Text Region Extraction in High Contrasting Image

Kang, Seokjun; Cha, Daewoong; Kim, Youngwoo; Han, Dong

doi:10.18178/ijfcc.2017.6.3.499

Cited by 3 publications

(1 citation statement)

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“…It is worth mentioning that the effectiveness of MSER for gray images is limited in its capability to detect regions of interest in extreme conditions, such as high contrast, low luminance, high light reflection, etc. [7]. Likewise, as the authors of [4] note, MSER is sensitive to blurring.…”

Section: {4 8}-raf and Topological Feature Detectorsmentioning

confidence: 89%

On the Topological Disparity Characterization of Square-Pixel Binary Image Data by a Labeled Bipartite Graph

Sánchez-Cuevas

Real²,

Díaz-del-Río³

et al. 2022

Pattern Recognition and Image Analysis

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Given an nD digital image I based on cubical n-xel, to fully characterize the degree of internal topological dissimilarity existing in I when using different adjacency relations (mainly, comparing 2n or 2 n − 1 adjacency relations) is a relevant issue in current problems of digital image processing relative to shape detection or identification. In this paper, we design and implement a new self-dual representation for a binary 2D image I, called {4, 8}-region adjacency forest of I ({4, 8}-RAF , for short), that allows a thorough analysis of the differences between the topology of the 4-regions and that of the 8-regions of I. This model can be straightforwardly obtained from the classical region adjacency tree of I and its binary complement image I c , by a suitable region label identification. With these two labeled rooted trees, it is possible: (a) to compute Euler number of the set of foreground (resp. background) pixels with regard to 4-adjacency or 8-adjacency; (b) to identify new local and global measures and descriptors of topological dissimilarity not only for one image but also between two or more images. The parallelization of the algorithms to extract and manipulate these structures is complete, thus producing efficient and unsophisticated codes with a theoretical computing time near the logarithm of the width plus the height of an image. Some toy examples serve to explain the representation and some experiments with gray real images shows the influence of the topological dissimilarity when detecting feature regions, like those returned by the MSER (maximally stable extremal regions) method. Keywords: Hierarchical representation • Digital image • Topological dissimilarity • Parallelism • (4 • 8)-adjacency tree • {4,

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Section: {4 8}-raf and Topological Feature Detectorsmentioning

confidence: 89%