Content Based Image Hashing Robust to Geometric Transformations

2009 ◽  
Author(s):  
Han-ling Zhang ◽  
Cai-qiong Xiong ◽  
Guang-zhi Geng
Keyword(s):  
Image Hashing ◽  
Geometric Transformations

A New Robust Reference Image Hashing System

2021 ◽  
pp. 1-1
Author(s):  
Satendra Pal Singh ◽  
Gaurav Bhatnagar ◽  
Amit Kumar Singh
Keyword(s):  
Reference Image ◽  
Image Hashing

scholarly journals Robust image hashing with compressed sensing and ordinal measures

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Zhenjun Tang ◽  
Hanyun Zhang ◽  
Shenglian Lu ◽  
Heng Yao ◽  
Xianquan Zhang
Keyword(s):  
Compressed Sensing ◽  
Image Hashing ◽  
Robust Image ◽  
Robust Image Hashing

Geometric Transformations of Pictured Space

Perception ◽  
1978 ◽  
Vol 7 (3) ◽  
pp. 269-282 ◽  
Author(s):  
James Farber ◽  
Richard R Rosinski
Keyword(s):  
Geometric Analysis ◽  
Space Perception ◽  
Unique Point ◽  
Geometric Transformations ◽  
Pictorial Space ◽  
Pictorial Perception ◽  
Correct Point

In general, a picture can represent a specific environment or scene only when the picture is seen from a unique viewing point. The determination of this unique point and of the distortions that occur when the picture is viewed from other points is crucial to all aspects of pictorial perception. To clarify the effects of the point of observation on pictorial space, the present paper discusses how the correct point may be calculated, provides a geometric analysis of the effects of altering the viewing point, and briefly reviews the effects of such alterations on space perception.

scholarly journals CGCI-SIFT: A More Efficient and Compact Representation of Local Descriptor

2013 ◽  
Vol 13 (3) ◽  
pp. 132-141 ◽  
Author(s):  
Dongliang Su ◽  
Jian Wu ◽  
Zhiming Cui ◽  
Victor S. Sheng ◽  
Shengrong Gong
Keyword(s):  
Peripheral Region ◽  
Intensity Difference ◽  
Compact Representation ◽  
Interest Point ◽  
Local Descriptor ◽  
Geometric Transformations ◽  
Intensity Information ◽  
Matching Efficiency ◽  
Inner Region ◽  
Better Than

This paper proposes a novel invariant local descriptor, a combination of gradient histograms with contrast intensity (CGCI), for image matching and object recognition. Considering the different contributions of sub-regions inside a local interest region to an interest point, we divide the local interest region around the interest point into two main sub-regions: an inner region and a peripheral region. Then we describe the divided regions with gradient histogram information for the inner region and contrast intensity information for the peripheral region respectively. The contrast intensity information is defined as intensity difference between an interest point and other pixels in the local region. Our experimental results demonstrate that the proposed descriptor performs better than SIFT and its variants PCA-SIFT and SURF with various optical and geometric transformations. It also has better matching efficiency than SIFT and its variants PCA-SIFT and SURF, and has the potential to be used in a variety of realtime applications.

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