Pattern recognition by affine moment invariants

Flusser, Jan; Suk, Tomáš

doi:10.1016/0031-3203(93)90098-h

Cited by 685 publications

(318 citation statements)

References 7 publications

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“…In addition, the Fourier descriptors (Calderon De Anda et al 2005c) and geometric moments (Flusser and Suk, 1993) are adopted to assess the irregularity of particles. The Fourier descriptors describe the shape of a particle contour, while geometric moments reflect the affine invariant feature inside characteristic.…”

Section: Crystal Shape Featurementioning

confidence: 99%

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

Huo

Liu

et al. 2016

Chemical Engineering Science

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Abstract:A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system. The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification. Fundamental image features of crystals are selected for efficient classification. In particular, a novel shape feature, referred to as inner distance descriptor, is introduced to quantitatively describe different crystal shapes, which is relatively independent of the crystal size and its geometric direction in an image captured for analysis. Moreover, a pixel equivalent calibration method based on subpixel edge detection and circle fitting is proposed to measure crystal sizes from the captured images. In addition, a kernel function based method is given to deal with nonlinear correlations between multiple features of crystals, facilitating computation efficiency for real-time shape identification.Case study and experimental results from the cooling crystallization of L-glutamic acid demonstrate that the proposed image analysis method can be effectively used for in-situ crystal size measurement and shape identification with good accuracy.

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Section: Crystal Shape Featurementioning

confidence: 99%

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

Huo

Liu

et al. 2016

Chemical Engineering Science

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“…We use the data set consisting of 808 diatoms from the ADIAC project, which have been manually labelled into 38 classes. Previously we have classified this data using several convexity measures both alone and in combination with the following set of descriptors [35]: circularity, ellipticity, rectangularity, triangularity [25] aspect ratio, compactness, convexity, eccentricity, the first four rotation, translation, and scale moment invariants, four rotation, translation, and scale moment invariants [28], the first three affine moment invariants [11]. For each diatom we have both the contour of its outer boundary and also the diatom's ornamentation, which consists of zero or more (mainly open) curve sections in the interior, see figure 22 for examples.…”

Section: Seventh Experiments (2d)mentioning

confidence: 99%

Measuring linearity of curves in 2D and 3D

Rosin

Pantović

Žunić

2016

Pattern Recognition

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In this paper we define a new linearity measure for open curve segments in 2D and 3D. The measure considers the distance of the curve end points to the curve centroid. It is simple to compute and has the basic properties that should be satisfied by any linearity measure. The new measure ranges over the interval (0, 1], and produces the value 1 if and only if the measured curve is a perfect straight line segment. Also, the new linearity measure is invariant with respect to translations, rotations and scaling transformations. The new measure is theoretically well founded and, because of this, its behaviour can be well understood and predicted to some extent. This is always beneficial because it indicates the suitability of the new measure to the desired application.Several experiments are provided to illustrate the behaviour and to demonstrate the efficiency and applicability of the new linearity measure.

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“…In this paper, we use Zernike moments up to order six, which means a total number of 24. Another type of moments, which are invariant under affine transformations, has been introduced by Flusser and Suk [17]. They have computed four moments explicitly.…”

Section: Nonlocal Means With Moment Invariantsmentioning

confidence: 99%

Rotationally invariant similarity measures for nonlocal image denoising

Grewenig

Zimmer

Weickert

2011

Journal of Visual Communication and Image Representation

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Many natural or texture images contain structures that appear several times in the image. One of the denoising filters that successfully take advantage of such repetitive regions is the nonlocal means filter. It is simple and yields very good denoising results. Unfortunately, the block matching within the standard nonlocal means filter is not able to handle rotation or mirroring. Rotated or mirrored instances are not detected as variations of the corresponding original structures. In this paper, we analyse two natural approaches for a rotationally invariant similarity measure that will be used as an alternative to, respectively a modification of the well-known block matching algorithm in nonlocal means denoising. The first approach is based on similarity distances computed with the help of moment invariants whereas the second one estimates the rotation angle, rotates the block via interpolation and then uses a standard block matching. In contrast to the standard method, the presented algorithms can find similar regions or patches in an image even if they appear in several rotated or mirrored instances. With this modification, the nonlocal means filter is able to find more suitable regions for its weighted average.

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Pattern recognition by affine moment invariants

Cited by 685 publications

References 7 publications

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

Measuring linearity of curves in 2D and 3D

Rotationally invariant similarity measures for nonlocal image denoising

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