Machine learning technique for object detection based on SURF feature Online publication date: Sun, 01-Jan-2017
by Amin Mohamed Ahsan; Dzulkifli Bin Mohamad
International Journal of Computational Vision and Robotics (IJCVR), Vol. 7, No. 1/2, 2017
Abstract: Local features that based on interest points have received a great interest in computer vision field and they play an important role in many applications, such as object recognition, tracking, and image retrieval. These features have proven to be invariant against the geometric and photometric transformations and proven to be robust under different types of image disturbances. Matching technique is usually employed in this field to recognise the object. Yet, it is not suitable for some applications such as searching for an isolated object, part-based object recognition, and object categorisation. A model for object detection with an artificial neural network (ANN) to overcome such shortages is proposed. Two datasets are prepared to be used for learning; one for human faces and the other for the cars. Features are extracted using speeded-up robust feature (SURF). The proposed model is evaluated using two benchmark datasets, Caltech101 and VOC2009. The obtained results are encouraging.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook