Projection Operators and Moment Invariants to Image Blurring

Flusser, Jan; Suk, Tomáš; Boldyš, Jiří; Zitová, Barbara

doi:10.1109/tpami.2014.2353644

Cited by 44 publications

(16 citation statements)

References 76 publications

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“…Instead of metrics, there are works which propose blur-invariant/robust representations. Among these are moment invariants described by Flusser et al [9] for symmetric blur kernels, which are further shown to be Point clouds Symmetric functions [20] complete. They also combine the proposed blur invariant with illumination and rotation invariance.…”

Section: Examples Of Factors Of Variation and Associated Invariantsmentioning

confidence: 99%

Invariant Methods in Computer Vision

2020

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Section: Examples Of Factors Of Variation and Associated Invariantsmentioning

confidence: 99%

Invariant Methods in Computer Vision

2020

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“…HU [25] apresentou em sua teoria, uma possibilidade capaz de realizar a descrição de figuras geomé-tricas planas com base nos momentos invariantes bidimensionais [26,27,28].…”

Section: Extração De Característicasunclassified

Classificaçâo automatica do tipo de ferro fundido utilizando reconhecimento de padrões em imagens de microscopia

et al. 2017

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RESUMOOs ferros fundidos possuem uma microestrutura, na qual, o formato da grafita presente tem direta influência na classificação entre os diversos tipos deste material. A classificação é feita, tradicionalmente, a partir da análise visual realizada por um especialista através do auxílio de um microscópio ótico. O presente trabalho propõe a utilização da Inteligência Computacional Aplicada em conjunto com extratores de informações em imagens de metalografia. A finalidade é auxiliar profissionais da área das Ciências dos Materiais na classificação de ferros fundidos de maneira automática e reduzir o tempo de classificação, restringindo o máximo possível as falhas presentes durante a classificação. Foram realizadas duas etapas para a análise. Na primeira foram consideradas as grafitas separadamente. Em uma segunda etapa, a imagem completa foi analisada considerando todas as grafitas extraídas da mesma, em que o tipo de objeto com maior incidência na análise seria o reconhecimento adotado para toda a amostra. Em ambas as etapas o classificador Support Vector Machine obteve os melhores resultados no reconhecimento do tipo de ferro fundido, com resultados próximos a 100%, e com redução média do tempo de classificação em 92%. Tanto os resultados como o tempo das classificações são comparados com a análise do especialista, como também aos resultados obtidos em classificações do ferro fundido que utilizam uma abordagem com redes neurais e uma classificação supervisionada utilizando apenas os descritores de forma. A partir dos resultados apresentados, conclui-se que a abordagem é promissora podendo incorporar softwares comerciais para auxiliar especialistas da área. Palavras-chave:Ferro fundido, Processamento Digital de Imagens, Reconhecimento de Padrões, Inteligên-cia Computacional Aplicada. ABSTRACTThe cast iron has a microstructure in which the shape of the contained graphite has direct influence in the classification between the several types of this material. The classification is usually made through a visual analysis performed by a specialist using an optical microscope. This work proposes the use of Applied Computational Intelligence in conjunction with extractors of information in metallographic images. The purpose is to assist professionals in the field of Materials Science in the classification of cast iron automatically and reduce the time for classification, restricting as much as possible the faults presented during classification. Two steps were performed for the analysis. In the first one we considered the graphites separately. In a second step, we analyzed the complete image considering all the graphite extracted from it, in which the type of object with greater incidence in the analysis would be the recognition adopted for the whole sample. In both steps, the classifier Support Vector Machine obtained the best results in the recognition of the type of cast iron, with results close to 100%, with a mean reduction of the classification time by 92%. Both the results and the time of the classifications are ...

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“…Highly compressed information about the object, even if it has been extracted from a blurred image without any restoration, could be sufficient provided that the features used for object description are not much affected by blur. This idea was originally proposed by Flusser et al [4,5,6,12] who introduced so-called blur invariants with respect to non-parametric centrosymmetric and N -fold symmetric h. For Gaussian blur, first heuristically derived blur invariants were proposed by Liu and Zhang [10]. Later on, Zhang et al [15] proposed a distance measure between two images which is independent of a circular Gaussian blur.…”

Section: Introductionmentioning

confidence: 99%

Image Invariants to Anisotropic Gaussian Blur

et al. 2019

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The paper presents a new theory of invariants to Gaussian blur. Unlike earlier methods, the blur kernel may be arbitrary oriented, scaled and elongated. Such blurring is a semi-group action in the image space, where the orbits are classes of blur-equivalent images. We propose a non-linear projection operator which extracts blur-insensitive component of the image. The invariants are then formally defined as moments of this component but can be computed directly from the blurred image without an explicit construction of the projections. Image description by the new invariants does not require any prior knowledge of the particular blur kernel shape and does not include any deconvolution. Potential applications are in blur-invariant image recognition and in robust template matching.

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Projection Operators and Moment Invariants to Image Blurring

Cited by 44 publications

References 76 publications

Invariant Methods in Computer Vision

Invariant Methods in Computer Vision

Classificaçâo automatica do tipo de ferro fundido utilizando reconhecimento de padrões em imagens de microscopia

Image Invariants to Anisotropic Gaussian Blur

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