Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

AlSaeed, Duaa; Bouridane, Ahmed; El-Zaart, Ali; Sammouda, Rachid

doi:10.1109/icites.2012.6216680

Cited by 31 publications

(19 citation statements)

References 12 publications

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“…Це просто, ефективно та без додаткових параметрів. Завдяки цим перевагам була запропонована велика кількість методів для вдосконалення оригінального методу Otsu, зокрема в роботах [9][10][11][12].…”

Section: рис 2 приклад глобальної та локальної бінаризації зображеньunclassified

Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision

Golovin

2022

Cybernetics and Computer Technologies

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Introduction. Many computer vision applications often use procedures for recognizing various shapes and estimating their dimensional characteristics. The entire pipeline of such processing consists of several stages, each of which has no clearly defined boundaries. However, it can be divided into low, medium, and high-level processes. Low-level processes only deal with primitive operations such as preprocessing to reduce noise, enhance contrast, or sharpen images. The processes of this level are characterized by the fact that there are images at the input and output. Image processing at the middle level covers tasks such as segmentation, description of objects, and their compression into a form convenient for computer processing. Middle-level processes are characterized by the presence of images only at the input, and only signs and attributes extracted from images are received at the output. High-level processing involves “understanding” a set of recognized objects and recognizing their interactions. Using the example of the developed software models for recognizing figures and estimating their characteristics, it is shown that the image processing process is reduced to transforming spatial image data into metadata, compressing the amount of information, which leads to a significant increase in the importance of data. This indicates that at the input of the middle level, the image should be as informative as possible (with high contrast, no noise, artifacts, etc.) because after the transformation of the spatial image data into metadata, no further the procedures are not able to correct the data obtained by the video sensors in the direction of improving or increasing the information content. Recognition of figures in an image can be realized quite efficiently through the use of the procedure for determining the contours of figures. To do this, you need to determine the boundaries of objects and localize them in the image, often the first step for procedures such as separating objects from the background, image segmentation, detection and recognition of various objects, etc. The purpose of the article is to study the image processing pipeline from the moment of image fixation to the recognition of a certain set of figures (for example, geometric shapes, such as a triangle, quadrilateral, etc.) in an image, the development of software models for recognizing figures in an image, determining the center of mass figures by means of computer vision. Results. We proposed and tested some variants of nonlinear estimating problem. The properties of such problems depend on value of regulating parameter. The dependence of estimation on value of parameter was studied. It was defined a range for parameter's value for which estimating problem gives adequate result for initial task. Numerical examples show how much volume of calculations reduces when using a dynamic branching tree. Conclusions. The results obtained can be used in many applications of computer vision, for example, counting objects in a scene, estimating their parameters, estimating the distance between objects in a scene, etc. Keywords: contour, segmentation, image binarization, computer vision, histogram.

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Section: рис 2 приклад глобальної та локальної бінаризації зображеньunclassified

Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision

Golovin

2022

Cybernetics and Computer Technologies

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“…In an ideal case, the gray level histogram has the symmetric distribution of two regions representing foregrounds and backgrounds, respectively, such that the threshold can be chosen at the bottom between the two regions [ 2 ]. Image segmentation is a sensitive and difficult process in computer vision and image analysis applications, where no segmentation algorithm can give the best result for any type of image [ 3 ]. Segmentation techniques tend to make understandable images after grouping objects and re-represent the image in separated segments.…”

Section: Introductionmentioning

confidence: 99%

“…In the case where the image histogram has nonsymmetric modes (skewed to the right), the original Otsu method showed some problems in the image thresholding result. In [ 3 ], the Otsu method was improved based on its original formula but using only the mean value of lognormal distribution on its objective function. However, theoretically, this improvement has some problems because the formula of the original Otsu is based on the Gaussian, and in [ 3 ], the authors used the mean of lognormal instead of the mean of Gaussian.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

Jumiawi¹,

El-Zaart²

2022

Entropy

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There are various distributions of image histograms where regions form symmetrically or asymmetrically based on the frequency of the intensity levels inside the image. In pure image processing, the process of optimal thresholding tends to accurately separate each region in the image histogram to obtain the segmented image. Otsu’s method is the most used technique in image segmentation. Otsu algorithm performs automatic image thresholding and returns the optimal threshold by maximizing between-class variance using the sum of Gaussian distribution for the intensity level in the histogram. There are various types of images where an intensity level has right-skewed histograms and does not fit with the between-class variance of the original Otsu algorithm. In this paper, we proposed an improvement of the between-class variance based on lognormal distribution, using the mean and the variance of the lognormal. The proposed model aims to handle the drawbacks of asymmetric distribution, especially for images with right-skewed intensity levels. Several images were tested for segmentation in the proposed model in parallel with the original Otsu method and the relevant work, including simulated images and Medical Resonance Imaging (MRI) of brain tumors. Two types of evaluation measures were used in this work based on unsupervised and supervised metrics. The proposed model showed superior results, and the segmented images indicated better threshold estimation against the original Otsu method and the related improvement.

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“…Therefore, the K-means method can not achieve an excellent result. D. H. AlSaeed [4] has improved Otsu's method. However, these traditional algorithms only consider the gray level information between image pixels.…”

Section: Introductionmentioning

confidence: 99%

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Lu¹,

Chen

et al. 2021

Opt Quant Electron

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Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learningbased methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multiscale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

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Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models

Cited by 31 publications

References 12 publications

Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision

Recognition of Geometric Figures and Determination of Their Characteristics by Means of Computer Vision

Improvement in the Between-Class Variance Based on Lognormal Distribution for Accurate Image Segmentation

MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

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