Optimization of Laplace of Gaussian (LoG) filter for enhanced edge detection: A new approach

Mallick, Arijit; Roy, Sourya; Chaudhuri, Sheli Sinha; Roy, Sangita

doi:10.1109/ciec.2014.6959172

Cited by 20 publications

(15 citation statements)

References 5 publications

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“…The architectural design of the proposed COVID-RENets is motivated by classical image processing techniques [29] and is based on the idea of exploiting the underlying patterns in images. In this regard, we systematically synergized the use of Region and Edge-based operations as well as convolution operation in CNN to learn the COVID-19 specific pneumonia patterns effectively.…”

Section: Materials and Methodologymentioning

confidence: 99%

Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network

Khan

Sohail

Zafar

et al. 2021

Photodiagnosis and Photodynamic Therapy

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Background: The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and overloaded the health care system. Therefore, it is crucial to develop a fast and accurate diagnostic system for the timely identification of COVID-19 infected patients and thus to control its spread. Methods: This work proposes a new deep CNN based technique for COVID-19 classification in X-ray images. In this regard, two novel custom CNN architectures, namely COVID-RENet-1 and COVID-RENet-2, are developed for COVID-19 specific pneumonia analysis. The proposed technique systematically employs Region and Edge-based operations along with convolution operations. The advantage of the proposed idea is validated by performing series of experimentation and comparing results with two baseline CNNs that exploited either a single type of pooling operation or strided convolution down the architecture. Additionally, the discrimination capacity of the proposed technique is assessed by benchmarking it against the state-of-the-art CNNs on radiologist's authenticated chest X-ray dataset. Implementation is available at https://github.com/PRLAB21/Coronavirus-Disease-Analysis-using-Chest-X-Ray-Images . Results: The proposed classification technique shows good generalization as compared to existing CNNs by achieving promising MCC (0.96), F-score (0.98) and Accuracy (98%). This suggests that the idea of synergistically using Region and Edge-based operations aid in better exploiting the region homogeneity, textural variations, and region boundary-related information in an image, which helps to capture the pneumonia specific pattern. Conclusion: The encouraging results of the proposed classification technique on the test set with high sensitivity (0.98) and precision (0.98) suggest the effectiveness of the proposed technique. Thus, it suggests the potential use of the proposed technique in other X-ray imagery-based infectious disease analysis.

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Section: Materials and Methodologymentioning

confidence: 99%

Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network

Khan

Sohail

Zafar

et al. 2021

Photodiagnosis and Photodynamic Therapy

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“…20 Many conventional filters have been developed to perform edge detection. The Prewitt filter, 21 Sobel edge detector, 21 Canny edge detector, 22 Zero Crossing Filter, and Laplace of Gaussian (LoG) filter 23 are widely used in image processing. The Canny edge detector was demonstrated to outperform other edge detection techniques in many applications.…”

Section: Edge Detectionmentioning

confidence: 99%

“…The Canny detector exhibits excellent performance, preserving most of the valuable edge information in the nonwoven web image. Unlike the Canny detector, another commonly used edge detector, LoG operator, 23 often loses certain edge information in dark regions. Figure 4 shows comparison results of Canny edge detection and LoG edge detection.…”

Section: Edge Detectionmentioning

confidence: 99%

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

Hou

Gao

et al. 2017

Textile Research Journal

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This paper describes a novel method for measuring fiber orientations in nonwoven web images by using Bézier fitting curves to detect corners of fiber edges and to separate crossing fiber edges. First, the Canny detector was adopted to extract fiber edges. Second, Bézier curve fitting was used to fit each fiber edge for calculating the curvature of every point on the edge. Third, corner points were detected by locating points where the curvatures were minimal on various edges and below the threshold to divide edges into segments for orientation calculations. Last, a formula calculating the fiber orientation statistics based on the Euclidean distance was established. The experiment results demonstrated that the proposed method is robust for analyzing different nonwoven web images, and has a high accuracy for corner detection and fiber orientation calculation.

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“…Due to its quick rate of convergence and fewer parameters to adjust, lot of works isdeveloping recently [15] [16][21] [24]. Cuckoo search algorithm has been modified in many different algorithms [24][20] [22].CS has been used for solving structural optimization and image processing problems [12] [23]. Springs and welded beam structures are optimally designed using cuckoo search [11][13] [14].…”

Section: Iiliterature Surveymentioning

confidence: 99%

A novel approach on Cuckoo search algorithm using Gamma distribution

Roy

Mallick

Chowdhury

et al. 2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Cuckoo search algorithm (CS) is one of the most efficient optimization techniques developed so far. Several attempts have been made in past in order to improve the efficiency of CSO algorithm. In this paper we have tried to exploit the fundamental step length distribution function of the CS algorithm in order to increase its efficiency. Cuckoo search is a metaheuristic optimization technique. In place of conventional Levy distribution, Gamma distribution has been used. We will represent the increased efficiency of the Gamma distribution aided CSO algorithm in the following paper.

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Optimization of Laplace of Gaussian (LoG) filter for enhanced edge detection: A new approach

Cited by 20 publications

References 5 publications

Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network

Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

A novel approach on Cuckoo search algorithm using Gamma distribution

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