Wide Baseline Matching Using Support Vector Regression

Xi, Haifeng; Cui, Weihua

doi:10.12928/telkomnika.v11i3.1144

Cited by 5 publications

(2 citation statements)

References 10 publications

(14 reference statements)

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“…From ( 5), the modulus from gradient magnitude is implemented on the input images where left image is 𝑚 𝑙 and right image is 𝑚 𝑟 . Through the gradient displacement of x-direction and the static position of y-direction, the cost for gradient matching, GM (𝑝, 𝑑) obtained and presented in (6),…”

Section: Matching Cost Computationmentioning

confidence: 99%

“…The corresponding process for pixel matching is one to one pixel matching, which only involves the pixel of interest. However, for block matching, the corresponding process involves multiple pixels which are pixel of interest and the surrounding or neighboring pixels in the elements of the support window [6]. The support window is also reference as "block" or "window".…”

Section: Introductionmentioning

confidence: 99%

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A study of stereo matching algorithm on low texture and depth discontinuity regions

Gan Yeou Wei,

Affendi Hamzah,

Syahrim Nik Anwar

et al. 2023

IJEECS

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<p>This article studies the performance of the proposed stereo matchingalgorithm on complex regions. These regions are areas with very limitedinformation for the matching process which are low texture, and depthdiscontinuity regions. In this study, each algorithm uses different matchingcost computation (MCC) techniques, but for cost aggregation (CA), disparityoptimization (DO) and disparity refinement (DR), the technique remains thesame. The MCC areabsolute difference(AD), the combination ofabsolutedifference and gradient matching(AD+GM) andcensus transform(CT).Then, for CA, DO and DR, they areminimum spanning tree(MST),winnertake all(WTA) andbilateral filter(BF), respectively. The results are presentedand discussed in this article. Hence, thru this study the robust method can beestimated at the MCC stage.</p>

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Section: Matching Cost Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A study of stereo matching algorithm on low texture and depth discontinuity regions

Gan Yeou Wei,

Affendi Hamzah,

Syahrim Nik Anwar

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

A Modelling of Stereo Matching Algorithm for Machine Vision Application

Hamzah

Kadmin

Gani

et al. 2019

Advances in Material Sciences and Engineering

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Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

2013

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Abstrak Tingkat kesembuhan pasien dapat ditingkatkan jika kanker ovarium dapat dideteksi lebih awal. Identifikasi deteksi dini kanker ovarium menggunakan profil ekspresi protein (SELDI-TOF MS Kata kunci: kanker ovarium, klasterisasi one-pass clustering, k-nearest neighbors Abstract The identification of ovarian cancer using protein expression profile (SELDI-TOF-MS) is important to assists early detection of ovarian cancer. The chance to save patient's life is greater when

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Wide Baseline Matching Using Support Vector Regression

Cited by 5 publications

References 10 publications

A study of stereo matching algorithm on low texture and depth discontinuity regions

A study of stereo matching algorithm on low texture and depth discontinuity regions

A Modelling of Stereo Matching Algorithm for Machine Vision Application

Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

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