9337

Fast Implementation of Scale Invariant Feature Transform Based on CUDA

Meng Lu
College of Information Science and Engineering, Northeastern University, China
Applied Mathematics & Information Sciences, Vol. 7, No. 2L, p.717-722, 2013
@article{lu2013fast,

   title={Fast Implementation of Scale Invariant Feature Transform Based on CUDA},

   author={Lu, Meng},

   journal={Appl. Math},

   volume={7},

   number={2L},

   pages={717–722},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

1352

views

Scale-invariant feature transform (SIFT) was an algorithm in computer vision to detect and describe local features in images. Due to its excellent performance, SIFT was widely used in many applications, but the implementation of SIFT was complicated and time-consuming. To solve this problem, this paper presented a novel acceleration algorithm for SIFT implementation based on Compute Unified Device Architecture (CUDA). In the algorithm, all the steps of SIFT were specifically distributed and implemented by CPU or GPU, accroding to the step’s characteristics or demandings, to make full use of computational resources. Experiments showed that compared with the traditional implementation of SIFT, this paper’s acceleration algorithm can greatly increase computation speed and save implementation time. Furthermore, the acceleration ratio had linear relation with the number of SIFT keypoints.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

TwitterAPIExchange Object
(
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
        (
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1475361990
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1475361990
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => WZh+pXruBYNlNH4FBctkW5oBYbY=
        )

    [url] => https://api.twitter.com/1.1/users/show.json
)
Follow us on Facebook
Follow us on Twitter

HGPU group

2006 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: