Different Thresholding Techniques in Image Processing : A Review

Seelaboyina, Radha; Vishwakarma, Rajeev G.

doi:10.1007/978-981-19-5936-3_3

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…Generally, binarization involves adaptive binarization, Otsu thresholding, etc. [2,3]. The adaptive binarization algorithm divides an image into several areas and then uses the pixel value around it to obtain a threshold.…”

Section: Related Workmentioning

confidence: 99%

“…To analyze 3D pattern films, a range of inspection methods such as shape, texture, similarity, and contrast, as found in existing algorithms, can be employed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Segmentation algorithms may be considered for analyzing the shape of 3D pattern films, while edge detection algorithms can be applied to study texture.…”

Section: Introductionmentioning

confidence: 99%

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Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

Lee,

Choi,

Kim

2023

Electronics

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A recent development in marketing tools aimed at boosting sales involves the use of three-dimensional pattern films. These films are designed to captivate customers’ interest by applying 3D patterns to the surface of products. However, ensuring the quality of these produced 3D films can be quite challenging due to factors such as low contrast and unclear layout. Furthermore, there has been a shortage of research on methods for evaluating the quality of 3D pattern films, and existing approaches have often failed to yield satisfactory results. To address this pressing issue, we propose an algorithm for classifying 3D pattern films into either ‘good’ or ‘bad’ categories. Unlike conventional segmentation algorithm or edge detection methods, our proposed algorithm leverages the width information at specific heights of the image histograms. The experimental results demonstrate a significant disparity in histogram shapes between good and bad patterns. Specifically, by comparing the widths of all images at the quintile of the histogram height, we show that it is possible to achieve a 100% accuracy in classifying patterns as either ‘good’ or ‘bad’. In comparative experiments, our proposed algorithm consistently outperformed other methods, achieving the highest classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

Lee,

Choi,

Kim

2023

Electronics

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“…To accomplish this, we utilize the Fast Fourier transform algorithm proposed by Mlyahilu and Kim [23] to extract images by pattern in the 3D film. Let I(i, j) be the N × N image in a spatial domain which is sampled from the Discrete Fourier Transform and is defined as F(k, l), as shown in Equation (2).…”

Section: Fast Fourier Transform Algorithm For Cutting 3d Film Pattern...mentioning

confidence: 99%

“…Thus, there is a need to develop an inspection algorithm to identify defective 3D film products. Quality inspection for 3D films can be performed using existing research methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

et al. 2023

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A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

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Enhance Image Quality with CAS-CNN: A Deep Convolutional Neural Network for Compression Artifact Suppression

Radha Seelaboyina

2024

jes

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Objective: The goal is to outdo clumsy filtering algorithms and prevailing deep learning approaches in terms of the image quality metrics, computational efficiency, and memory usage by attaining better performance. Methodology: Two-step approach has the methodology component including the formulation of the problem and review of the literature collections to detect known techniques. CAS-CNN's architecture is designed, and the entire experiment setup requires the preparation of an original and a compressed images dataset and also defining loss function for training and improving CAS-CNN parameters as a whole. Outputs of metrics are the PSNR, SSIM, MOS values, inference time, model size, training time, and hardware usage. Results and Discussion: In terms of competitiveness, CAS-CNN is always ahead of all the latest algorithm levels and achieves the best results by standard metrics such as PSNR and SSIM, thus it is the method that gives the most undistorted recreated images with a high quality. It has shown its ability to compete closely with traditional methods by providing better performance, as well as consuming fewer computing resources, which makes it closer to the practical reality of real time applications. The CAS-CNN's model creates efficiency that enables cutting-edge design and testing into the market timelier than the existing methods do. Small progress is seen despite its quick performance; but still, important issues like artificial data and restrictions on given conditions are noticed, which means that we must not stop searching for scaling and generalizing power. Conclusion: CAS-CNN is a next-level algorithm concerning removing distortion in compression, superior to prevailing techniques in terms of both effectiveness, efficiency, and accessibility. It holds an edge over other methods due to its high performance and efficiency, and as such, it is an applicable tool for a variety of situations where one must deal with limited resources in the need for high-quality images. More research is needed to account for the existing shortcomings and apply CAS-CNN to a range of different datasets, as well as the circumstances in the real world.

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Different Thresholding Techniques in Image Processing : A Review

Cited by 3 publications

References 8 publications

Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

Classification of a 3D Film Pattern Image Using the Optimal Height of the Histogram for Quality Inspection

Enhance Image Quality with CAS-CNN: A Deep Convolutional Neural Network for Compression Artifact Suppression

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