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ICIP 2018

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

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Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
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7 October 2018 - 2:43am
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Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3584. Accessed: Dec. 10, 2018.
@article{3584-18,
url = {http://sigport.org/3584},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3584
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3584
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3584.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3584

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

Paper Details

Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
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Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Towards_Camera_Identification_From_Cropped_Query_Images_ICIP.pdf

(43 downloads)

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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3583. Accessed: Dec. 10, 2018.
@article{3583-18,
url = {http://sigport.org/3583},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3583
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3583
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3583.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3583

Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance


Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data—each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data.

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Authors:
Honglei Zhang, Moncef Gabbouj
Submitted On:
7 October 2018 - 1:32am
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poster_ICIP_2018.pdf

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[1] Honglei Zhang, Moncef Gabbouj, "Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3582. Accessed: Dec. 10, 2018.
@article{3582-18,
url = {http://sigport.org/3582},
author = {Honglei Zhang; Moncef Gabbouj },
publisher = {IEEE SigPort},
title = {Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance},
year = {2018} }
TY - EJOUR
T1 - Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance
AU - Honglei Zhang; Moncef Gabbouj
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3582
ER -
Honglei Zhang, Moncef Gabbouj. (2018). Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance. IEEE SigPort. http://sigport.org/3582
Honglei Zhang, Moncef Gabbouj, 2018. Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance. Available at: http://sigport.org/3582.
Honglei Zhang, Moncef Gabbouj. (2018). "Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance." Web.
1. Honglei Zhang, Moncef Gabbouj. Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3582

DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT


Visual tracking frameworks employing Convolutional Neural Networks (CNNs) have shown state-of-the-art performance due to their hierarchical feature representation. While classification and update based deep neural net tracking have shown good performance in terms of accuracy, they have poor tracking speed. On the other hand, recent matching based techniques using CNNs show higher than real-time speed in tracking but this speed is achieved at a considerably lower accuracy.

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6 October 2018 - 9:51pm
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poster_ICIP2.pdf

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[1] , "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3581. Accessed: Dec. 10, 2018.
@article{3581-18,
url = {http://sigport.org/3581},
author = { },
publisher = {IEEE SigPort},
title = {DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT},
year = {2018} }
TY - EJOUR
T1 - DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3581
ER -
. (2018). DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. IEEE SigPort. http://sigport.org/3581
, 2018. DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. Available at: http://sigport.org/3581.
. (2018). "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT." Web.
1. . DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3581

SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK


We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches.

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Authors:
Michael Liebling
Submitted On:
6 October 2018 - 8:16pm
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AS_ICIP2018_Poster_horiz.pdf

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[1] Michael Liebling, "SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3580. Accessed: Dec. 10, 2018.
@article{3580-18,
url = {http://sigport.org/3580},
author = {Michael Liebling },
publisher = {IEEE SigPort},
title = {SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK
AU - Michael Liebling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3580
ER -
Michael Liebling. (2018). SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK. IEEE SigPort. http://sigport.org/3580
Michael Liebling, 2018. SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK. Available at: http://sigport.org/3580.
Michael Liebling. (2018). "SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK." Web.
1. Michael Liebling. SEMI-BLIND SPATIALLY-VARIANT DECONVOLUTION IN OPTICAL MICROSCOPY WITH LOCAL POINT SPREAD FUNCTION ESTIMATION BY USE OF CONVOLUTIONAL NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3580

ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES

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Authors:
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj
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6 October 2018 - 6:22pm
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ICIP2018_2065_slide_v2.pptx

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[1] Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj, "ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3579. Accessed: Dec. 10, 2018.
@article{3579-18,
url = {http://sigport.org/3579},
author = {Xiaoxi Pan; Mouloud Adel; Caroline Fossati; Thierry Gaidon; Eric Guedj },
publisher = {IEEE SigPort},
title = {ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES},
year = {2018} }
TY - EJOUR
T1 - ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES
AU - Xiaoxi Pan; Mouloud Adel; Caroline Fossati; Thierry Gaidon; Eric Guedj
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3579
ER -
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. (2018). ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES. IEEE SigPort. http://sigport.org/3579
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj, 2018. ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES. Available at: http://sigport.org/3579.
Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. (2018). "ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES." Web.
1. Xiaoxi Pan, Mouloud Adel, Caroline Fossati, Thierry Gaidon, Eric Guedj. ALZHEIMER’S DISEASE DIAGNOSIS WITH FDG-PET BRAIN IMAGES BY USING MULTI-LEVEL FEATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3579

A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY


Model based methods have gained popularity in the past few decades in reconstruction problems particularly when the measurement data is sparse. In model based inference, apart from a model for the measurements, there exists a model for the unknown signal to be reconstructed, called the prior model. Model based methods tend to do very well when the prior model is accurate and representative of real world behavior of the unknown signal.

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Authors:
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman
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6 October 2018 - 5:02pm
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ICIP_2018_Final_Poster_v2.pdf

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[1] Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman, "A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3578. Accessed: Dec. 10, 2018.
@article{3578-18,
url = {http://sigport.org/3578},
author = {Zeeshan Nadir; Kristin M. Rice; Michael S. Brown; Charles A. Bouman },
publisher = {IEEE SigPort},
title = {A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY},
year = {2018} }
TY - EJOUR
T1 - A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY
AU - Zeeshan Nadir; Kristin M. Rice; Michael S. Brown; Charles A. Bouman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3578
ER -
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. (2018). A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY. IEEE SigPort. http://sigport.org/3578
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman, 2018. A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY. Available at: http://sigport.org/3578.
Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. (2018). "A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY." Web.
1. Zeeshan Nadir, Kristin M. Rice, Michael S. Brown, Charles A. Bouman. A HYBRID PRIOR MODEL FOR TUNABLE DIODE LASER ABSORPTION TOMOGRAPHY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3578

IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING


Pixel-value-ordering (PVO) appears as an efficient technique for high-fidelity reversible data hiding. This paper proposes a reversible data hiding scheme based on the pairwise PVO framework with improved difference equations. Both the pixel pair selection and the embedding algorithms are also streamlined. The proposed scheme uses a block classification approach based on a local complexity metric. Uniform blocks are processed using the proposed improved pairwise PVO algorithm. Slightly noisy blocks are embedded using a classic PVO scheme and noisy blocks are kept unchanged.

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Authors:
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc
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6 October 2018 - 8:51am
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ICIP2018_pairwiseIPVO.pdf

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[1] Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc, "IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3576. Accessed: Dec. 10, 2018.
@article{3576-18,
url = {http://sigport.org/3576},
author = {Ioan-Catalin Dragoi; Ion Caciula; Dinu Coltuc },
publisher = {IEEE SigPort},
title = {IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING},
year = {2018} }
TY - EJOUR
T1 - IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING
AU - Ioan-Catalin Dragoi; Ion Caciula; Dinu Coltuc
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3576
ER -
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. (2018). IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING. IEEE SigPort. http://sigport.org/3576
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc, 2018. IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING. Available at: http://sigport.org/3576.
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. (2018). "IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING." Web.
1. Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3576

SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE


We address the problem of camera motion estimation from a single blurred image with the aid of deep convolutional neural networks.
Unlike learning-based prior works that estimate a space-invariant blur kernel, we solve for the global camera motion which in turn

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Authors:
Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram
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6 October 2018 - 8:47am
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SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE.pdf

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[1] Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram, "SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3575. Accessed: Dec. 10, 2018.
@article{3575-18,
url = {http://sigport.org/3575},
author = {Nimisha T M; Vijay Rengarajan; Rajagopalan Ambasamudram },
publisher = {IEEE SigPort},
title = {SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE},
year = {2018} }
TY - EJOUR
T1 - SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE
AU - Nimisha T M; Vijay Rengarajan; Rajagopalan Ambasamudram
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3575
ER -
Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram. (2018). SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE. IEEE SigPort. http://sigport.org/3575
Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram, 2018. SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE. Available at: http://sigport.org/3575.
Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram. (2018). "SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE." Web.
1. Nimisha T M, Vijay Rengarajan, Rajagopalan Ambasamudram. SEMI-SUPERVISED LEARNING OF CAMERA MOTION FROM A BLURRED IMAGE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3575

REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION


This paper proposes a new vacating room after encryption reversible data hiding scheme developed for color images. The proposed scheme uses standard exclusive-or encryption and inherits the main features of vacating room after encryption schemes, namely joint and separate methods for data embedding. The proposed scheme exploits both the correlation between neighboring pixels and the correlation between color channels by predicting the original pixel values on color channel differences.

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Authors:
Ioan-Catalin Dragoi, Dinu Coltuc
Submitted On:
6 October 2018 - 8:44am
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ICIP2018_CriptColor.pdf

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[1] Ioan-Catalin Dragoi, Dinu Coltuc, "REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3574. Accessed: Dec. 10, 2018.
@article{3574-18,
url = {http://sigport.org/3574},
author = {Ioan-Catalin Dragoi; Dinu Coltuc },
publisher = {IEEE SigPort},
title = {REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION},
year = {2018} }
TY - EJOUR
T1 - REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION
AU - Ioan-Catalin Dragoi; Dinu Coltuc
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3574
ER -
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION. IEEE SigPort. http://sigport.org/3574
Ioan-Catalin Dragoi, Dinu Coltuc, 2018. REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION. Available at: http://sigport.org/3574.
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). "REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION." Web.
1. Ioan-Catalin Dragoi, Dinu Coltuc. REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3574

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