2-D Gabor filter based transition region extraction and morphological operation for image segmentation

Parida, Priyadarsan; Bhoi, Nilamani

doi:10.1016/j.compeleceng.2016.10.019

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…Image filtering based on Gabor filters is a procedure widely used for the extraction of spatially localized spectral features. The frequency and orientation representation of Gabor filters are similar to human visual system, and they have been found vital features that can be used for image segmentation [ 16 , 28 ]. In our project, the processed images of fire forest combine many complexities due to the higher intensity’s variation and the texture geometrical diversity.…”

Section: Methodsmentioning

confidence: 99%

“…In the last decade, the Gabor filters, firstly proposed by Dennis Gabor in 1946 in 1-D and extended, in 1985, to 2-D by Daugman, have received much attention. Their wide usage in multiple fields can be taken as proof of their success: image analysis, compression and restoration, object tracking and movement estimation, face recognition, smoke detection, texture retrieval, contour extraction, or image segmentation [ 14 , 15 , 16 , 17 ].…”

Section: Motivationmentioning

confidence: 99%

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A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig¹,

Bouchouicha

Tlig

et al. 2020

Sensors

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Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

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Section: Methodsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig¹,

Bouchouicha

Tlig

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The final step for defect detection is post processing by morphological operation. Here, the morphological operation [25] termed opening is used on the binary or grayscale image by way of the structuring element. There must be a distinct structuring element object, while divergent to group of objects.…”

Section: E Post Processing By Morphological Operationmentioning

confidence: 99%

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJACSA

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There are various mathematical optimization problems that can be effectively solved by meta-heuristic algorithms. The improvement of these algorithms is that they carry out iterative search processes which resourcefully act upon exploration and exploitation in spatial domain containing global and local optima. An innovative robust Cuckoo Optimization Algorithm (COA) with adaptive thresholding is proposed to solve the problem of detection and estimation of surface defects on metal coating surfaces. The proposed method is developed through implementing changes to COA and improved the performance. For improving capability of local search as well to keep the global search effect, the enhanced methods such as level set is associated with the proposed method. Also, the method adapts dynamic step size, adaptively changing with the search process for improving the rate of convergence and the ability of local search. The algorithm performance is scrutinized from the experimental analysis and results. Also, the segmentation effectiveness is further enhanced by adapting suitable methods for preprocessing and post processing. The comparison and analysis of the results accomplished with the proposed method and results of earlier methods shows superior performance of the proposed method.

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“…2). Optimization paramater using Cuckoo Search Algorithm (CSA) In 2009, Yang and Deb formulated CSA, an optimization algorithm inspired by population of cuckoo bird [10]. In this experiment, CSA will be implemented to optimalize the balancing parameter α and value of percentile thresholding and final thresholding.…”

Section: B Weighted Thresholding 1) Threshold Estimationmentioning

confidence: 99%

Image Thresholding Based on Hierarchical Clustering Analysis and Percentile Method for Tuna Image Segmentation

Puspaningrum

Nur

Riza

et al. 2018

NJCA

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Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

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2-D Gabor filter based transition region extraction and morphological operation for image segmentation

Cited by 16 publications

References 20 publications

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

A Modified Adaptive Thresholding Method using Cuckoo Search Algorithm for Detecting Surface Defects

Image Thresholding Based on Hierarchical Clustering Analysis and Percentile Method for Tuna Image Segmentation

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