Authors:
Alina Trifan
and
António J. R. Neves
Affiliation:
Universidade de Aveiro, Portugal
Keyword(s):
Bayer Pattern, Raw Data, Feature Detectors and Descriptors, Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Image Understanding
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
Local feature descriptors and detectors have been widely used in computer vision in the last years for solving object detection and recognition tasks. Research efforts have been focused on reducing the complexity of these descriptors and improving their accuracy. However, these descriptors have not been tested until now on raw image data. This paper presents a study on the use of two of the most known and used feature descriptors, SURF and SIFT, directly on raw CFA images acquired by a digital camera. We are interested in understanding if the number and quality of the keypoints obtained from a raw image are comparable to the ones obtained in the grayscale images, which are normally used by these transforms. The results that we present show that the number and positions of the keypoints obtained from grayscale images are similar to the ones obtained from CFA images and furthermore to the ones obtained from grayscale images that resulted directly from the interpolation of a CFA image.