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Enhancement of perceptual hashing algorithm applied on onsight application / Allyona Corona, and Ayra Denise Talamayan. 6

By: Allyona Corona, and Ayra Denise Talamayan. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 201746Edition: Description: 28 cm. 90 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: A perceptual hash function maps an image to produce a binary string of hash value based on the image's visual appearance. Such a function calculates and compares two hash values to determine whether photos are similar or not. This paper presents enhanced techniques that overcome certain limitations of perceptual image hashing. Cross correlation method was employed as a similarity measure for normalization while Euclidean distance was introduced to compare pictures by computing the distance of two different vectors from two different images. Experimental results showed that the proposed algorithm is robust against normal digital processing like cropping, distorting such as swirled and fisheyed effect, and flipping horizontally or vertically of an image. When compared with the current scheme, the suggested study yielded better identification performance, thus producing an algorithm for applications in image databases, indexing, watermaking, and authentication. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Archives	Filipiniana-Thesis	QA76.C67.2017 (Browse shelf)	Available		FT6056

Total holds: 0

Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2017. 56

ABSTRACT: A perceptual hash function maps an image to produce a binary string of hash value based on the image's visual appearance. Such a function calculates and compares two hash values to determine whether photos are similar or not. This paper presents enhanced techniques that overcome certain limitations of perceptual image hashing. Cross correlation method was employed as a similarity measure for normalization while Euclidean distance was introduced to compare pictures by computing the distance of two different vectors from two different images. Experimental results showed that the proposed algorithm is robust against normal digital processing like cropping, distorting such as swirled and fisheyed effect, and flipping horizontally or vertically of an image. When compared with the current scheme, the suggested study yielded better identification performance, thus producing an algorithm for applications in image databases, indexing, watermaking, and authentication.

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