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  1. 1
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…Based on previous researches, most existing segmentation methods focused on a specific environment. …”
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  2. 2
    Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu

    Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu by Sabtu, Melati

    Published 2005
    “…For overall, the back-propagation algorithm has been proved as a method that can be used for recognition areas.…”
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  3. 3
    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

    The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer by Tehseen Mazhar, Inayatul Haq, Allah Ditta, Syed Agha Hassnain Mohsan, Faisal Rehman, Imran Zafar, Jualang Azlan Gansau, Lucky Poh Wah Goh

    Published 2023
    “…Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. …”
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  4. 4
    Development Of Automatic Liver Segmentation Method For Three- Dimensional Computed Tomography Dataset

    Development Of Automatic Liver Segmentation Method For Three- Dimensional Computed Tomography Dataset by Chew, Chin Boon

    Published 2018
    “…Both methods are able to speed up the segmentation time. …”
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    Monograph
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  5. 5
    UNSUPERVISED SEGMENTATION OF CORAL REEF IMAGES BY USING COLOR AND TEXTURE FEATURES

    UNSUPERVISED SEGMENTATION OF CORAL REEF IMAGES BY USING COLOR AND TEXTURE FEATURES by MOHAMMAD SAMEER ALOUN

    Published 2022
    “…The unsupervised segmentation of color-texture regions using J-value segmentation (JSEG) algorithm is one of the most popular and robust unsupervised segmentation algorithms. …”
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  6. 6
    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation

    Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation by Alia, Osama Moh’d Radi

    Published 2011
    “…Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. …”
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  7. 7
    Image segmentation based on normalised cuts with clustering algorithm

    Image segmentation based on normalised cuts with clustering algorithm by Choong, Mei Yeen

    Published 2013
    “…With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. …”
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  8. 8
    Formulation of hybrid 3D image segmentation algorithm based partial differential equation

    Formulation of hybrid 3D image segmentation algorithm based partial differential equation by Aboaba, Abdulfattah A., Hameed, Shihab A., Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha, Chowdhury, Md. Sazzad Hossien

    Published 2012
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  9. 9
    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images

    Fuzzy modeling of brain tissues in Bayesian segmentation of brain MR images by Farzan, Ali, Ramli, Abdul Rahman, Mashohor, Syamsiah, Mahmud, Rozi

    Published 2010
    “…Hence involving problem specific information and expert knowledge in designing segmentation algorithms seems to be useful. A two-fold fuzzy segmentation algorithm based on Bayesian method is proposed in this paper. …”
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  10. 10
    Novel techniques for enhancement and segmentation of acne vulgaris lesions

    Novel techniques for enhancement and segmentation of acne vulgaris lesions by Malik, A. S., Humayun, J., Kamel, N., Yap, F. B.-B.

    Published 2013
    “…Methods: For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. …”
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  11. 11
    AUTOMATED CERVICAL CELL NUCLEI SEGMENTATION BASED ON MULTILAYER UNSUPERVISED CLUSTERING ALGORITHM AND MORPHOLOGICAL APPROACH

    AUTOMATED CERVICAL CELL NUCLEI SEGMENTATION BASED ON MULTILAYER UNSUPERVISED CLUSTERING ALGORITHM AND MORPHOLOGICAL APPROACH by Khalis Danial Nukman, Khiruddin, Wan Azani, Mustafa, Khairur Rijal, Jamaludin, Khairul Shakir, Ab Rahman, Hiam, Alquran, Syahrul Nizam, Junaini

    Published 2025
    “…The average segmentation score of the 917 images exceeds 90%, highlighting the method's flexibility.…”
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  12. 12
    Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation

    Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

    Published 2009
    “…In order to show the real time ability and robustness of proposed method for color segmentation, experimental results conducted on vision based mobile robot for navigation. …”
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  13. 13
    Model-based hybrid variational level set method applied to lung cancer detection

    Model-based hybrid variational level set method applied to lung cancer detection by Jing, Wang, Liew, Siau-Chuin, Azian, Abd Aziz

    Published 2024
    “…This algorithm simplifies the (Local Intensity Clustering) LIC model and devises a new energy functional based on the region-based pressure function. …”
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  14. 14
    Segmentation of MRI brain images using statistical approaches

    Segmentation of MRI brain images using statistical approaches by Balafar, Mohammad Ali

    Published 2011
    “…Noise is one of the obstacles for brain MRI segmentation. The non-Local means (NL-means) algorithm is a state-of-the art neighbourhood-based noisereduction method which is time-consuming and its accuracy can be improved. …”
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  15. 15
    Image enhancement and segmentation on simultaneous latent fingerprint detection

    Image enhancement and segmentation on simultaneous latent fingerprint detection by Rozita, Mohd Yusof

    Published 2015
    “…Segmentation of 240 images is performed and compared with manual segmentation methods. …”
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  16. 16
    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System

    Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

    Published 2019
    “…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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  17. 17
    A rule-based image segmentation method and neural network model for classifying fruit in natural environment / Hamirul'aini Hambali

    A rule-based image segmentation method and neural network model for classifying fruit in natural environment / Hamirul'aini Hambali by Hambali, Hamirul'aini

    Published 2015
    “…Based on the previous researches, most existing segmentation methods are unable to accurately segment images under natural illumination. …”
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  18. 18
    Sobel and Canny Edges Segmentations for the Dental Age Assessment

    Sobel and Canny Edges Segmentations for the Dental Age Assessment by Razali M.R.M., Ahmad N.S., Hassan R., Zaki Z.M., Ismail W.

    Published 2024
    Conference Paper
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  19. 19
    Multiregion segmentation of mammogram images by using parametric kernel graph cut algorithm / Nurhanani Abdul Rahim, Nor Farah Nabilah Mushtafa and Nadhirah Afiqah Zailan

    Multiregion segmentation of mammogram images by using parametric kernel graph cut algorithm / Nurhanani Abdul Rahim, Nor Farah Nabilah Mushtafa and Nadhirah Afiqah Zailan by Abdul Rahim, Nurhanani, Mushtaf, Nor Farah Nabilah, Zailan, Nadhirah Afiqah

    Published 2019
    “…Many methods are widely applied for image segmentation. …”
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  20. 20
    Development of a new algorithm for segmentation of flotation froth images

    Development of a new algorithm for segmentation of flotation froth images by Jahedsaravani, Ali, Marhaban, Mohammad Hamiruce, Massinaei, Mohammad, Saripan, M. Iqbal, Mehrshad, Naser, Mohd Noor, Samsul Bahari

    Published 2014
    “…The developed algorithm is validated using some froth images captured at different operating conditions, so the results indicate that the method can segment the mixture of big and small bubbles effectively.…”
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