A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Patanavijit, Vorapoj; Thakulsukanant, Kornkamol

doi:10.11591/ijeecs.v24.i1.pp144-156

Cited by 3 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noise can originate from various factors, primarily from prolonged exposure and high sensitivity. Types of noise include Gaussian noise [14]- [16], Salt and Pepper noise [17]- [19], uniform noise [20]- [22], photon noise [23]- [25], readout noise [26]- [28], reset noise [29]- [31], quantization noise [32]- [34], and more [35]- [37]. To mitigate such noise, noise filters are employed.…”

Section: Algorithm For Recognizing Sudden Accelerationmentioning

confidence: 99%

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

Rhee,

Cho

2024

IJEECS

View full text Add to dashboard Cite

<span>This paper explores using cameras aimed at the accelerator and brake pedals during sudden unintended acceleration in cars, removing noise from captured images to determine driver incompetence. A car model was constructed using Raspberry Pi to simulate brake malfunction using random functions, increasing the revolutions per minute (RPM) to simulate sudden acceleration. By employing a DC encoder motor to measure the motor's rotational speed through waveform counts, the RPM was calculated. The study recognized sudden acceleration when the brake malfunctioned through the DC encoder motor, causing an abnormal RPM increase, allowing camera capture toward the accelerator and brake during sudden acceleration events. Precautions were taken for problems arising from noise in captured images. The Unix operating system was utilized to apply Gaussian filter image processing techniques for noise removal. While using an average value filter led to abrupt changes by replacing with the average of surrounding signals, resulting in an unsmooth image, a Gaussian filter was used in this study to decrease weights as distance from the center increased, mitigating these issues.</span>

show abstract

Section: Algorithm For Recognizing Sudden Accelerationmentioning

confidence: 99%

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

Rhee,

Cho

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…However, the main weakness of the spatial-field optical flow approach is very unstable to determine the MV under the noisy domain. For the noisy domain, many alternative approaches proposed the denoise mechanism [15]- [17] in the prior stage to reduce the interference in the noisy domain. Thus, this paper targets the improvement in the stability of the MV determined by the spatial-field optical flow approach under the noisy domain.…”

Section: Introductionmentioning

confidence: 99%

Noise resistance evaluation of spatial-field optical flow using modifying Lorentzian function

Kesrarat¹,

Patanavijit²

2022

Bulletin EEI

Self Cite

View full text Add to dashboard Cite

This paper presents the evaluation of the modifying Lorentzian function on the spatial-field optical flow to examine the validity in the noisy domain of motion estimation. In the routine of the motion estimation, the frame’s motion vector is estimated by the optical flow approach where the flow of the image’s frames is caught to estimate the motion vector. Nevertheless, in the noisy domain, the preciseness of the motion vector is weakened. We operated the measurement along with several non-Gaussian noises standards through several styles of the standard image frame. The determination on error vector magnitude (EVM) was taken into account to consider the preciseness of direction and length of the motion vector (MV) in comparison with various noise resistance techniques in spatial-field optical flow approach. In the achievement results, we found that this modifying Lorentzian norm function added up in the optical flow strengthen the degree of preciseness in the estimation of the spatial-field optical flow approach in the noisy domain.

show abstract

“…Many model emphasizes efficient transmission of images from one place to another with minimum possible errors [17]- [19]. Many solutions proposed the improvement in the quality of the interfered image or denoise process [20]- [22] in early-stage separately out of the optical flow. But these approaches took additional denoise processing.…”

Section: Introductionmentioning

confidence: 99%

Noise resistance territorial intensity-based optical flow using inverse confidential technique on bilateral function

Kesrarat¹,

Patanavijit²

2021

Bulletin EEI

Self Cite

View full text Add to dashboard Cite

This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.

show abstract

A computational experimental of noise suppressing technique stand on hard decision threshold dissimilarity

Cited by 3 publications

References 19 publications

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

Image and noise reduction for assessing driver incompetence in cases of sudden unintended acceleration

Noise resistance evaluation of spatial-field optical flow using modifying Lorentzian function

Noise resistance territorial intensity-based optical flow using inverse confidential technique on bilateral function

Contact Info

Product

Resources

About