A New Approach to the Implementation of Histogram Equalization in Image Processing

Yelmanov, Sergei; Hranovska, Olena; Romanyshyn, Yuriy

doi:10.1109/aiact.2019.8847920

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Then there is an improved method for Histogram Equalization [6], especially for the histogram alignment that satisfies the basic requirements for the intensity of the transforming image and fulfilling the criteria of self-duality. The formulation is derived using Equations (2) to (5).…”

Section: A Histogram Equalizationmentioning

confidence: 99%

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

Sari,

Marta,

Amin A

et al. 2023

Int. J. Artif. Intell. Robot.

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The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

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Section: A Histogram Equalizationmentioning

confidence: 99%

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

Sari,

Marta,

Amin A

et al. 2023

Int. J. Artif. Intell. Robot.

View full text Add to dashboard Cite

show abstract

“…When the gray value distribution of the image is too concentrated, histogram equalization [30][31][32][33] can make the gray probability distribution of the image uniform and make the histogram of the image as stable as possible. Transform as follows:…”

Section: Histogram Equalizationmentioning

confidence: 99%

Fast Target Recognition Based on Improved ORB Feature

et al. 2022

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A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.

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“…Histogram equalization is the most well-known image processing procedure in the spatial domain based on intensity transformation [20]. Histogram Equalization can be divided into global histogram equalization (GHE) and local histogram equalization (LHE) [21].…”

Section: Histogram Equalizationmentioning

confidence: 99%

Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

Salman

Hayaty

Fajri

2022

JUITA

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In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.

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A New Approach to the Implementation of Histogram Equalization in Image Processing

Cited by 11 publications

References 4 publications

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

Fast Target Recognition Based on Improved ORB Feature

Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

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