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  1. 1
    Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system

    Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system by Nurnajmin Qasrina Ann, Ayop Azmi

    Published 2018
    “…This thesis explains the development of new algorithm for distance measurement using stereo vision sensor. …”
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  2. 2
    Identification of power poles based on satellite stereo images using graph-cut algorithm

    Identification of power poles based on satellite stereo images using graph-cut algorithm by Qayyum, Abdul, Malik, Aamir Saeed, Mohamad Saad, Mohamad Naufal, Ahmad, Rana Fayyaz, Taun Abdullah, Taun Ab Rashid

    Published 2015
    “…In this paper, we proposed a new method to detect the vegetation using satellite stereo imagery. The method is based on Graph-Cut algorithm which applied on satellite stereo images. …”
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  3. 3
    Identification of power poles based on satellite stereo images using graph-cut algorithm

    Identification of power poles based on satellite stereo images using graph-cut algorithm by Qayyum A., Malik A.S., Saad M.N.M., Ahmad R.F., Abdullah A.R.T.

    Published 2023
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  4. 4
    Literature Survey On Stereo Vision Disparity Map Algorithms

    Literature Survey On Stereo Vision Disparity Map Algorithms by Rostam Affendi, Hamzah, Haidi, Ibrahim

    Published 2016
    “…The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. …”
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  5. 5
    Identification of power poles based on satellite stereo images using graph-cut algorithm

    Identification of power poles based on satellite stereo images using graph-cut algorithm by Qayyum, A., Malik, A.S., Saad, M.N.M., Ahmad, R.F., Abdullah, A.R.T.

    Published 2015
    “…In this paper, we proposed a new method to detect the vegetation using satellite stereo imagery. The method is based on Graph-Cut algorithm which applied on satellite stereo images. …”
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  6. 6
    Stereo matching algorithm based on hybrid convolutional neural network and directional intensity difference

    Stereo matching algorithm based on hybrid convolutional neural network and directional intensity difference by Hamid, Mohd Saad, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Kadmin, Ahmad Fauzan

    Published 2021
    “…A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. …”
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  7. 7
    Improvement Of Stereo Matching Algorithm Based On Sum Of Gradient Magnitude Differences And Semi-Global Method With Refinement Step

    Improvement Of Stereo Matching Algorithm Based On Sum Of Gradient Magnitude Differences And Semi-Global Method With Refinement Step by Hamzah, Rostam Affendi, Ibrahim, Haidi

    Published 2018
    “…A new stereo matching algorithm which uses improved matching cost computation and optimisation using the semi-global method (SGM) is proposed.The absolute difference is sensitive to low textured regions and high noise on the stereo images with radiometric distortions. …”
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  8. 8
    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
    “…This thesis presents a local-based stereo matching algorithm to increase the accuracy on complex regions. …”
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  9. 9
    Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction

    Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction by Abd Razak, Siti Safwana

    Published 2019
    “…Therefore, to overcome the causes of effected accuracy, new Stereo Matching Algorithm (SMA) based on Adaptive Weighted Bilateral Filter (AWBF) was introduced together with characterize the SMA based on quantitative and qualitative measurements and produced SMA performance were evaluate using standard taxonomy of SM. …”
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  10. 10
    Local Stereo Matching Algorithm Using Modified Dynamic Cost Computation

    Local Stereo Matching Algorithm Using Modified Dynamic Cost Computation by Kadmin, Ahmad Fauzan, Hamzah, Rostam Affendi, Abd Manap, Nurulfajar, Hamid, Mohd Saad, Tg. Wook, Tg Mohd Faizal

    Published 2021
    “…This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. …”
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  11. 11
    Designing of disparity map based on hierarchical dynamic programming using satellite stereo imagery

    Designing of disparity map based on hierarchical dynamic programming using satellite stereo imagery by Qayyum A., Malik A.S., Naufal M., Saad M., Mazher M., Rasheed W., Abdullah T.A.R.B.T.

    Published 2023
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  12. 12
    Evaluation of dynamic programming among the existing stereo matching algorithms

    Evaluation of dynamic programming among the existing stereo matching algorithms by Teo, Chee Huat, Nurulfajar, Abd Manap

    Published 2015
    “…There are various types of existing stereo matching algorithms on image processing which applied on stereo vision images to get better results of disparity depth map. …”
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  13. 13
    Improved stereo matching algorithm based on census transform and dynamic histogram cost computation

    Improved stereo matching algorithm based on census transform and dynamic histogram cost computation by Kadmin, Ahmad Fauzan, Hamzah, Rostam Affendi, Abd Manap, Nurulfajar, Hamid, Mohd Saad, Abd Gani, Shamsul Fakhar

    Published 2021
    “…Stereo matching is a significant subject in the stereo vision algorithm. …”
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  14. 14
    Disparity map algorithm for stereo matching process using local based method

    Disparity map algorithm for stereo matching process using local based method by Gan, Melvin Yeou Wei

    Published 2022
    “…The aim of Stereo Vision Disparity Map (SVDM) algorithm is to obtain the disparity map from two images. …”
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  15. 15
    A new function of stereo matching algorithm based on hybrid convolutional neural network

    A new function of stereo matching algorithm based on hybrid convolutional neural network by Hamid, Mohd Saad, Kadmin, Ahmad Fauzan, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Abd Gani, Shamsul Fakhar, Herman, Adi Irwan

    Published 2022
    “…This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. …”
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  16. 16
    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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  17. 17
    A Pixel to Pixel Correspondence and Region of Interest in Stereo Vision Application

    A Pixel to Pixel Correspondence and Region of Interest in Stereo Vision Application by Hamzah, Rostam Affendi, A Aziz, Khairul Azha, M Shokri, Ahmad Sayuthi

    Published 2012
    “…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. …”
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  18. 18
    Modeling Of A Stereo Vision System Using A Genetic Algorithm Based Fuzzy Linear Regression.

    Modeling Of A Stereo Vision System Using A Genetic Algorithm Based Fuzzy Linear Regression. by Hiaw, San Kho, Chee, Peng Lim, Abdul Aziz, Zalina, Abu Hassan, Anwar Hasni

    Published 2007
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  19. 19
    A Pixel Matching Process And Multiples Roi For Stereo Images In Stereo Vision Application

    A Pixel Matching Process And Multiples Roi For Stereo Images In Stereo Vision Application by Hamzah, Rostam Affendi, Salleh, Azahari, A Aziz, Khairul Azha, Mohammed Napiah, Zul Atfyi Fauzan

    Published 2011
    “…The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. …”
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  20. 20
    Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images

    Enhanced Image View Synthesis Using Multistage Hybrid Median Filter For Stereo Images by Ali Hussein Aboali, Maged Yahya

    Published 2018
    “…Disparity depth map estimation of stereo matching algorithm is one of the most active research topics in computer vision.In the field of image processing,many existing stereo matching algorithms to obtain disparity depth map are developed and designed with low accuracy.To improve the accuracy of disparity depth map is quite challenging and difficult especially with uncontrolled dynamic environment.The accuracy is affected by many unwanted aspects including random noises,horizontal streaks,low texture,depth map non-edge preserving, occlusion,and depth discontinuities.Thus,this research proposed a new robust method of hybrid stereo matching algorithm with significant accuracy of computation.The thesis will present in detail the development,design, and analysis of performance on Multistage Hybrid Median Filter (MHMF).There are two main parts involved in our developed method which combined in two main stages.Stage 1 consists of the Sum of Absolute Differences (SAD) from Basic Block Matching (BBM) algorithm and the part of Scanline Optimization (SO) from Dynamic Programming (DP) algorithm.While,Stage 2 is the main core of our MHMF as a post-processing step which included segmentation,merging, and hybrid median filtering.The significant feature of the post-processing step is on its ability to handle efficiently the unwanted aspects obtained from the raw disparity depth map on the step of optimization.In order to remove and overcome the challenges unwanted aspects, the proposed MHMF has three stages of filtering process along with the developed approaches in Stage 2 of MHMF algorithm.There are two categories of evaluation performed on the obtained disparity depth map: subjective evaluation and objective evaluation.The objective evaluation involves the evaluation on Middlebury Stereo Vision system and evaluation using traditional methods such as Mean Square Errors (MSE),Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM).Based on the results of the standard benchmarking datasets from Middlebury,the proposed algorithm is able to reduce errors of non-occluded and all errors respectively.While,the subjective evaluation is done for datasets captured from MV BLUE FOX camera using human's eyes perception.Based on the results,the proposed MHMF is able to obtain accurate results, specifically 69% and 71% of non-occluded and all errors for disparity depth map, and it outperformed some of the existing methods in the literature such as BBM and DP algorithms.…”
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