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Journal articles on the topic 'And Morphological operations'

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Published: 4 June 2025
Last updated: 23 June 2025

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1

Agam, Gady, and Its’hak Dinstein. "Regulated morphological operations." Pattern Recognition 32, no. 6 (1999): 947–71. http://dx.doi.org/10.1016/s0031-3203(98)00120-4.

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2

SATO, Hisashi. "Pseudo-morphological Operations using Set Operations." Journal of Graphic Science of Japan 35, no. 4 (2001): 11–18. http://dx.doi.org/10.5989/jsgs.35.4_11.

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3

Cheng, C. H., and D. L. Yang. "Realisation of morphological operations." IEE Proceedings - Circuits, Devices and Systems 142, no. 6 (1995): 364. http://dx.doi.org/10.1049/ip-cds:19952204.

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4

Chudasama, Diya, Tanvi Patel, Shubham Joshi, and Ghanshyam I. Prajapati. "Image Segmentation using Morphological Operations." International Journal of Computer Applications 117, no. 18 (2015): 16–19. http://dx.doi.org/10.5120/20654-3197.

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5

Arfa hassan and Shazia Saqib. "Circle Detection Using Morphological Operations." Lahore Garrison University Research Journal of Computer Science and Information Technology 3, no. 2 (2019): 31–34. http://dx.doi.org/10.54692/lgurjcsit.2019.030275.

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Circle detection is the very important and challenging field in image processing. In previous few decants the field of fining and detection of circular objects in images gain more attention because the location and additional information of circular object in the image can easily be find when a circle is extracted in that image so that information can be used in industries and businesses in many ways. The circle detection is today commonly used in computer application i.e. Computer vision applications and in the field of robotics to detect and recognize the circular objects. In measurement based images the circle detection is the most important task and necessary task. In my article I used a method that detect circle in the image with fast calculations and in short time instant of these methods that perform heavy calculations that consume processing power of GPU as well as time in circle detection. This method uses a simple algorithm that detect circle using simple calculations
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6

Weeks, Arthur R. "Morphological operations on color images." Journal of Electronic Imaging 10, no. 2 (2001): 548. http://dx.doi.org/10.1117/1.1353199.

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7

Jonker, Pieter P. "Morphological operations in recursive neighbourhoods." Pattern Recognition Letters 25, no. 5 (2004): 527–41. http://dx.doi.org/10.1016/j.patrec.2003.12.004.

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8

Sarabia-Pérez, Rubén, Antonio Jimeno-Morenilla, and Rafael Molina-Carmona. "Deterministic mathematical morphology for CAD/CAM." Engineering Computations 31, no. 7 (2014): 1221–41. http://dx.doi.org/10.1108/ec-02-2013-0056.

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Purpose – The purpose of this paper is to present a new geometric model based on the mathematical morphology paradigm, specialized to provide determinism to the classic morphological operations. The determinism is needed to model dynamic processes that require an order of application, as is the case for designing and manufacturing objects in CAD/CAM environments. Design/methodology/approach – The basic trajectory-based operation is the basis of the proposed morphological specialization. This operation allows the definition of morphological operators that obtain sequentially ordered sets of points from the boundary of the target objects, inexistent determinism in the classical morphological paradigm. From this basic operation, the complete set of morphological operators is redefined, incorporating the concept of boundary and determinism: trajectory-based erosion and dilation, and other morphological filtering operations. Findings – This new morphological framework allows the definition of complex three-dimensional objects, providing arithmetical support to generating machining trajectories, one of the most complex problems currently occurring in CAD/CAM. Originality/value – The model proposes the integration of the processes of design and manufacture, so that it avoids the problems of accuracy and integrity that present other classic geometric models that divide these processes in two phases. Furthermore, the morphological operative is based on points sets, so the geometric data structures and the operations are intrinsically simple and efficient. Another important value that no excessive computational resources are needed, because only the points in the boundary are processed.
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9

Jimeno-Morenilla, Antonio, Francisco A. Pujol, Rafael Molina-Carmona, José L. Sánchez-Romero, and Mar Pujol. "Trajectory-Based Morphological Operators: A Model for Efficient Image Processing." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/801587.

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Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images.
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10

A, Srikrishna, Pallavi P, Geetha Madhuri V, and Neelima N. "Object Segmentation Using Multiscale Morphological Operations." International Journal of Computer Science, Engineering and Information Technology 3, no. 4 (2013): 11–23. http://dx.doi.org/10.5121/ijcseit.2013.3402.

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11

Raid, A. M., W. M. Khedr, M. A. El-dosuky, and Mona Aoud. "Image Restoration Based on Morphological Operations." International Journal of Computer Science, Engineering and Information Technology 4, no. 3 (2014): 9–21. http://dx.doi.org/10.5121/ijcseit.2014.4302.

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12

Mohamed, M. A., and J. Saniie. "Statistical evaluation of sequential morphological operations." IEEE Transactions on Signal Processing 43, no. 7 (1995): 1703–9. http://dx.doi.org/10.1109/78.398731.

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13

Comer, Mary L. "Morphological operations for color image processing." Journal of Electronic Imaging 8, no. 3 (1999): 279. http://dx.doi.org/10.1117/1.482677.

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14

Shih, F. Y., Chung Ta King, and C. C. Pu. "Pipeline architectures for recursive morphological operations." IEEE Transactions on Image Processing 4, no. 1 (1995): 11–18. http://dx.doi.org/10.1109/83.350817.

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15

Busch, Christoph, and Michael Eberle. "Morphological Operations for Color-Coded Images." Computer Graphics Forum 14, no. 3 (1995): 193–204. http://dx.doi.org/10.1111/1467-8659.1430193.

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16

Busch, Christoph, and Michael Eberle. "Morphological Operations for Color-Coded Images." Computer Graphics Forum 14, no. 3 (1995): 193–204. http://dx.doi.org/10.1111/j.1467-8659.1995.cgf143_0193.x.

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17

Parvathi, R., and C. Yuvapriya. "Morphological operations on temporal intuitionistic fuzzy sets." Notes on Intuitionistic Fuzzy Sets 28, no. 4 (2022): 397–412. http://dx.doi.org/10.7546/nifs.2022.28.4.397-412.

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This paper is devoted to develop the theory of temporal intuitionistic fuzzy sets. The matrix representation of a TIFS is also introduced for easy symbolization. In addition to a few basic operations, length of a TIFS and its properties are discussed. Morphological operations on temporal intuitionistic fuzzy sets are defined using (i) mathematical operations, (ii) structuring element, (iii) inclusion indicators, and (iv) temporal intuitionistic fuzzy divergence and verified with suitable examples.
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18

Madar, Bandu, G. Kiran, and C. Ramakrishna. "Captcha Breaking using Segmentation and Morphological Operations." International Journal of Computer Applications 166, no. 4 (2017): 34–38. http://dx.doi.org/10.5120/ijca2017914013.

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19

Bilynsky, Y. Y., A. I. Nikolskyy, and A. B. Huralnyk. "Filtering Ultrasound Images Based on Morphological Operations." Visnyk of Vinnytsia Politechnical Institute 145, no. 4 (2019): 71–79. http://dx.doi.org/10.31649/1997-9266-2019-145-4-71-79.

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20

Wilson, G. R. "Morphological operations on crack coded binary images." IEE Proceedings - Vision, Image, and Signal Processing 143, no. 3 (1996): 171. http://dx.doi.org/10.1049/ip-vis:19960320.

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21

Ndunge Mutua, Elizabeth, Bernard Shibwabo Kasamani, and Christoph Reich. "Retinal Image Quality Assessment Using Morphological Operations." International Journal of Computing and Digital Systems 15, no. 1 (2024): 1111–22. http://dx.doi.org/10.12785/ijcds/160181.

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22

Shih, Frank Y., and Jenlong Moh. "Implementing morphological operations using programmable neural networks." Pattern Recognition 25, no. 1 (1992): 89–99. http://dx.doi.org/10.1016/0031-3203(92)90009-8.

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23

Zhang, Yuan, Xiurong Ma, and Duo Cao. "Automatic Modulation Recognition Based on Morphological Operations." Circuits, Systems, and Signal Processing 32, no. 5 (2013): 2517–25. http://dx.doi.org/10.1007/s00034-013-9577-4.

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24

Kamil, Mohammed Y., and Ali Mohammed Salih. "Breast Tumor Detection Via Fuzzy Morphological Operations." International Journal of Advanced Pervasive and Ubiquitous Computing 11, no. 1 (2019): 33–44. http://dx.doi.org/10.4018/ijapuc.2019010103.

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Breast cancer is one of most dangerous diseases and more common in women. The early detection of cancer is one of the most key factors for possible cure. There are numerous methods of diagnosis amongst which: clinical examination, sonar and mammography, which is the best and more effective in detecting breast cancer. Detection of breast tumors is difficult because of the weak illumination in the image and the overlap between regions. Segmentation is one the crucial steps in locating the tumors, which is an important method of diagnosis of the computer. In this study, segmentation techniques are proposed based on; classic morphology and fuzzy morphology, and a comparison between them. The proposed methods were tested using the database of mini -MIAS, which contains 322 images. After the comparison the statistical results, it shows, the detection of tumor boundary with fuzzy morphology give the higher accuracy than the results in classic morphology. The accuracy is 60.69%, 58.61% respectively due to the high flexibility of foggy logic in dealing with the low lighting in the medical images.
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25

AMER, AISHY. "NEW BINARY MORPHOLOGICAL OPERATIONS FOR EFFECTIVE LOW-COST BOUNDARY DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 02 (2003): 201–13. http://dx.doi.org/10.1142/s0218001403002307.

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In this paper, new operational definitions of binary morphological, both conditional and nonconditional, operations are proposed. The new operations are applied to detect boundary points from binary images. Comparisons of boundary detection algorithms using proposed, standard morphological, and gradient-based operations, showing the effectiveness of the proposed operations, are given. Comparative hardware implementations of standard and proposed morphological operations are also given. Main distinguishing aspects of the new operations are: speed and low hardware implementation (i.e., e.g., low number of buffers and D-Flip–Flops).
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26

A., Panimalar, Mohana K., Parvathi R., and Santhosh Kumar S. "Mathematical Morphological Operations for Quadri -Partitioned Neutrosophic Set." International Journal of Neutrosophic Science 23, no. 2 (2024): 77–90. http://dx.doi.org/10.54216/ijns.230207.

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This research aims to introduce a novel notion in mathematical morphological operations on a quadri-partitioned neutrosophic set, which is a particular case of the neutrosophic refined set. In neutrosophic theory, the set is divided into three parts: the true set, the false set, and the indeterminancy set. The indeterminacy is studied in depth in quadri neutrosophic. The primary intention is to reduce uncertainty. The suggested study extracts the core concepts of morphological operations and explains their algebraic properties. Some features of morphological operators linked to quadri-partitioned neutrosophic sets are also derived.
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27

Mohammed, Rana. "Recognition of Printed English Characters Using Morphological Operations." AL-Rafidain Journal of Computer Sciences and Mathematics 8, no. 1 (2011): 135–45. http://dx.doi.org/10.33899/csmj.2011.163614.

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28

Mello-Román, Julio César, José Luis Vázquez Noguera, Horacio Legal-Ayala, et al. "Dermoscopy Images Enhancement via Multi-Scale Morphological Operations." Applied Sciences 11, no. 19 (2021): 9302. http://dx.doi.org/10.3390/app11199302.

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Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.
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29

Kaur, Savreet, and Raman Maini. "Implementation of Barcode Localization Technique using Morphological Operations." International Journal of Computer Applications 97, no. 13 (2014): 42–47. http://dx.doi.org/10.5120/17068-7488.

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30

Plaza, A., P. Martinez, R. Perez, and J. Plaza. "Spatial/spectral endmember extraction by multidimensional morphological operations." IEEE Transactions on Geoscience and Remote Sensing 40, no. 9 (2002): 2025–41. http://dx.doi.org/10.1109/tgrs.2002.802494.

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31

Kulshreshtha, Akanksha, and Arpita Nagpal. "ANALYSIS OF MORPHOLOGICAL OPERATIONS ON IMAGE SEGMENTATION TECHNIQUES." ICTACT Journal on Image and Video Processing 12, no. 1 (2021): 2555–58. http://dx.doi.org/10.21917/ijivp.2021.0362.

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Image segmentation is a process of partitioning an image into different subregions based on edge detection, area based or clustering based methods. Segmentation of brain MRI images is a challenging task. This paper provides a thorough analysis of different segmentation techniques with morphological operators for brain tumor detection. After segmenting the image, morphological operators are used to eliminate and add some pixels from tumor boundaries and to improve the performance of segmentation algorithm. Manual segmentation is used to construct the gold standard for comparing the segmented image. Comparison is performed using performance parameters such as dice, Jaccard coefficient, selectivity, recall and precision. The experimental results show that precision can be improved up to 85% in clustering-based segmentation and full selectivity can be achieved by combining segmentation techniques with morphological operation of erosion. The other performance parameters have also improved by applying erosion than dilation.
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32

Vardavoulia, Maria I. "Hardware implementation of soft color image morphological operations." Optical Engineering 41, no. 7 (2002): 1536. http://dx.doi.org/10.1117/1.1482721.

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33

Boutalis, Y. S., K. Tsirikolias, B. G. Metrtzios, and I. T. Andreadis. "Implementation of morphological filters using coordinate logic operations." Pattern Recognition 35, no. 1 (2002): 187–98. http://dx.doi.org/10.1016/s0031-3203(01)00017-6.

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34

Zhang, Li, and Liren Liu. "Morphological operations for symbolic substitution and Hough transform." Optics Communications 76, no. 3-4 (1990): 194–98. http://dx.doi.org/10.1016/0030-4018(90)90281-w.

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35

Kim, Wook-Joong, Seong-Dae Kim, and Hyder Radha. "3D binary morphological operations using run-length representation." Signal Processing: Image Communication 23, no. 6 (2008): 442–50. http://dx.doi.org/10.1016/j.image.2008.04.010.

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36

Wang, Zhenzhou. "A new clustering method based on morphological operations." Expert Systems with Applications 145 (May 2020): 113102. http://dx.doi.org/10.1016/j.eswa.2019.113102.

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37

Ghosh, Pijush K., and Robert M. Haralick. "Mathematical morphological operations of boundary-represented geometric objects." Journal of Mathematical Imaging and Vision 6, no. 2-3 (1996): 199–222. http://dx.doi.org/10.1007/bf00119839.

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38

Ajwang, PO, and CL Kanali. "Separation of overlapping image objects using morphological operations." Material Science & Engineering International Journal 7, no. 4 (2023): 181–83. http://dx.doi.org/10.15406/mseij.2023.07.00221.

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In application of robots in harvesting of agricultural and horticultural crops, a common problem is the occurrence of overlapping objects in a scene. Typically, a partially obscured object must be conclusively identified and extracted before it can be picked by the robot. Morphological operations can be used to smooth edges of a region, force shapes onto region edges, count regions and estimate the size of regions. Morphological operators typically use set theories of intersection and union. The case of separating overlapping flowers is presented in this paper. Thresholding, followed by sequential erosion and dilation were successfully applied to separate overlapping flowers in a series of images.
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39

Embick, David, and Rolf Noyer. "Movement Operations after Syntax." Linguistic Inquiry 32, no. 4 (2001): 555–95. http://dx.doi.org/10.1162/002438901753373005.

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We develop a theory of movement operations that occur after the syntactic derivation, in the PF component, within the framework of Distributed Morphology.The theory is an extension of what was called Morphological Merger in Marantz 1984 and subsequent work.A primary result is that the locality properties of a Merger operation are determined by the stage in the derivation at which the operation takes place: specifically, Merger that takes place before Vocabulary Insertion, on hierarchical structures, differs from Merger that takes place post—Vocabulary Insertion/linearization.Specific predictions of the model are tested in numerous case studies.Analyses showing the interaction of syntactic movement, PF movement, and rescue operations are provided as well, including a treatment of Englishdo-support.
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40

Collins, Chris, and Richard S. Kayne. "Towards a Theory of Morphology as Syntax." Studies in Chinese Linguistics 44, no. 1 (2023): 1–32. http://dx.doi.org/10.2478/scl-2023-0001.

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Abstract Phenomena traditionally thought of as morphological can be accounted for in terms of syntactic operations and principles, hence bringing forth questions that traditional morphology fails to ask (for instance, concerning the licensing of empty morphemes). The language faculty contains no specific morphological component, nor any post-syntactic morphological operations.
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41

Dewi, Putrie Lestari, Madenda Sarifuddin, Ernastuti, and Prasetyo Wibowo Eri. "Comparison of Three Segmentation Methods for Breast Ultrasound Images Based on Level Set and Morphological Operations." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 383–91. https://doi.org/10.11591/ijece.v7i1.pp383-391.

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Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (CV) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), TrueNegative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.
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42

Zhu, You Lian, and Cheng Huang. "Median Morphological Filter Design Based on the PSO Algorithm." Applied Mechanics and Materials 128-129 (October 2011): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.181.

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Design of morphological filter greatly depends on morphological operations and structuring elements selection. A filter design method used median closing morphological operation is proposed to enhance the image denoising ability and the PSO algorithm is introduced for structural elements selecting. The method takes the peak value signal-to-noise ratio (PSNR) as the cost function and may adaptively build unit structuring elements with zero square matrix. Experimental results show the proposed method can effectively remove impulse noise from a noisy image, especially from a low signal-to-noise ratio (SNR) image; the noise reduction performance has obvious advantages than the other.
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Li, Xinxin, Meiling Tian, Jinyu He, Jiaqi Di, Aijun Zhang, and Xiaohong Qin. "Simulation evaluation of knitted mesh structure using morphological operations." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502211172. http://dx.doi.org/10.1177/15589250221117215.

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High-quality simulation of fabric structural features is significant for realizing the morphological prediction of nonuniform mesh structures and building numerical simulation of physical properties. For lace textiles which have hundreds and thousands of variable meshes, an objective and unified standard to evaluate structural deformation is quite challenging. It needs to comprehensively and quantitatively conclude a result, instead of visual and subjective judgment. Therefore, this paper proposed an image-based method to mathematically solve accuracy by comparing morphological features from both simulation result and real fabric photograph. Jacquardtronic lace textiles were fabricated as experimental samples. Based on morphological expansion and corrosion algorithms, non-characteristic lapping details were eroded from binary images with only featured single-pixel contours of irregular meshes. Shape descriptor of each featured image was represented by a moment vector of seven Hu invariant moments. Then the morphological vectors of both simulated and real fabric images were substituted into a defined equation of similarity measurement. This image-based evaluation model effectively avoids defects of subjective visual observation and geometric measurement methods.
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44

Chen, Bo, and Meng Jia. "A Novel Edge Detection Approach Based on Soft Morphological Operations." Applied Mechanics and Materials 220-223 (November 2012): 2828–32. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2828.

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Edge detection and target segmentation is difficult due to noise existing in an image. A novel edge detection method is proposed based on soft morphological operations in this paper. Because soft morphological operations can remove noise while preserving image details, which can be used to construct morphological edge detection operators with high robustness and better edge effect. Experimental results show that, comparing with the existing edge detection operators, the novel edge detection method can get better edge effect while removing pseudo edges.
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45

GATOS, BASILIOS, STAVROS J. PERANTONIS, NIKOS PAPAMARKOS, and IOANNIS ANDREADIS. "FAST IMPLEMENTATION OF MORPHOLOGICAL OPERATIONS USING BINARY IMAGE BLOCK DECOMPOSITION." International Journal of Image and Graphics 04, no. 02 (2004): 183–202. http://dx.doi.org/10.1142/s0219467804001361.

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Morphological transformations are commonly used to perform a variety of image processing tasks. However, morphological operations are time-consuming procedures since they involve ordering and min/max computation of numbers resulting from image interaction with structuring elements. This paper presents a new method that can be used to speed up basic morphological operations for binary images. To achieve this, the binary images are first decomposed in a set of non-overlapping rectangular blocks of foreground pixels that have predefined maximum dimensions. Then off-line dilation and erosion of all rectangular blocks are arbitrary obtained and stored into suitable look-up array tables. By using the look up tables, the results of the morphological operations to the rectangular blocks are directly obtained. Thus, first all image blocks are replaced by their look-up array tables. Then the morphological operations are applied only to the limited number of the remaining pixels. Experimental results reveal that starting from a block represented binary image morphological operations can be executed with different types of structuring elements in significantly less CPU time. Using the block representation, we are able to perform dilation 16 times faster than non-fast implementations and 10 times faster than an alternative fast implementation based on contour processing. Significant acceleration is also recorded when using this approach for repeated application of dilation (for 10 iterations, dilation using the block representation is over 20 times faster than non-fast implementations and over four times faster than using the fast contour based approach).
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46

Joseph, Neetha, and Jyotsna E. "ROAD DETECTION USING MORPHOLOGICAL OPERATIONS IN A COMPLEX SCENARIO." ICTACT Journal on Image and Video Processing 4, no. 2 (2013): 702–8. http://dx.doi.org/10.21917/ijivp.2013.0102.

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47

Gaffar, Achmad Fanany Onnilita, Supriadi Supriadi, Arief Bramanto Wicaksono Saputra, Rheo Malani, and Agusma Wajiansyah. "A Splicing Technique for Image Tampering using Morphological Operations." Signal and Image Processing Letters 1, no. 2 (2019): 36–45. http://dx.doi.org/10.31763/simple.v1i2.4.

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Image tampering is one part of the field of image editing or manipulation that changes certain parts of the graphic content of a given image. There are several techniques commonly used for image tampering, such as splicing, copy-move, retouching, etc. Splicing is a type of image tampering technique that combines two different images, replacing particular objects, skewing, rotation, etc. This study applies the splicing technique to image tampering using morphological operations. Morphology is a collection of image processing operations that process images based on their shape. The aim of this study is to replace particular objects in an original image with other objects that are similar to another selected image. In this study, we try to replace the ball object in the original image with another ball object from another image
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48

Jones, R., and I. Svalbe. "Algorithms for the decomposition of gray-scale morphological operations." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 6 (1994): 581–88. http://dx.doi.org/10.1109/34.295903.

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49

Morales, Eduardo, and Frank Y. Shih. "Wavelet coefficients clustering using morphological operations and pruned quadtrees." Pattern Recognition 33, no. 10 (2000): 1611–20. http://dx.doi.org/10.1016/s0031-3203(99)00147-8.

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Dong, Pinliang. "Implementation of mathematical morphological operations for spatial data processing." Computers & Geosciences 23, no. 1 (1997): 103–7. http://dx.doi.org/10.1016/s0098-3004(96)00065-9.

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