Author Description

Humans make object recognition look trivial. We can easily identify objects in our surroundings, regardless of their circumstances, whether they are upside down, different of colour or texture, partly occluded, etc. Even objects that appear in many different forms, like vases, or objects that are subject to considerable shape deviations, such as trees, can easily be generalized by our brain to one kind of object. Objet identification is done by integrating scale invariant feature extraction (SIFT) and shape index representation of range images allows matching of surface with different scales and orientations. Shape index is obtained and which is used as a local descriptor or key-point descriptor. Key-point descriptors are identified where shape index values are extremum. So, proposed project is for object identification uses 2 different properties like 3D surface properties for shape index identification and 2D scale invariant feature transform for key-point detection and feature extraction. This proposed method may be applicable for scaled, rotated and occluded range images.