Research on Algorithms for Calculation of Projective Transformation in the Problem of Planar-Object Targeting by Feature Points

Shemiakina, Julia; Zhukovsky, Alexander; Faradjev, Igor

doi:10.3103/s0147688218050064

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The term text feature point corresponds to the generally accepted concept of a feature (key) point consisting of a pair of coordinates and a descriptor that allows distinguishing a feature point from adjacent points of the image. Known examples of descriptors are SIFT, SURF, ORG, YAP descriptors [16]. In the case of a text feature point, the descriptor is the kernel () Ker W , and there may be feature points with the same descriptors, but with different localization in the document.…”

Section: Methods Descriptionmentioning

confidence: 99%

Comparison of scanned administrative document images

Андреева¹,

Arlazarov²,

Slavin

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In this work the methods of comparison of digitized copies of administrative documents were considered. This problem arises, for example, when comparing two copies of documents signed by two parties in order to find possible modifications made by one party, in the banking sector at the conclusion of contracts in paper form. The proposed method of document image comparison is based on a combination of several ways of image comparison of words that are descriptors of text feature points. Testing was conducted on public Payslip Dataset (French). The results showed the high quality and the reliability of finding differences in two images that are versions of the same document.

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Section: Methods Descriptionmentioning

confidence: 99%

Comparison of scanned administrative document images

Андреева¹,

Arlazarov²,

Slavin

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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“…Thus, in theory, the normalization should be projective. The latter is commonly employed as a part of image preprocessing for various computer vision tasks, such as document OCR [1,2,3,4,5], vehicle license plate recognition [6], TV-stream recognition based on a picture of a TV screen [7], chessboard recognition [8], artificial on-road obstacles detection [9], object detection using shape features (detection of the shape of an object within an image and matching that shape with an object from database) [10,11,12,13,14,15], surface parameters monitored from satellites (time-temporal variability of sea surface temperature, determining the velocity of the cloud masses motion, etc.) [16], reconstruction of plans and maps from the aerial photographs [17,18], and many more.…”

Section: Projective Image Normalizationmentioning

confidence: 99%

Optimal affine image normalization approach for optical character recognition

Konovalenko¹,

Kokhan²,

Nikolaev³

2021

Computer Optics

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Optical character recognition (OCR) in images captured from arbitrary angles requires preliminary normalization, i.e. a geometric transformation resulting in an image as if it was captured at an angle suitable for OCR. In most cases, a surface containing characters can be considered flat, and a pinhole model can be adopted for a camera. Thus, in theory, the normalization should be projective. Usually, the camera optical axis is approximately perpendicular to the document surface, so the projective normalization can be replaced with an affine one without a significant loss of accuracy. An affine image transformation is performed significantly faster than a projective normalization, which is important for OCR on mobile devices. In this work, we propose a fast approach for image normalization. It utilizes an affine normalization instead of a projective one if there is no significant loss of accuracy. The approach is based on a proposed criterion for the normalization accuracy: root mean square (RMS) coordinate discrepancies over the region of interest (ROI). The problem of optimal affine normalization according to this criterion is considered. We have established that this unconstrained optimization is quadratic and can be reduced to a problem of fractional quadratic functions integration over the ROI. The latter was solved analytically in the case of OCR where the ROI consists of rectangles. The proposed approach is generalized for various cases when instead of the affine transform its special cases are used: scaling, translation, shearing, and their superposition, allowing the image normalization procedure to be further accelerated.

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Research on Algorithms for Calculation of Projective Transformation in the Problem of Planar-Object Targeting by Feature Points

Cited by 2 publications

References 5 publications

Comparison of scanned administrative document images

Comparison of scanned administrative document images

Optimal affine image normalization approach for optical character recognition

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