License plate detection for multi-national vehicles – a generalized approach

Asif, Muhammad Rizwan; Qi, Chun; Wang, Tiexiang; Fareed, Muhammad Sadiq; Khan, Subhan

doi:10.1007/s11042-019-08199-4

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…For instances, kernel 𝑔 (1) manages to identify the edges that fall in the northwest, west and also north direction. Meanwhile, kernel 𝑔 (2) can detect edges pointing southwest, west and south. With these four aforementioned kernels, we can expect the edges detected to be covered throughout all the directions.…”

Section: Proposed Mini Kirsch Detection Kernelsmentioning

confidence: 99%

“…In the application of image processing, machine vision, and computer vision, edge detection is one of the crucial steps in pre-processing stages for finding the boundaries of objects within an image, for instance detecting local discontinuities in pixels intensity or brightness for boundaries extraction [1]. Edge detection is widely implemented in the application of car's license plate detection [2], human face recognition through iris localization for eye tracking [3], synthetic aperture radar images to detect edges of ships, aircraft, terrain, meteorological forms and mobile vehicles [4], agricultural plant leaves recognition [5], and dehaze or deblurring method [6]. Furthermore, biomedical image, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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Section: Proposed Mini Kirsch Detection Kernelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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show abstract

“…Although DL emerged in 1982 in the form of neural networks (Hopfield, 1982), it started to gain attention in 2012 due to its notable performance for image classification tasks (Krizhevsky et al, 2017(Krizhevsky et al, , 2012. Since then, it has been applied successfully to many applications including object detection (Asif et al, 2019;Redmon et al, 2016;Ren et al, 2015), image super-resolution (Dong et al, 2016;Zhang et al, 2018), speech recognition (Zhang et al, 2017), and stock market predictions (Pang et al, 2020). The revival of DL was mainly influenced by the availability of cheap computing resources, deeper network architectures, and large-scale publicly available datasets.…”

Section: Introductionmentioning

confidence: 99%

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications

Asif

Foged

Bording

et al. 2023

Earth Syst. Sci. Data

Self Cite

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Abstract. Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database (RMD) with a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. Potentially, the presented database can be used to build surrogate models of well-known processes and to aid in labour-intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, incorporate consistency and credibility into deep learning models. We show the effectiveness of the presented database by surrogating the forward-modelling process, and we urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publicly available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

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“…From the perspective of neurophysiology and psychophysics, relevant scholars have proposed that there are multiple edge masks and stripe masks in the preprocessing of human vision to convolve the image, and the output of these masks approximates the first derivative of the brightness function and second order directional derivatives [29][30][31]. For the step edge, the most drastic changes are the extreme point of the first derivative and the zero-crossing point of the second derivative, so the Gauss-Laplace operator is derived [32]. This method first smoothes the image, that is, the Gaussian function is used to smooth the image, and then the Laplace operator is used to detect the edge, thereby reducing the noise of the image and realizing the preprocessing of the image before edge extraction [33].…”

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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License plate detection for multi-national vehicles – a generalized approach

Cited by 10 publications

References 24 publications

Mini Kirsch Edge Detection and Its Sharpening Effect

Mini Kirsch Edge Detection and Its Sharpening Effect

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications

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

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