چکیده (انگلیسی):

Moment invariants have been widely introduced in recognizing planar objects for a few decades. This
is due the robustness of moment function in distinguishing the original identity of object under various
two Dimensional (2D) transformations. A set of moments computed from a planar images, represents
the global description of an object’s shape and geometrical features of an image. Since global
descriptor utilizes the information of a whole object or shape to describe the features of an object, it
does not tolerate occlusion. If there is a mixture of regions that do not belong to the object of the
interest, an additional task of segmentation is required to isolate the object for recognition. Hence,
moment invariants are proposed to be employed as local descriptors for object recognition since local
descriptors do not suffer from the drawbacks caused by image clutter and occlusion. A new approach
of local feature descriptors using moment invariants is presented. The preliminary framework is
divided into three different stages. Interest points are firstly detected in the entire image. The local
descriptors are then produced by applying moment invariants on the region around the interest points.
Cross-correlation is finally carried out for feature matching.