The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

Othman, Mahmod; Abdullah, Sharifah Lailee Syed; Ahmad, Khairul Azman; Bakar, Mohd Nazari Abu; Mansor, Ab Razak

doi:10.32890/jict2016.15.1.6

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…The term "edge-based model" refers to a model that uses an edge detection function to convert original images into edge images. The techniques are useful for shape boundary recognition (Othman et al, 2016). Active Contour Model (ACM) is an effective edge-based algorithm that is widely used in image segmentation to detect objects in an image using curve evolution techniques (Fang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Selective Segmentation Model for Vector-Valued Images

Ghani

Jumaat

2022

Journal of Information and Communication Technology

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One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.

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Section: Introductionmentioning

confidence: 99%

Selective Segmentation Model for Vector-Valued Images

Ghani

Jumaat

2022

Journal of Information and Communication Technology

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“…Generally, edge detection techniques are used to find discontinuities in a gray level image. The techniques that are useful for shape boundary recognition (Othman et al, 2016) can be implemented using edge detection operators, such as Prewitt, Sobel, Roberts, Canny, and Test operators (Saini & Arora, 2014).…”

Section: Introductionmentioning

confidence: 99%

Selective Image Segmentation Models Using Three Distance Functions

Jumaat

Abdullah

2021

JICT

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Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.

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“…Image edge detection is the process of detecting discontinuous points in the image function [4]. The execution efficiency of the algorithm and the detection accuracy of the discontinuous points are important criteria for judging the application of the algorithm in the field of computer vision [5][6][7]. The inaccuracy of the edge detection algorithm directly affects the application of target tracking and recognition in image sequences, and also affects the results of feature-based image processing techniques, such as stereo analysis, region segmentation, data encoding, image restoration, data hiding.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

Liu

Chen

et al. 2020

IEEE Access

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Image edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection. The basic theory and processing flow of image edge detection are analyzed, and several commonly used edge detection preprocessing methods and their basic principles are elaborated in detail. The existing traditional edge detection methods are briefly introduced, and the traditional edge detection methods are compared. This paper studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image. INDEX TERMS edge detection; multiple operators; adaptive dynamic weights; virtual reality scenes.

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The Fusion of Edge Detection and Mathematical Morphology Algorithm for Shape Boundary Recognition

Cited by 5 publications

References 5 publications

Selective Segmentation Model for Vector-Valued Images

Selective Segmentation Model for Vector-Valued Images

Selective Image Segmentation Models Using Three Distance Functions

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

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