Image registration requires a step of detection and matching of primitives. This phase is important to obtain reliable registration. In this paper, we mainly focus on geometric registration methods which are based on the extraction and matching of distinctive feature points in images. Several methods such as SIFT, SURF, BRIEF, BRISK, ORB, FREAK and FRIF, are already proposed. In this paper, we present a comparative study of feature detector and descripts methods for registration which can be classified according to the type of descriptor and can be local classical or binary. We have presented, through this study, the difference between geometric methods of descriptor leveling as well as points of interest detector used and which have an influence on the resetting registration result. We can see that each method has weak points as well as strong points. The major difference is the level of invariance to the type of processing and the temporal complexity.
In this paper, an unsupervised registration approach based on possibility theory, called "Unsupervised Possibilistic registration", is proposed to encounter this problem. It consists on adding an unsupervised projection step that allows matching possibility maps, obtained from the two images instead of the grey-level images (knowing that the thematic classes and their number have no effect on the registration). The experiments and the comparative study using MRI images have shown promising results. It is shown that the proposed unsupervised registration approach overcomes major problems of existing methods and allows temporal complexity optimization.
The classification of remote-sensing images based on multiple information sources offers a consistent method for the
automatic cartography of forest stands. However, fusion models reveal problems of combinatorial explosion due to the
calculation of the assignment functions. This article proposes an information-fusion approach that responds to the need
for updating the forest inventory, based on belief theory. It illustrates a solution that overcomes the problem of
combinatorial explosion that arises with the evaluation of evidence-mass functions which are used as the frame of
discernment events. This solution is based on a refinement of the frame of discernment based on the determination of all
focal elements (singleton or composite hypothesis of non null masses). Thus, the combination of information source
masses would involve only the focal elements masses. In the approach proposed here, the notions of fuzzy logic and
possibility theory have been used for the calculation of masses and combinations between classes as an intermediary
phase in arriving at belief functions. The result of the application of our fusion approach revealed a significant
improvement in optimizing the calculation of mass evidence functions and thus achieving a satisfactory classification.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.