumination Invariant ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2409. Accessed: May. 25, 2019.
@article{2409-18,
url = {http://sigport.org/2409},
author = {Bingshu Wang; C.L. Philip Chen; Yuyuan Li; Yong Zhao },
publisher = {IEEE SigPort},
title = {Hard Shadows Removal Using An Approximate Illumination Invariant },
year = {2018} }
TY - EJOUR
T1 - Hard Shadows Removal Using An Approximate Illumination Invariant
AU - Bingshu Wang; C.L. Philip Chen; Yuyuan Li; Yong Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2409
ER -
Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao. (2018). Hard Shadows Removal Using An Approximate Illumination Invariant . IEEE SigPort. http://sigport.org/2409
Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao, 2018. Hard Shadows Removal Using An Approximate Illumination Invariant . Available at: http://sigport.org/2409.
Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao. (2018). "Hard Shadows Removal Using An Approximate Illumination Invariant ." Web.
1. Bingshu Wang, C.L. Philip Chen, Yuyuan Li, Yong Zhao. Hard Shadows Removal Using An Approximate Illumination Invariant [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2409

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