Content Based Image Retrieval Using Gray Scale Weighted Average Method

Kumar, K. P. Sanal; Li, Jianping; Zain-Ul-Abidin, None; Shaikh, Riaz Ahmed

doi:10.14569/ijacsa.2016.070101

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…Merging cell value of the reconstruction results between the current injection and magnetic field method is conducted using 4 ways, namely minimum, maximum, maxmin, and average value (Kumar et al, 2016) (Arai, 2020) (Noushad, 2017) (Wang, 2020). By using these ways, the simulation results of the merging of the two methods are shown in table 3.…”

Section: Merging Of Reconstruction Results Of Current Injection and M...mentioning

confidence: 99%

Electrical Resistance Tomographic by Using Current Injection and Magnetic Field Induction

Darmawan

Kurniadi²,

Suprijanto³

2023

J. Geoscience Eng. Environ. Technol.

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A critical issue in electrical tomography is ill-posed problems due to low sensitivity. In the electric current injection method, the placement of the injection electrode on the object boundary can influence it. This condition causes the reconstruction result of parameter change far away from the boundary to be inferior in quality. Another excitation method is using magnetic field induction proposed to overcome these problems. Each reconstruction image was obtained using two methods with three types of parameter changes, that represented the edge and the center of the object position. Both reconstruction results are merged and further processed to enhance the quality of the image, based on the average value of the resistivity of each element. The results show that the final image reconstruction has a smaller root mean square error (RMSE) than the electric current injection method.

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Section: Merging Of Reconstruction Results Of Current Injection and M...mentioning

confidence: 99%

Electrical Resistance Tomographic by Using Current Injection and Magnetic Field Induction

Darmawan

Kurniadi²,

Suprijanto³

2023

J. Geoscience Eng. Environ. Technol.

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“…In this paper, the weighted average method is used for image gray processing, and different weights are used for the weighted average of RGB color components. The specific principles are interpreted as follows [22]: Frontiers in Physics frontiersin.org…”

Section: Image Preprocessmentioning

confidence: 99%

Tire appearance defect detection method via combining HOG and LBP features

Liu

Jia

et al. 2023

Front. Phys.

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Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.

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“…In so doing, slack variables are considered to verbalize the latent violations and to a drawback parameter for blocking the margin violations. The function which linearly used to learn SVMs is stated as; 7 where w is a weight vector, x is to input sample and b represents a used threshold. To characterize the minimum distance between hyper-plane and support vectors, we maximized the margin of hyper-plane, which separated via the trained SVMs learner.…”

Section: Fig 1 Sideline Of Svms Hyper-planementioning

confidence: 99%

“…In contrast, the high-level techniques are active to retrieve patterns by scanning whole image. The semantic digital image retrieval is one the talented exploration field where several researchers consider approaches either for image analysis [5,6,7,8] or image retrieval [9,10,11,12]. Most techniques of image retrieval usually used text metadata that relied on the description oh image's textual [13,14,15].…”

Section: Introductionmentioning

confidence: 99%