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38 results on '"Civicioglu, Pinar"'

11. Contrast stretching based pansharpening by using weighted differential evolution algorithm

Author

BEŞDOK, ERKAN and Civicioglu, Pinar

Subjects
Artificial Intelligence, General Engineering, Computer Science Applications
Abstract

© 2022 Elsevier LtdPansharpening techniques were developed to generate a super-resolution multispectral pansharpened image, PI, with the combination of a multispectral image carrying high-resolution spectral information with a panchromatic image carrying high resolution spatial information. For using energy resources and communication bandwidth efficiently, Earth Observation Satellites acquire multispectral images with lower spatial resolution when compared to panchromatic images. Contrast Stretching alters the range and statistical distribution of pixel values of an image to facilitate perception of image features and can be used to match histograms of distinct images. The Contrast Stretching Based Pansharpening method, CSP, has been introduced in this paper. CSP considers the pansharpening as a rescaling-based pixel-level image fusion problem in spatial domain. CSP uses the contrast stretching to generate two modified-multispectral images and one modified-panchromatic image, which are used to compute pansharpened image. The Weighted Differential Evolution Algorithm has been used to optimize the numerical values of internal parameters of CSP. The successes of CSP and 17 different pansharpening methods have been statistically compared by using three Test Image sets with different characteristics. Ten different image quality measures have been used in accuracy assessment analysis of the PIs generated by the related methods used in the Experiments. Statistical analysis exposed that CSP generates more pleasing PIs both quantitatively and qualitatively compared to the comparison methods employed in this paper.

Published
2022

13. Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods.

Author

Besdok, Erkan and Civicioglu, Pinar

Subjects
COMPUTER vision, LAPLACIAN operator, CAMERA speeds, SMOOTHNESS of functions, GEOMETRY, GEOMETRIC vertices
Abstract

M esh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. While the Bilateral filter excels in preserving sharp features due to its integration of geometric proximity with intensity similarity, the Laplacian filter prioritizes smoothness by averaging neighboring vertex positions, and the Taubin filter offers a balanced approach by merging attributes of both Laplacian and high-pass filters. The Bilateral filter's primary strength lies in its ability to maintain sharp features on a mesh, ensuring that intricate details are preserved by considering both the spatial closeness and intensity similarity of vertices. The Laplacian filter, although effective in achieving mesh smoothness, has the propensity to excessively smooth out sharp and defining features, potentially causing a loss of critical details in the processed mesh. The Taubin filter integrates the best of both worlds, ensuring smoothness without excessive mesh shrinkage; however, it might not excel in feature preservation as effectively as the Bilateral filter or smooth as uniformly as the Laplacian filter, making it a middle-ground option for certain applications. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Adaptive Implicit-Camera Calibration in Photogrammetry Using Anfis

Author

Beṣdok, Erkan, Çivicioğlu, Pınar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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15. Implicit Camera Calibration by Using Resilient Neural Networks

Author

Çivicioğlu, Pınar, Beşdok, Erkan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, King, Irwin, editor, Wang, Jun, editor, Chan, Lai-Wan, editor, and Wang, DeLiang, editor

Published
2006
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16. Tracking Aircrafts by Using Impulse Exclusive Filter with RBF Neural Networks

Author

Çivicioğlu, Pınar, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Savacı, F. Acar, editor

Published
2006
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17. Using LM Artificial Neural Networks and η-Closest-Pixels for Impulsive Noise Suppression from Highly Corrupted Images

Author

Çivicioğlu, Pınar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Liao, Xiao-Feng, editor, and Yi, Zhang, editor

Published
2005
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18. Using a Neuro-Fuzzy Network for Impulsive Noise Suppression from Highly Distorted Images of WEB-TVs

Author

Çivicioğlu, Pınar, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Szczepaniak, Piotr S., editor, Kacprzyk, Janusz, editor, and Niewiadomski, Adam, editor

Published
2005
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19. Using an Exact Radial Basis Function Artificial Neural Network for Impulsive Noise Suppression from Highly Distorted Image Databases

Author

Çivicioğlu, Pınar, Alçı, Mustafa, Beṣdok, Erkan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Yakhno, Tatyana, editor

Published
2005
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20. Impulsive Noise Suppression from Highly Corrupted Images by Using Resilient Neural Networks

Author

Beşdok, Erkan, Çivicioğlu, Pınar, Alçı, Mustafa, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Rutkowski, Leszek, editor, Siekmann, Jörg H., editor, Tadeusiewicz, Ryszard, editor, and Zadeh, Lotfi A., editor

Published
2004
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24. Connected pixels-based image smoothing filter.

Author

Besdok, Erkan and Civicioglu, Pinar

Subjects
PIXELS, DIGITAL image processing, COMPUTER algorithms, DEEP learning, VIDEO surveillance
Abstract

Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such as colour and intensity, allows for the formation of meaningful patterns or objects, which is essential for object recognition and segmentation. This approach is particularly valuable in photogrammetric imaging, video surveillance, deep learning as it facilitates the isolation of regions of interest and object tracking. Image smoothing is also a crucial aspect in enhancing visual quality by reducing noise and enhancing details, especially in applications such as aerial mapping, medical imaging, video compression, image resizing and computer vision. The absence of connected pixels and image smoothing would make image processing tasks more challenging and less reliable, making them fundamental to digital image processing and critical to various applications in diverse fields. This paper introduces a novel image smoothing filter called Connected Pixels Based Image Smoothing Filter (CPF), which is based on gray connected pixels. The success of the CPF was compared to that of the Non-Local Means Filter (NLMF) in terms of Structural Similarity Index (SSIM) for the same Mean Squared Error (MSE). The experimental results showed that CPF has a better ability to preserve image details compared to NLMF. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS

Author

Civicioglu, Pinar

Subjects
Image processing -- Methods, Fuzzy algorithms -- Analysis, Fuzzy logic -- Analysis, Fuzzy systems -- Analysis, Extreme value theory -- Analysis, Electromagnetic noise -- Analysis, Fuzzy logic, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A novel adaptive network-based fuzzy inference system (ANFIS)-based filter (ABF) is described for the restoration of images corrupted by impulsive noise (IN). The results have indicated that the proposed filter achieves a better performance than the comparison filters in terms of noise suppression and detail preservation, even when the images are highly corrupted by IN.

Published
2007

31. Adaptive implicit-Camera Calibration in photogrammetry using Anfis

Author

Civicioglu, Pinar and Besdok, Erkan

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Camera Calibration (CC) is required in many Photogrammetry and Computer-Vision applications, where 3D information is extracted from images and CC is also employed for pose determination of imaging sensors. In this paper, a novel implicit-CC model (ICC) based on Adaptive Neuro Fuzzy Inference System has been introduced. The ICC is particularly useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The ICC supports multi-view back-projection in intelligent-photogrammetry. In this paper, the back-projection performance of the ICC has been compared with the Modified Direct-Linear-Transformation (MDLT) on real-images in order to evaluate the success of the proposed ICC. Extensive simulation results show that the ICC achieves a better performance than the MDLT in the 3D reconstruction of scene.

Published
2005

32. Implicit camera calibration by using resilient neural networks

Author

Besdok, Erkan and Civicioglu, Pinar

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

The accuracy of 3D measurements of objects is highly affected by the errors originated from camera calibration. Therefore, camera calibration has been one of the most challenging research fields in the computer vision and photogrammetry recently. In this paper, an Artificial Neural Network Based Camera Calibration Method, NBM, is proposed. The NBM is especially useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The NBM offers solutions to various camera calibration problems such as calibrating cameras with automated active lenses that are often encountered in computer vision applications. The difference of the NBM from the other artificial neural network based back-projection algorithms used in intelligent photogrammetry (photogrammetron) is its ability to support the multiple view geometry. In this paper, a comparison of the proposed method has been made with the Bundle Block Adjustment based back-projection algorithm, BBA. The performance of accuracy and validity of the NBM have been tested and verified over real images by extensive simulations.

Published
2005

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