scispace - formally typeset
Open Access

Object classification via geometrical, zernike and legendre moments

Reads0
Chats0
TLDR
Three kinds of moments: Geometrical, Zernike and Legendre Moments have been evaluated for classifying 3D object images using Nearest Neighbor classifier.
Abstract
In many applications, different kinds of moments have been utilized to classify images and object shapes. Moments are important features used in recognition of different types of images. In this paper, three kinds of moments: Geometrical, Zernike and Legendre Moments have been evaluated for classifying 3D object images using Nearest Neighbor classifier. Experiments are conducted using ETH-80 database, which contains 80 objects.

read more

Citations
More filters
Journal ArticleDOI

A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System

TL;DR: A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented and a general evaluation method for multi-class tracking systems is shown.
Journal ArticleDOI

Zernike moments and genetic algorithm: Tutorial and application

TL;DR: This work showed how to effectively apply ZM on RGB images and demonstrated the effectiveness of Zernike moment in image classification system with a neuro-genetic intelligent system built with PNN classifier.
Journal ArticleDOI

An efficient multiple classifier system for Arabic handwritten words recognition

TL;DR: An efficient multiple classifier system for Arabic handwritten words recognition by using Chebyshev moments enhanced with some Statistical and Contour-based Features for describing word images and combining several classifiers integrated at the decision level is proposed.
Journal ArticleDOI

Simple object recognition based on spatial relations and visual features represented using irregular pyramids

TL;DR: This work proposes a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships—which are obtained by means of combinatorial pyramids—in order to identify similar objects from a database.
Proceedings ArticleDOI

Application of computer vision and color image segmentation for yield prediction precision

TL;DR: A novel application of computer vision and color image segmentation for automating the precise yield prediction process of gerbera flower yield from the polyhouse images is described.
References
More filters
Journal ArticleDOI

Visual pattern recognition by moment invariants

TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Journal ArticleDOI

Pictorial Structures for Object Recognition

TL;DR: A computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager, that allows for qualitative descriptions of visual appearance and is suitable for generic recognition problems.
Journal ArticleDOI

Image analysis via the general theory of moments

TL;DR: Two-dimensional image moments with respect to Zernike polynomials are defined, and it is shown how to construct an arbitrarily large number of independent, algebraic combinations of zernike moments that are invariant to image translation, orientation, and size as discussed by the authors.
Journal ArticleDOI

Invariant image recognition by Zernike moments

TL;DR: A systematic reconstruction-based method for deciding the highest-order ZERNike moments required in a classification problem is developed and the superiority of Zernike moment features over regular moments and moment invariants was experimentally verified.
Proceedings ArticleDOI

Image Classification using Random Forests and Ferns

TL;DR: It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.
Related Papers (5)