<p>In robot navigation, and image content searches reliable salient features are of pivotal importance. Also in biometric human recognition, salient features are increasingly used. </p><p>Regardless the application, image matching is one of the many problems in computer </p><p>vision, including object recognition. </p><p> </p><p>This report investigates some salient features to match sub-images of different images. </p><p>An underlying assumption is that sub-images, also called image objects, or objects, are </p><p>possible to recognize by the salient features that can be recognized independently. </p><p> </p><p>Since image objects are images of 3D objects, the salient features in 2D images must be </p><p>invariant to reasonably large viewing direction and distance (scale) changes. These </p><p>changes are typically due to 3D rotations and translations of the 3D object with respect to </p><p>the camera. Other changes that influence the matching of two 2D image objects is </p><p>illumination changes, and image acquisition noise. </p><p> </p><p>This thesis will discuss how to find the salient features and will compare them with </p><p>respect to their matching performance. Also it will explore how these features are </p><p>invariant to rotation and scaling.</p>

In robot navigation, and image content searches reliable salient features are of pivotal importance. Also in biometric human recognition, salient features are increasingly used. Regardless the application, image matching is one of the many problems in computer vision, including object recognition. This report investigates some salient features to match sub-images of different images. An underlying assumption is that sub-images, also called image objects, or objects, are possible to recognize by the salient features that can be recognized independently. Since image objects are images of 3D objects, the salient features in 2D images must be invariant to reasonably large viewing direction and distance (scale) changes. These changes are typically due to 3D rotations and translations of the 3D object with respect to the camera. Other changes that influence the matching of two 2D image objects is illumination changes, and image acquisition noise. This thesis will discuss how to find the salient features and will compare them with respect to their matching performance. Also it will explore how these features are invariant to rotation and scaling.