Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Forensics Image Background Matching Using Scale Invariant Feature Transform and Speeded up Robust Features

Forensics Image Background Matching Using Scale Invariant Feature Transform and Speeded up Robust Features

Fogg, Paul N.

Paperback

General ComputersGeneral EducationGeneral Law

ISBN10: 1288368895
ISBN13: 9781288368891
Publisher: Biblioscholar
Published: Nov 29 2012
Pages: 62
Weight: 0.22
Height: 0.13 Width: 6.14 Depth: 9.21
Language: English

In criminal investigations, it is not uncommon for investigators to obtain a photograph or image that shows a crimebeing committed. Additionally, thousands of pictures may exist of a location, taken from the same or varying viewpoints.Some of these images may even include a criminal suspect or witness. One mechanism to identify criminals and witnessesis to group the images found on computers, cell phones, cameras, and other electronic devices into sets representing thesame location. One or more images in the group may then prove the suspect was at the crime scene before, during, and/or after a crime. This research extends three image feature generation techniques, the Scale Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), and the Shi-Tomasi algorithm, to group images based onlocation. The image matching identifies keypoints in images with changes in the contents, viewpoint, and individualspresent at each location. After calculating keypoints for each image, the algorithm stores the strongest features for eachimage are stored to minimize the space and matching requirements.

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