Moment invariants under similarity transformation

Diao, Luhong; Peng, Juan; Kong, Fanyu

doi:10.1016/j.patcog.2015.05.003

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…For object classification, moment invariants (INVs) are often used as feature vector sets [9] for designing and applying machine learning technique. Diao et al [10] presented a method of deriving INVs under similarity transformation. They tested the derived INVs for bird, face, school desk, and bush models.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Lin

2016

Mathematical Problems in Engineering

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Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

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Section: Related Workmentioning

confidence: 99%

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Lin

2016

Mathematical Problems in Engineering

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Necessary Condition of Affine Moment Invariants

et al. 2018

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2D Geometric Moment Invariants from the Point of View of the Classical Invariant Theory

Bedratyuk

2020

J Math Imaging Vis

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Invariants allow to classify images up to the action of a group of transformations. In this paper we introduce notions of the algebras of simultaneous polynomial and rational 2D moment invariants and prove that they are isomorphic to the algebras of joint polynomial and rational SO(2)-invariants of binary forms. Also, to simplify the calculating of invariants we pass from an action of Lie group SO(2) to an action of its Lie algebra so 2 . This allow us to reduce the problem to standard problems of the classical invariant theory.

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Moment invariants under similarity transformation

Cited by 7 publications

References 25 publications

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Necessary Condition of Affine Moment Invariants

2D Geometric Moment Invariants from the Point of View of the Classical Invariant Theory

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