A novel wavelet edge detection algorithm for noisy images

Srivastava, Gaurav; Verma, Rohit; Mahrishi, Ruchika; Siddavatam, Rajesh

doi:10.1109/icumt.2009.5345404

Cited by 15 publications

(15 citation statements)

References 11 publications

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“…Mallat and Zhong [14] showed that the local maxima in a wavelet transform of an image can be used to detect and locate edges in that image. Since then, several wavelet-based edge detection operators have been proposed to date (see [15], [16], [17] for example).…”

Section: B Multi-resolution Techniquesmentioning

confidence: 99%

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

Naeem

Siraj

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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In the fields of computer vision and image processing, edge detection refers to the identification and localization of significant changes in a digital image. This article presents a survey of widely-used edge detection techniques including linear approaches, morpohlogical operations, multi-resolution analysis, and machine learning methods. Since there exists no single method that is applicable in all situations, different methods are deployed for different applications. A hierarchical framework based on multiple criteria has been proposed here that can facilitate the process of selecting the most appropriate edge detection method in a given scenario. Use of the proposed framework has been explained through an example of medical images. Finally, possible areas for further exploration have been highlighted.

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Section: B Multi-resolution Techniquesmentioning

confidence: 99%

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

Naeem

Siraj

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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“…Sharp discontinuities in an image are very well known as edge detection [4]. Sobel, Prewitt, Canny, Laplacian and Roberts are considered as examples of traditional edge detection operators [5].…”

Section: Introductionmentioning

confidence: 99%

Shape based Image Retrieval Utilising Colour Moments and Enhanced Boundary Object Detection Technique

Alnihoud¹

2018

ijacsa

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The need for automatic object recognition and retrieval have increased rapidly in the last decade. In contentbased image retrieval (CBIR) visual cues such as colour, texture, and shape are the most prominent features used. Texture features are not considered as a significant discriminator unless it is integrated with colour features. Colour-based image retrieval uses global and/or local features has proved its ability to retrieve images with a high degree of accuracy. In contrast, shape-based retrieval is still suffering from numerous unsolved problems such as precise edge detection, overlapping objects, and high cost of feature extraction. In this paper, global colour features are utilized to discriminate unrelated images. Furthermore, a novel hybrid approach is proposed, consisting of a combination of boundary-based shape descriptor (BBSD) and region-based shape descriptor (RBSD), image retrieval. An enhanced object boundary detection (EBOD) is proposed, which uses canny edge detector to detect shape boundaries, with morphological opening to remove isolated nodes. Subsequently, morphological closing is utilized to solidify objects within the target image to enhance shape-based features representation. Finally, shape features are extracted and Euclidean distance measure with different threshold values to measure the similarity between feature vectors is adopted. Five semantic categories of WANG image database are selected to test the proposed approach. The results of experiments are promising, when compared with most common related approaches.

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“…They employ a thresholding technique to classify a pixel as an edge or non-edge based on its magnitude, a pixel with a weak magnitude may be recognized as non-edge and accordingly the edges become broken. Noise phenomena is an important hindrance to the detection of continuous edges [14]. It causes some variation of pixel intensities and accordingly reduces the performance of an ED algorithm in noisy images.…”

Section: Introductionmentioning

confidence: 99%

An improved teaching–learning based robust edge detection algorithm for noisy images

Thirumavalavan

Jayaraman

2016

Journal of Advanced Research

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A novel wavelet edge detection algorithm for noisy images

Cited by 15 publications

References 11 publications

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

A Framework to Select Edge Detection Method Using Multi-criteria Decision Making

Shape based Image Retrieval Utilising Colour Moments and Enhanced Boundary Object Detection Technique

An improved teaching–learning based robust edge detection algorithm for noisy images

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