A Method to Improve Interest Point Detection and its GPU Implementation

Prabakar Karuppannan Gunashekhar
The Department of Electrical and Computer Engineering, Louisiana State University
Louisiana State University, 2012

   title={A Method To Improve Interest Point Detection And Its Gpu Implementation},

   author={Gunashekhar, P.K.},



Download Download (PDF)   View View   Source Source   



Interest point detection is an important low-level image processing technique with a wide range of applications. The point detectors have to be robust under affine, scale and photometric changes. There are many scale and affine invariant point detectors but they are not robust to high illumination changes. Many affine invariant interest point detectors and region descriptors, work on the points detected using scale invariant operators. Since the performance of those detectors depends on the performance of the scale invariant detectors, it is important that the scale invariant initial stage detectors should have good robustness. It is therefore important to design a detector that is very robust to illumination because illumination changes are the most common. In this research the illumination problem has been taken as the main focus and have developed a scale invariant detector that has good robustness to illumination changes. In the paper [6] it has been proved that by using contrast stretching technique the performance of the Harris operator improved considerably for illumination variations. In this research the same contrast stretching function has been incorporated into two different scale invariant operators to make them illumination invariant. The performances of the algorithms are compared with the Harris-Laplace and Hessian-Laplace algorithms [15].
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1662 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

337 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: