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  1. 1
    Image enhancement and segmentation on simultaneous latent fingerprint detection

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

    Published 2015
    “…This thesis presents an algorithm to segment individual fingerprints for SLF image. …”
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  2. 2
    Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image

    Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image by Zainal Azali, Muhammad Nazmi

    Published 2024
    “…In computer vision technology, stereo matching algorithm plays an important role in generating disparity map or depth map through a correspondence process from stereo images. …”
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  3. 3
    An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

    An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation by Ismael, Ahmed Naser

    Published 2016
    “…Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. …”
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  4. 4
    Mathematical snalysis of blood vessels segmentation based on three retinal images datasets

    Mathematical snalysis of blood vessels segmentation based on three retinal images datasets by Bilal, Sara Mohammed Osman Saleh, Munir, Fatin, Khalifa, Othman Omran

    Published 2014
    “…The results show that measuring the automatic segmentation algorithm performance is based mainly on how the retinal images are acquired as well as the image processing technique used for segmentation. …”
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  5. 5
    Image matching using relational graph representation

    Image matching using relational graph representation by Lai, Chui Yen

    Published 2005
    “…The matching accuracy of images of complicated scene remains low even after the incorporation of inter-line descriptions into the image matching algorithm.…”
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  6. 6
    Stereo Matching Algorithm Based On Per Pixel Difference Adjustment, Iterative Guided Filter And Graph Segmentation

    Stereo Matching Algorithm Based On Per Pixel Difference Adjustment, Iterative Guided Filter And Graph Segmentation by Hamzah, Rostam Affendi, Ibrahim, Haidi, Abu Hassan, Anwar Hasni

    Published 2017
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  7. 7
    Embedded car plate image recognition system

    Embedded car plate image recognition system by Moo Wui Hung

    Published 2008
    “…In order to speed up the image processing process, the system do not use complex algorithm such as Neural Network but use a simple algorithm such as template matching. …”
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    Learning Object
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  8. 8
    Stereo matching algorithm using census transform and segment tree for depth estimation

    Stereo matching algorithm using census transform and segment tree for depth estimation by Hamzah, Rostam Affendi, Zainal Azali, Muhammad Nazmi, Mohd Noh, Zarina, Tengku Wook, Tg Mohd Faisal, Zainal Abidin, Izwan

    Published 2023
    “…The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. …”
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  9. 9
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. …”
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  10. 10
    Blood vessels segmentation based on three retinal images datasets

    Blood vessels segmentation based on three retinal images datasets by Bilal, Sara Mohammed Osman Saleh, Munir, Fatin, Karbasi, Mostafa

    Published 2016
    “…The results show that measuring the automatic segmentation algorithm performance is based mainly on how the retinal images are acquired as well as the image processing technique used for segmentation. …”
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  11. 11
    Disparity Refinement Based on Depth Image Layers Separation for Stereo Matching Algorithms

    Disparity Refinement Based on Depth Image Layers Separation for Stereo Matching Algorithms by Abd Manap, Nurulfajar, Soraghan, John J.

    Published 2012
    “…This paper presents a method to improve the raw disparity maps in the disparity refinement stage for stereo matching algorithm. The proposed algorithm will use the disparity depth map from the stereo matching algorithm as initial disparity depth output with a basic similarity metric of SAD. …”
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  12. 12
    Quality inspection of engraved image using based matching approach

    Quality inspection of engraved image using based matching approach by Norazlina , Ahmad, Marizan, Sulaiman, Muhamad Khairi, Aripin

    Published 2011
    “…This paper proposes shape-based vision algorithm, a hierarchical template-matching approach that implemented in flexible manufacturing system to verify the quality of engraved image. …”
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  13. 13
    THE INVESTIGATION ON ARABIC WORD POSE ESTIMATION ALGORITHM AS MARKER FOR AUGMENTED REALITY APPLICATION

    THE INVESTIGATION ON ARABIC WORD POSE ESTIMATION ALGORITHM AS MARKER FOR AUGMENTED REALITY APPLICATION by Mohd Zailani, Syarah Munirah, Morshidi, Malik Arman

    Published 2018
    “…This study investigates which combination of matching technique with Infinitesimal Plane-Based Pose Estimation (IPPE) that suits better in estimating the pose of Arabic text images without character segmentation. …”
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  14. 14
    Shape-Based Matching: Application of Edge Detection Using Harris Point

    Shape-Based Matching: Application of Edge Detection Using Harris Point by Sulaiman , Marizan, Mohd Shah, Hairol Nizam

    Published 2011
    “…This paper presents a seqence of object rcognition algorithm using shape-based matching that mainly focused on image recognition, image segmentation and flexible Region of Interest.…”
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  15. 15
    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System

    Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System by Zainal Abidin, Zaheera, -, M.Manaf, Shibghatullah, Abdul Samad, -, S.H.A.Mohd Yunos, -, S.Anawar, -, Z.Ayop

    Published 2012
    “…Then the image captured is segmented and normalized for encoding process. …”
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  16. 16
    Region and active contour-based segmentation technique for medical and weak-edged images

    Region and active contour-based segmentation technique for medical and weak-edged images by Aboaba, Abdulfattah A., Hameed, Shihab A., Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha

    Published 2015
    “…One of the key requirement in image guided surgery (IGS)/ computer aided surgery (CAS) planning is accurate segmentation of the images concerned. …”
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  17. 17
    Development of images segmentation using image thresholder and batch processing technique on the blood smears

    Development of images segmentation using image thresholder and batch processing technique on the blood smears by Al-Shoukry, Suhad, Zalili, Musa, Amer, Duha, Kareem, Safaa Muhsen

    Published 2022
    “…Image segmentation is an important part of image processing, and one of the most common approaches is threshold segmentation. …”
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  18. 18
    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System

    Iris Segmentation Analysis Using Integro-Differential Operator And Hough Transform In Biometric System by Zainal Abidin, Zaheera, Anawar, Syarulnaziah, Ayop, Zakiah, Manaf, Mazani, Shibghatullah, A.S., Mohd Yunos, S.H.A.

    Published 2012
    “…Iris segmentation is foremost part of iris recognition system.There are four steps in iris recognition: segmentation,normalization,encoding and matching.Here, iris segmentation has been implemented using Hough Transform and IntegroDifferential Operator techniques.The performance of iris recognition system depends on segmentation and normalization technique.Iris recognition systems capture an image from individual eye.Then the image captured is segmented and normalized for encoding process.The matching technique,Hamming Distance,is used to match the iris codes of iris in the database weather it is same with the newly enrolled for verification stage.These processes produce values of average circle pupil,average circle iris,error rate and edge points.The values provide acceptable measures of accuracy False Accept Rate (FAR) or False Reject Rate (FRR).Hough Transform algorithm,provide better performance,at the expense of higher computational complexity.It is used to evolve a contour that can fit to a non-circular iris boundary.However,edge information is required to control the evolution and stopping the contour.The performance of Hough Transform for CASIA database was 80.88% due to the lack of edge information.The GAR value using Hough Transform is 98.9% genuine while 98.6% through Integro-Differential Operator.…”
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  19. 19
    Retinal blood vessel segmentation by using matched filtering and fuzzy C-means clustering with integrated level set method for diabetic retinopathy assessment

    Retinal blood vessel segmentation by using matched filtering and fuzzy C-means clustering with integrated level set method for diabetic retinopathy assessment by Memari, Nogol, Ramli, Abd Rahman, Saripan, M. Iqbal, Mashohor, Syamsiah, Moghbel, Mehrdad

    Published 2019
    “…A genetic algorithm enhanced spatial fuzzy c-means method is then utilised for extracting an initial blood vessel network, with the segmentation further refined by using an integrated level set approach.Results:The proposed method is validated by using publicly accessible digital retinal images for vessel extraction, structured analysis of the retina and Child Heart and Health Study in England (CHASE_DB1) datasets. …”
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  20. 20
    Stroke-to-stroke matching in on-line signature verification

    Stroke-to-stroke matching in on-line signature verification by Ahmad Jaini, Azhar

    Published 2010
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