ArtWork recognition in 360-degree image using 32-hedron based rectilinear projection and scale invariant feature transform

Jin, Xun; Kim, Jongweon

doi:10.1109/iceict.2016.7879716

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The Difference of Gaussiane (DoG) algorithm is used to find these feature points. Similar features can be found in spaces of different scales [18].…”

Section: A Sift Techniquesupporting

confidence: 54%

Intelligent Visual Similarity-Based Phishing Websites Detection

Chen

Huang

2020

Symmetry

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This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.

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“…The Difference of Gaussiane (DoG) algorithm is used to find these feature points. Similar features can be found in spaces of different scales [18].…”

Section: A Sift Techniquesupporting

confidence: 54%

Intelligent Visual Similarity-Based Phishing Websites Detection

Chen

Huang

2020

Symmetry

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“…Thus, the 360-degree image must be averagely partitioned. In a previous work [75], we used 32-hedron to partition the 360-degree image. However, in this work, to measure the effects of the number, size, and direction of the polygons for identification, we use three types of polyhedrons for rectilinear projection: 32-hedron, dodecahedron, and octahedron.…”

Section: Polyhedron-based Rectilinear Projectionmentioning

confidence: 99%

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Jin

Kim

2017

Applied Sciences

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Abstract:With the increased development of 360-degree production technologies, artwork has recently been photographed without authorization. To prevent this infringement, we propose an artwork identification methodology for 360-degree images. We transform the 360-degree image into a three-dimensional sphere and wrap it with a polyhedron. On the sphere, several points are located on the polyhedron to determine the width, height, and direction of the rectilinear projection. The 360-degree image is divided and transformed into several rectilinear projected images to reduce the adverse effects from the distorted panoramic image. We also propose a method for improving the identification precision of artwork located at a highly distorted position using the difference of keypoint shapes. After applying the proposed methods, identification precision is increased by 45% for artwork that is displayed on a 79-inch monitor in a seriously distorted position with features that were generated by scale-invariant feature transformations.

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