Improved Edge Detection Algorithms Based on a Riesz Fractional Derivative

Georgescu, Carmina

doi:10.1007/978-3-319-93000-8_23

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In order to prove accurately, the selected sub-matrix is multiplied by the PRIWITT matrix. If the value obtained is greater than zero, it is confirmed that there exists an edge over the pivot pixel [22][23] [24]. Figure 7, illustrates how an edge is detected from an image.…”

Section: Proposed Edge Detection Methodsmentioning

confidence: 98%

A versatile hybrid image fusion model to fuse multispectral image data

Ignatious

El-Sayed

Khan

et al. 2022

Preprint

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Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making caused by their poor perceivance of environmental information. In todays, technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context leading to inadequate decision making causing fatal accidents. To overcome this drawback effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks namely data cleaning and data fusion. Since the raw sensory data is complex and exhibits multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focuses on data fusion. In particular, we propose a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposes a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model is proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features are also proposed. The proposed models are evaluated using various standard metrics with existing popular models. The proposed image fusion model performed better than the other models.

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Section: Proposed Edge Detection Methodsmentioning

confidence: 98%

A versatile hybrid image fusion model to fuse multispectral image data

Ignatious

El-Sayed

Khan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In practical applications, this feature may lead to the defective performance of the corresponding masks. Noise can also pose a challenge to the performance of these algorithms to extract edges in images [3].…”

Section: A Short Review Of Some Of the Well-known Methodsmentioning

confidence: 99%

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

2020

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Computers and electronics play an enormous role in today's society, impacting everything from communication and medicine to science. The development of computer-related technologies has led to the emergence of many new important interdisciplinary fields, including the field of image processing. Image processing tries to find new ways to access and extract information from digital images or videos. Due to this great importance, many researchers have tried to utilize new and powerful tools introduced in pure and applied mathematics to develop new concepts in imaging science. One of these valuable research areas is the contents of fractional differential calculus. In recent years, extensive applications to the new fractional operators have been employed in real-world problems. This article attempts to address a practical aspect of this era of research in the edge detecting of an image. For this purpose, two general structures are first proposed for making new fractional masks. Then the components in these two structures are evaluated using the fractional integral Atangana-Baleanu operator. The performance and effectiveness of these proposed designs are illustrated by several numerical simulations. A comparison of the results with the results of several well-known masks in the literature indicates that the results presented in this article are much more accurate and efficient. This is the main achievement of this article. These fractional masks are all novel and have been introduced for the first time in this contribution. Moreover, in terms of computational cost, the proposed fractional masks require almost the same amount of computations as the existing conventional ones. By observing the numerical simulations presented in the paper, it is easily understood that with proper adjustment for the fractional-order parameter, the accuracy of the obtained results can be significantly improved. Each of the new suggested structures in this article can be regarded as a valid and effective alternative for the well-known existing kernels in identifying the edges of an image.

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Discrete Laplacian Operator and Its Applications in Signal Processing

2020

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Fractional calculus has increased in popularity in recent years, as the number of its applications in different fields has increased. Compared to the traditional operations in calculus (integration and differentiation) which are uniquely defined, the fractional-order operators have numerous definitions. Furthermore, a consensus on the most suitable definition for a given task is yet to be reached. Fractional operators are defined as continuous operators and their implementation requires a discretization step. In this article, we propose a discrete fractional Laplacian as a matrix operator. The proposed operator is real (non-complex) which makes it computationally efficient. The construction of the proposed fractional Laplacian utilizes the DCT transform avoiding the complexity associated with the discretization step which is typical in the constructions based on signal processing. We demonstrate the utility of the proposed operator on a number of data modeling and image processing tasks. INDEX TERMS Fractional-Laplacian, discrete operator, image-processing, trend-filtering, fractional calculus.

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Improved Edge Detection Algorithms Based on a Riesz Fractional Derivative

Cited by 3 publications

References 4 publications

A versatile hybrid image fusion model to fuse multispectral image data

A versatile hybrid image fusion model to fuse multispectral image data

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

Discrete Laplacian Operator and Its Applications in Signal Processing

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