Invariants

theory of invariants and its application to recognition

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LEADERS IN THE FIELD

We are not just inventors of complex moments

During last fifteen years we contributed significantly to the theory of moments invariants. This includes derivation of complete systems of invariants with respect to affine transformation, with respect to blurring by kernels with various types of symmetries and also invariants to combined degradations.

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VICE CHANCELLOR

Translation, rotation, and scaling (TRS) are the simplest transformations of spatial coordinates. TRS, sometimes called similarity transform

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The affine transform is general linear transformation of space coordinates of the image:

u = a0 + a1x + a2y
v = b0 + b1x + b1149

The geometric moments in three dimensions (3D) are defined: 

Tensor method

The moment

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Vector fields are a special kind of multidimensional data. In each pixel, the field is assigned to a vector that shows the direction and the magnitude of the quantity t

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Vector fields are a special kind of multidimensional data. In each pixel, the field is assigned to a vector that shows the direction and the magnitude of the quantity, which has been measured.

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Tensor fields are a special kind of multidimensional data. In each pixel or voxel, the field is assigned to a tensor.

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Moment Invariants to Convolution are mathematical properties or features of shapes that remain constant when the shape is convolved with a kernel, making them useful fo

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Point-Based Projective Invariants are mathematical properties of a set of points that remain unchanged under projective transformations, such as translation, rotation, scaling, and perspective dist

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OUR TOP HIGHLIGHTS FOR THIS FIELD

This database called MEW (Middle European Woods) was originally with

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Images captured by sensors are often incomplete or degraded, and we need to reconstruct them into

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The detection of vortices, ripples and other singu

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From the given input image containing the blurred object in front of a static background and a se

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This web page describes results published in our paper: Michal Šorel, Filip Šroubek, Fast convo

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Super-resolution is a mathematical algorithm that combines multiple low-resolution input images a

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2022 IEEE International Conference on Image Processing (ICIP), p. 33

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Journal of Biomedical Optics vol.21, 12 (2016), p. 1-8

We propose two automatic methods f

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Biomedical Signal Processing and Control, vol.78, September (2022),

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Witnessing impressive results of deep nets in a number of computer v

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Our work is aimed at the processing of image data, collected during material research. We concent

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Infrared analysis has long been used in the fields of art history an

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Jan Flusser, Tomáš Suk, and Barbara Zitová, 2D and 3D Image Analysis by Moments, Wiley

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Jan Flusser, Tomáš Suk, and Barbara Zitová, Moments and Moment Invariants in Pattern R

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Identifying, tracking, and investigating online disinformation and o

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PROviding VErificatioN AssistaNCE for New Content

TH0401042

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Supported by (ID): TA04011392

Grantor: Technology Agency ČR1123

TH04010422667

Standard convolution as a model of radiometric degradation is in majority of cases inaccurate as

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Smartphones are now widely used as photographic devices. Their cheap cameras are prone to many de

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Blind deconvolution aims to estimate the blur PSF from a single blurred image and then deblur the

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Blind deconvolution, which comprises simultaneous blur and image estimation, is a strongly ill-po

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Super-resolution (SR) is the process of combining a sequen

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The blur caused by camera motion is a serious problem in many areas of optical imaging such as re

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We assume a standard acquisition model. The original image undergoes

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