In this paper we focus on automatically learning object models in the framework of keypoint based object recognition. The proposed method uses a collection of views of the objects to build the model. For each object the collection is composed of N×M views obtained rotating the object around its vertical and horizontal axis. As keypoint based object recognition using a complete set of views is computationally expensive, we focused on the definition of a selection method that creates, for each object, a subset of the initial views that visually summarize the characteristics of the object and should be suited for recognition. We select the views by determining maxima and minima of a function, based on the number of SIFT descriptors able to evaluate views similarity and relevance. Experimental results for recognition on a publicly available dataset are reported.
|Titolo della pubblicazione ospite||2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)|
|Numero di pagine||5|
|Stato di pubblicazione||Published - 2016|
All Science Journal Classification (ASJC) codes
- Media Technology
- Signal Processing