Edge sharpening through ramp width reduction

Leu, Jia-Guu

doi:10.1016/s0262-8856(99)00041-4

Cited by 22 publications

(29 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In noise reduction alone there are hundreds of available techniques which can be categorized into three major groups of filtering, morphological operations and noise modeling [12] [13]. Filters can be designed for smoothing [14], sharpening [15], thresholding [16], removing slightly textured background [17] and contrast adjustment processes [18]. Various morphological operations can be designed to connect broken strokes [19], decompose the connected strokes [20], smooth the contours, prune the wild points, thin the characters [21], and extract boundaries [22].…”

Section: A Preprocessingmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

View full text Add to dashboard Cite

Abstract-The development of handwriting character recognition (HCR) is an interesting area in pattern recognition.HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

show abstract

Section: A Preprocessingmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…Generally, one can deblur a blurry edge by two ways: to improve its contrast and to reduce its width. For a wide and blurry edge, to increase the gray level difference across it, while its width remains unchanged, brings only a very limited effect (see Figure 3(c)) [37].…”

Section: D Casementioning

confidence: 99%

Image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter

Zhang

2011

Sci. China Inf. Sci.

View full text Add to dashboard Cite

A large number of applications in image processing and computer vision depend on image quality. In this paper, main concerns are image denoising and deblurring simultaneously in a restoration task by three types of methodologies: non-convex regularization, inverse diffusion and shock filter. We discuss their relations in the context of image deblurring: the inverse diffusion implied by the non-convex regularization, and the superior ability of deblurring edge of the shock filter to that of the inverse diffusion, both in 1D and 2D cases. Finally, we propose a region-based adaptive anisotropic diffusion with shock filter method, which shows advantages of deblurring edges, denoising and smoothing contours in experiments, compared with some related methods. Therein an idea of "divide and rule" is introduced.

show abstract

“…In noise reduction alone there are hundreds of available techniques which can be categorized into three major groups of filtering, morphological operations and noise modelling (Serra, 1994;Sonka & Boyle, 1999). Filters can be designed for smoothing (Legault & Suen, 1997), sharpening (Leu, 2000), thresholding (Solihin & Leedham, 1999), removing slightly textured background (Lee & Fan, 2000) and contrast adjustment process (Polesel et. al., 1997).…”

Section: The Pre-processing Phasementioning

confidence: 99%

Hybrid of HMM and Fuzzy Logic for Isolated Handwritten Character Recognition

Suliman¹

2010

Character Recognition

View full text Add to dashboard Cite

Edge sharpening through ramp width reduction

Cited by 22 publications

References 20 publications

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter

Hybrid of HMM and Fuzzy Logic for Isolated Handwritten Character Recognition

Contact Info

Product

Resources

About