Using Moment Invariants for Classifying Shapes on Large_scale Maps

Fish Shape Recognition using Multiple Shape Descriptors

This paper studies recognition of fish shapes using both Region based and Contour based shape based descriptors[9]. Moment Invariants are chosen as the Region based descriptor and the Simple (geometric) shape descriptors (SSD) are used as Contour based shape descriptors. The shapes are varied through scaling and rotation. Manhattan Distance is used as the classifier. The study of the recognitio...

Improved moment invariants for shape discrimination

Moment invariants have been frequently used as features for shape recognition. They are computed based on the information provided by both the shape boundary and its interior region. Although several fast algorithms for computing traditional moment invariants have been proposed, none has ever shown the theoretical results of moment invariants computed based on the shape boundary only. This pape...

Shape Representation and Retrieval Using Radial Basis Probabilistic Neural Networks

This paper presents a 2D shape retrieval system using moment invariants based on a representation of image boundary of the shape objects. The original binary image is transformed into a matrix which consists of numbers that represents the distance to the shape contour. Chessboard distance transform is used for the representation of the shape contour. Moment invariants are computed for the origi...

Discriminative wavelet shape descriptors for recognition of 2-D patterns

In this paper, we present a set of wavelet moment invariants, together with a discriminative feature selection method, for the classification of seemingly similar objects with subtle differences. These invariant features are selected automatically based on the discrimination measures defined for the invariant features. Using a minimum-distance classifier, our wavelet moment invariants achieved ...

Using Image Moment Invariants to Distinguish Classes of Geographical Shapes