Algorithms for Rectification of Geometric Distortions

2014 ◽  
pp. 87-105
Keyword(s):  
Geometric Distortions

Real-Time Compressed- Domain Video Watermarking Resistance to Geometric Distortions

2012 ◽  
Vol 19 (1) ◽  
pp. 70-79 ◽  
Author(s):  
Liyun Wang ◽  
Hefei Ling ◽  
Fuhao zou ◽  
Zhengding Lu
Keyword(s):  
Real Time ◽  
Video Watermarking ◽  
Compressed Domain ◽  
Geometric Distortions

A Tris(trifluoromethyl)antimony Adduct of a Nucleophilic Carbene: Geometric Distortions in Carbene Adducts

1999 ◽  
Vol 625 (11) ◽  
pp. 1813-1817 ◽  
Author(s):  
Anthony J. Arduengo, III ◽  
Roland Krafczyk ◽  
Reinhard Schmutzler ◽  
Walter Mahler ◽  
William J. Marshall
Keyword(s):  
Geometric Distortions

Colourlab Image Database: Geometric Distortions

2021 ◽  
Vol 2021 (29) ◽  
pp. 258-263
Author(s):  
Marius Pedersen ◽  
Seyed Ali Amirshahi
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Image Database ◽  
Quality Metrics ◽  
Current Image ◽  
Geometric Distortions ◽  
Image Quality Metrics ◽  
Objective Metrics ◽  
Reference Images

Over the years, a high number of different objective image quality metrics have been proposed. While some image quality metrics show a high correlation with subjective scores provided in different datasets, there still exists room for improvement. Different studies have pointed to evaluating the quality of images affected by geometrical distortions as a challenge for current image quality metrics. In this work, we introduce the Colourlab Image Database: Geometric Distortions (CID:GD) with 49 different reference images made specifically to evaluate image quality metrics. CID:GD is one of the first datasets which include three different types of geometrical distortions; seam carving, lens distortion, and image rotation. 35 state-ofthe-art image quality metrics are tested on this dataset, showing that apart from a handful of these objective metrics, most are not able to show a high performance. The dataset is available at <ext-link ext-link-type="url" xlink:href="http://www.colourlab.no/cid">www.colourlab.no/cid</ext-link>.

Edge Detection by Maximum Entropy

2013 ◽  
pp. 146-159
Author(s):  
Ibrahim Guelzim ◽  
Ahmed Hammouch ◽  
El Mustapha Mouaddib ◽  
Driss Aboutajdine
Keyword(s):  
Edge Detection ◽  
Statistical Method ◽  
Maximum Entropy ◽  
Image Resolution ◽  
Entropy Measure ◽  
Geometric Distortions ◽  
Omnidirectional Images ◽  
Grayscale Images

In the edge detection, the classical operators based on the derivation are sensitive to noise which causes detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure. The authors compared its behavior with existing methods on omnidirectional images and perspectives images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images, the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors compared the results of detection visually. The tests are performed on grayscale images.

Edge Detection by Maximum Entropy

2011 ◽  
Vol 1 (3) ◽  
pp. 1-15
Author(s):  
Ibrahim Guelzim ◽  
Ahmed Hammouch ◽  
El Mustapha Mouaddib ◽  
Driss Aboutajdine
Keyword(s):  
Edge Detection ◽  
Statistical Method ◽  
Maximum Entropy ◽  
Image Resolution ◽  
Entropy Measure ◽  
Geometric Distortions ◽  
Omnidirectional Images ◽  
Grayscale Images

In the edge detection, the classical operators based on the derivation are sensitive to noise which causes detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure. The authors compared its behavior with existing methods on omnidirectional images and perspectives images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images, the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors compared the results of detection visually. The tests are performed on grayscale images.

Response to Geometric Distortions from Atom-Based Relativistic Projection on Electronic States

2007 ◽  
Author(s):  
Alexei V. Matveev ◽  
Notker Rösch ◽  
Theodore E. Simos ◽  
George Maroulis
Keyword(s):  
Electronic States ◽  
Geometric Distortions
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