31,964 research outputs found
Colour Text Segmentation in Web Images Based on Human Perception
There is a significant need to extract and analyse the text in images on Web documents, for effective indexing, semantic analysis and even presentation by non-visual means (e.g., audio). This paper argues that the challenging segmentation stage for such images benefits from a human perspective of colour perception in preference to RGB colour space analysis. The proposed approach enables the segmentation of text in complex situations such as in the presence of varying colour and texture (characters and background). More precisely, characters are segmented as distinct regions with separate chromaticity and/or lightness by performing a layer decomposition of the image. The method described here is a result of the authorsâ systematic approach to approximate the human colour perception characteristics for the identification of character regions. In this instance, the image is decomposed by performing histogram analysis of Hue and Lightness in the HLS colour space and merging using information on human discrimination of wavelength and luminance
Text Extraction from Web Images Based on A Split-and-Merge Segmentation Method Using Color Perception
This paper describes a complete approach to the segmentation and extraction of text from Web images for subsequent recognition, to ultimately achieve both effective indexing and presentation by non-visual means (e.g., audio). The method described here (the first in the authorsâ systematic approach to exploit human colour perception) enables the extraction of text in complex situations such as in the presence of varying colour (characters and background). More precisely, in addition to using structural features, the segmentation follows a split-and-merge strategy based on the Hue-Lightness- Saturation (HLS) representation of colour as a first approximation of an anthropocentric expression of the differences in chromaticity and lightness. Character-like components are then extracted as forming textlines in a number of orientations and along curves
Two Approaches for Text Segmentation in Web Images
There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perceptionâ in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)
Two Approaches for Text Segmentation in Web Images
There is a significant need to recognise the text in images on web pages, both for effective indexing and for presentation by non-visual means (e.g., audio). This paper presents and compares two novel methods for the segmentation of characters for subsequent extraction and recognition. The novelty of both approaches is the combination of (different in each case) topological features of characters with an anthropocentric perspective of colour perceptionâ in preference to RGB space analysis. Both approaches enable the extraction of text in complex situations such as in the presence of varying colour and texture (characters and background)
Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline
Neurodegenerative and neuroinflammatory diseases regularly cause optic nerve and
retinal damage. Evaluating retinal changes using optical coherence tomography (OCT)
in diseases like multiple sclerosis has thus become increasingly relevant. However,
intraretinal segmentation, a necessary step for interpreting retinal changes in the context
of these diseases, is not standardized and often requires manual correction. Here
we present a semi-automatic intraretinal layer segmentation pipeline and establish
normative values for retinal layer thicknesses at the macula, including dependencies on
age, sex, and refractive error. Spectral domain OCT macular 3D volume scans were
obtained from healthy participants using a Heidelberg Engineering Spectralis OCT. A
semi-automated segmentation tool (SAMIRIX) based on an interchangeable third-party
segmentation algorithm was developed and employed for segmentation, correction, and
thickness computation of intraretinal layers. Normative data is reported froma 6mmEarly
Treatment Diabetic Retinopathy Study (ETDRS) circle around the fovea. An interactive
toolbox for the normative database allows surveying for additional normative data. We
cross-sectionally evaluated data from218 healthy volunteers (144 females/74males, age
36.5 ± 12.3 years, range 18â69 years). Average macular thickness (MT) was 313.70 ±
12.02 Όm, macular retinal nerve fiber layer thickness (mRNFL) 39.53 ± 3.57 Όm, ganglion
cell and inner plexiform layer thickness (GCIPL) 70.81 ± 4.87 Όm, and inner nuclear layer
thickness (INL) 35.93 ± 2.34 Όm. All retinal layer thicknesses decreased with age. MT
and GCIPL were associated with sex, with males showing higher thicknesses. Layer
thicknesses were also positively associated with each other. Repeated-measurement
reliability for the manual correction of automatic intraretinal segmentation results was excellent, with an intra-class correlation coefficient >0.99 for all layers. The SAMIRIX
toolbox can simplify intraretinal segmentation in research applications, and the normative
data application may serve as an expandable reference for studies, in which normative
data cannot be otherwise obtained
- âŠ