Efficient 3D stereo vision stabilization for  multi-camera viewpoints

Bhuiyan, Sharif Shah Newaj; Khalifa, Othman O.

doi:10.11591/eei.v8i3.1518

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Sharif Shah et al 17 described a technique for efficient 3D stereo vision stabilization for various camera views. The method makes use of spanning tree algorithm, Laplace pyramid scaling, and Harris corner detection to find precise matching key locations in images and stable camera angles.…”

Section: The Use Of Harris Corner Detectionmentioning

confidence: 99%

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

Fadhil,

Hussien,

Khairi

et al. 2024

Baghdad Sci.J

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Text encryption in video is a vital tool in many industries where protecting sensitive information is of highest concern. Algorithms and automated detection methods commonly struggle to locate hidden text in video frames. This resistance increases the security of the concealed information because it is challenging for uninvited parties to detect the presence of encrypted text. This research suggests a novel method for text encryption inside video utilizing the Salsa20 encryption algorithm and Harris Corner Detection. The goal of this study is to increase the secrecy and security of textual information that is embedded in video content. First step is locateing important corners or spots inside the video frames, the Harris Corner Detection method is first used. These corners act as strong feature points that can be used to incorporate text. Without degrading the video's overall visual quality, the Harris Corner Detection method aids in precisely localizing the places where text may be concealed. Second, the encryption key is generated using the Salsa20 encryption technique. Salsa20 is a popular stream cipher that offers reliable encryption and effective operation. The embedded text is converted into a cipher text by XOR it with the encryption key before embedding it in the video, guaranteeing its confidentiality and protecting it from unauthorized access. Comparing the suggested method to current text encryption methods reveals a number of advantages. The Harris Corner Detection technique integrates text into video frames so that it blends in less noticeably, making it more difficult for adversaries to identify the existence of hidden information. Salsa20's use further guarantees the encrypted text's high level of security and protection, defending it against potential attacks. The proposed system is evaluated using MSE, PSNR, correlation, NPCR, UACI and entropy, these evaluation metrics provide excellent results.

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Section: The Use Of Harris Corner Detectionmentioning

confidence: 99%

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

Fadhil,

Hussien,

Khairi

et al. 2024

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

“…where p is (𝑥, 𝑦); the position of the targeted pixel, d is disparity value, 𝑚 𝑙 is gradient of left image and 𝑚 𝑟 is gradient of right image. By adding a cut-off point, the gradient matching with threshold, 𝐺𝑀 ′ (𝑝, 𝑑) is presented in (7),…”

Section: Matching Cost Computationmentioning

confidence: 99%

“…Only then, the aggregated pixel of interest from each images; left image and right image will correspondence with each other. Examples of block matching techniques are sum of absolute difference (SAD), sum squared difference (SSD) normalized cross correlation (NCC), rank transform (RT) and census transform (CT) according to the literature survey of [7]. Block matching techniques produces better disparity map compared to the pixel matching technique, but the computation time will be longer and closely tied to the sizes of the support window.…”

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

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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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“…Computation of this method is usually fast. However, the quality suffers, especially in the depth discontinuity area [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

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Efficient 3D stereo vision stabilization for multi-camera viewpoints

Cited by 6 publications

References 14 publications

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

A Proposed Text Encryption inside Video Using Harris Corner Detection and Salas20 Encryption Algorithm

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

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

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