Efficient Object Detection Based on Local Invariant Features

Authors

  • Anand Rathod Department of Computer, SCOE, Pune, India
  • Manoj Bagale Department of Computer, SCOE, Pune, India
  • Dinesh Taral Department of Computer, SCOE, Pune, India
  • D. R. Pawar Department of Computer, SCOE, Pune, India

DOI:

https://doi.org/10.37591/joipprp.v2i2.382

Keywords:

Object detection, image scale, translation, rotation, illumination

Abstract

Object detection is an important task in image processing and computer vision. Binary Robust Invariant Scalable Keypoint (BRISK) local features are extracted from the object. These features are invariant to image scale, translation, rotation, illumination and partial occlusion. Object detection process begins by matching individual features of the user queried object to a database of features with different objects which are saved in advance.

 

Cite this Article:
Anand Rathod, Manoj Bagale, Dinesh Taral, D.R. Pawar. Efficient Object Detection Based on Local Invariant Features. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(2): 1–3p.

References

Matusiak K, Skulimowskiand P, Strumillo P. Object recognition in a mobile phone application for visually impaired users. Human System Interaction (HSI), 2013 The 6th International Conference on IEEE. 2013; 479–484p.

Kanghun Jeong, Hyeonjoon Moon. Object detection using FAST corner detector based on smartphone platforms. 2011 First ACIS/JNU International Conference on. IEEE; 111–115p.

Stefan Leutenegger, Margarita Chli, Roland Y Siegwart. Binary robust invarient scalable keypoints. IEEE. 2011; 2548 – 2555p.

Reza Oji. An automatic algorithm for object recognition and detection based on ASIFT key points. An International Journal (SIPIJ). 2012; 3(5): 29p.

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Published

2015-05-14

Issue

Vol. 2 No. 2 (2015)

Section

Research Articles

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