An improved Otsu method for threshold segmentation based on set mapping and trapezoid region intercept histogram

Ouyang, Haibin; Fan, Chaodong

doi:10.1016/j.ijleo.2019.163106

Cited by 33 publications

(10 citation statements)

References 13 publications

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“…Otsu (1979) is a segmentation method utilized for finding an optimum threshold value of the images depending on increasing the between class variances. These approaches are utilized for finding the threshold optimal value which separates the images into several classes [18]. These methods identify L v intensity level of grey images and the likelihood distribution can be evaluated using Eq.…”

Section: Otsu Based Segmentationmentioning

confidence: 99%

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Abukhalil¹,

Alqaralleh²,

Alomari³

2022

Computers, Materials &Amp; Continua

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Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

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Section: Otsu Based Segmentationmentioning

confidence: 99%

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Abukhalil¹,

Alqaralleh²,

Alomari³

2022

Computers, Materials &Amp; Continua

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“…In [12], 2D histogram projection along with a fast scheme, based on the wavelet transform, for searching the extrema of the projected histogram, was proposed. In [13], a modified 2D Otsu mapped the 2D histogram pixels onto different trapezoid regions in order to narrow the threshold range. In [14], the median and average filters were used in order to smooth the image, which was used in the next step to build the 2D histogram, while the optimal threshold value was selected by performing two 1D searches on the 2D histogram.…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation Methods for Subpicture Partitioning in the VVC Video Encoder

Panagou¹,

Belememis²,

Koziri³

2022

Electronics

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The emergence of the new generation video coding standard, Versatile Video Coding (VVC), has brought along novel features rendering the new standard more efficient and flexible than its predecessors. Aside from efficient compression of 8 k or higher camera-captured content, VVC also supports a wide range of applications, including computer-generated content, high dynamic range (HDR) content, multilayer and multi-view coding, video region extraction, as well as 360° video. One of the newly introduced coding tools in VVC, offering extraction and independent coding of rectangular sub-areas within a frame, is called Subpicture. In this work, we turn our attention to frame partitioning using Subpictures in VVC, and more particularly, a content-aware partitioning is considered. To achieve that, we make use of image segmentation algorithms and properly modify them to operate on a per Coding Tree Unit (CTU) basis in order to render them compliant with the standard’s restrictions. Additionally, since subpicture boundaries need to comply with slice boundaries, we propose two methods for properly partitioning a frame using tiles/slices aiming to avoid over-partitioning of a frame. The proposed algorithms are evaluated regarding both compression efficiency and image segmentation effectiveness. Our evaluation results indicate that the proposed partitioning schemes have a negligible impact on compression efficiency and video quality

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“…23,24,26 The best threshold should be the best separation between the two classes, and the best judgment of the separation between classes is based on the maximum of the variance between classes or the minimum of the variance within classes in the sense of mathematical statistics. 26 Therefore, in the selection of threshold value of traditional Canny algorithm, Otsu algorithm can be combined with gradient amplitude to get the high and low thresholds in Canny algorithm. 27,28 If the total number of pixels in the image with gray level i is n i and the range of gray level is [0, L-1], then the total number of pixels is:…”

Section: Adaptive Threshold Calculation Based On Otsumentioning

confidence: 99%

“…18 The basic idea is to divide image pixels into background and target, and get the optimal threshold by searching and calculating the maximum variance between classes. 23,24,26 The best threshold should be the best separation between the two classes, and the best judgment of the separation between classes is based on the maximum of the variance between classes or the minimum of the variance within classes in the sense of mathematical statistics. 26 Therefore, in the selection of threshold value of traditional Canny algorithm, Otsu algorithm can be combined with gradient amplitude to get the high and low thresholds in Canny algorithm.…”

Section: Fuzzy-canny Edge Extraction Algorithmmentioning

confidence: 99%

Lane line recognition based on improved 2D-gamma function and variable threshold Canny algorithm under complex environment

Zhang

Liang

Jiang

et al. 2020

Measurement and Control

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The visual guidance of AGV (automated guided vehicle) has gradually become one of the most important perception methods. Aiming at the problem that it is difficult to extract lane line accurately when AGV is running in complex working environment (such as uneven illumination, overexposure, lane line is not obvious, etc.), a scheme of lane line recognition under complex environment is proposed. Firstly, the variable scale image correction is carried out for the uneven illumination area in ROI (region of interest), and the threshold of Canny algorithm is adjusted adaptively according to the luminance of ROI region by Fuzzy-Canny algorithm; Secondly, the edge points matching the lane width feature are extracted by the way of aerial view. Finally, the curve fitting method based on RANSAC (Random Sample Consensus) is used to fit a curve with the lowest error rate and then get the lane center curve. The experimental results show that the processing algorithm used in this paper is feasible and effective, has strong robustness and fast computing performance, and can meet the requirements of intelligent AGV in various complex environments.

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An improved Otsu method for threshold segmentation based on set mapping and trapezoid region intercept histogram

Cited by 33 publications

References 13 publications

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Image Segmentation Methods for Subpicture Partitioning in the VVC Video Encoder

Lane line recognition based on improved 2D-gamma function and variable threshold Canny algorithm under complex environment

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