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  1. 1

    Segmentation of Malay syllables in connected digit speech using statistical approach by Salam, M.S., Mohamad, Dzulkifli, Saleh, S.H.

    Published 2008
    “…This paper reports the finding of segmentation experiment using four criterions ie. the insertion, omissions, accuracy and segmentation match between the algorithms. …”
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    Article
  2. 2

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

    Published 2024
    “…This thesis presents a local-based stereo matching algorithm to increase the accuracy on complex regions. …”
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    Thesis
  3. 3
  4. 4

    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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    Thesis
  5. 5

    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
    “…Fundamentally, the framework input is the stereo image which represents left and right images respectively. 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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    Article
  6. 6

    An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition by Moayad, Yousif Potrus, Ngah, Umi Kalthum, Bestoun S. , Ahmed

    Published 2014
    “…At first, the pre-segmentation algorithm uses a modified dominant point detection algorithm to mark a minimal number of points which defines the text skeleton. …”
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    Article
  7. 7

    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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    Thesis
  8. 8

    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
  9. 9

    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
    “…There are four steps in iris recognition: segmentation,normalization, encoding and matching. Here, iris segmentation has been implemented using Hough Transform and Integro-Differential Operator techniques. …”
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    Article
  10. 10

    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
    “…In this work, three basic algorithms for segmenting retinal blood vessels, based on different image processing techniques which are Matched filter, Hough transform and Morphological operations are used and their strengths and weaknesses in terms of retinal images segmentation are compared mathematically. …”
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    Proceeding Paper
  11. 11

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

    Published 2015
    “…The segmentation accuracy is evaluated using two approaches which are tumor template matching and comparison with ground truth. …”
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    Thesis
  12. 12

    Image matching using relational graph representation by Lai, Chui Yen

    Published 2005
    “…Feature-based matching algorithm cannot tackle the problem of matching ambiguities easily. …”
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    Thesis
  13. 13

    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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    Article
  14. 14

    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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    Article
  15. 15

    An efficient method of biometric matching using interpolated ECG data by Sidek, Khairul Azami, Sufi, Fahim, Khalil, Ibrahim, Al-Shammary, Dhiah

    Published 2010
    “…Three different databases with two different sampling rates containing 36 ECG recordings were used for development and evaluation. Each ECG recording is divided into two segments: a segment for enrolment, and a segment for recognition. …”
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    Proceeding Paper
  16. 16

    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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    Article
  17. 17

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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    Final Year Project
  18. 18

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

    Published 2011
    “…The extracted contour is used as template for object recognition during matching process. …”
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    Conference or Workshop Item
  19. 19

    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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    Conference or Workshop Item
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