Two-dimensional Otsu Algorithm Research Articles

Otsu Image Segmentation Based on a Fractional Order Moth–Flame Optimization Algorithm

To solve the shortcomings of the Otsu image segmentation algorithm based on traditional Moth–Flame Optimization (MFO), such as its poor segmentation accuracy, slow convergence, and tendency to fall into local optimum, this paper proposes fractional order moth–flame optimization with the Otsu image segmentation algorithm. Utilizing the advantages of memorability and heritability in fractional order differentiation, the position updating of moths is controlled by fractional order. Using the adaptive fractional order, the positions of moths are used to adjust the fractional order adaptively to improve the convergence speed. Combining the improved MFO algorithm with the two-dimensional Otsu algorithm, the optimization objective function is achieved by using its dispersion matrix. The experimental results indicate that, compared with traditional MFO, the convergence rate of the proposed algorithm is improved by about 74.62%. Furthermore, it has better segmentation accuracy and a higher fitness value than traditional MFO.

A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

A New Method for Calculating Reservoir Core-Bound Water Saturation Using the Cast Thin Section

The rock coring of the reservoir in the Bohai A field is difficult. The cores of the target section in the study area are loose, making it difficult to accurately measure the core-bound water saturation. The purpose of this research was to develop and validate a method for calculating a reservoir core-bound water saturation ratio using the cast thin section. First, pepper noise denoising and image enhancement were performed on the thin section by median filtering and gamma variation. Based on this, the enhanced sheet images were thresholded for segmentation by the two-dimensional OTSU algorithm, which automatically picked up the thin section pore-specific parameters. Then, the thin section image was equivalent to a capillary cross-section, while the thin film water fused to the pore surface was observed as bound water. For hydrophilic rocks with a strong homogeneity, the area of thin film water in the pore space of the sheet was divided by the total area of the pore space, which produced the bound water saturation. Next, the theoretical relationship between the film water thickness and the critical pore throat radius was derived based on the Young–Laplace equation. The bound water saturation of the rock was calculated by combining the pore perimeter and the area that was automatically picked up from the thin film for a given critical pore throat radius of the rock. Finally, 22 images of thin sections of sparse sandstone from the coring well section of the study area were image processed using the new method proposed in this paper, and the bound water saturation was calculated. The calculated results were compared with 22 NMR-bound water saturations and 11 semi-permeable baffle plate-bound water saturations in the same layer section. The results showed that the bound water saturation values calculated by the three methods produced consistent trends with absolute errors within 5%. The calculated results confirm the reliability of the method proposed in this paper. This method can effectively avoid the problem of the inaccurate results of core experiments due to the easy damage of sparse sandstone and provides a new idea for the accurate determination of the bound water saturation of sparse sandstone.

Conveyor belt tear detection method based on lion group optimization two-dimensional Otsu algorithm

Conveyor belt tear detection method based on lion group optimization two-dimensional Otsu algorithm

Modified and Optimized Method for Segmenting Pulmonary Parenchyma in CT Lung Images, Based on Fractional Calculus and Natural Selection

Abstract Computer-aided diagnosis of lung segmentation is the fundamental requirement to diagnose lung diseases. In this paper, a two-dimensional (2D) Otsu algorithm by Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) is proposed to segment the pulmonary parenchyma from the lung image obtained through computed tomography (CT) scans. The proposed method extracts pulmonary parenchyma from multi-sliced CT. This is a preprocessing step to identify pulmonary diseases such as emphysema, tumor, and lung cancer. Image segmentation plays a significant role in automated pulmonary disease diagnosis. In traditional 2D Otsu, exhaustive search plays an important role in image segmentation. However, the main disadvantage of the 2D Otsu method is its complex computation and processing time. In this paper, the 2D Otsu method optimized by DPSO and FODPSO is developed to reduce complex computations and time. The efficient segmentation is very important in object classification and detection. The particle swarm optimization (PSO) method is widely used to speed up the computation and maintain the same efficiency. In the proposed algorithm, the limitation of PSO of getting trapped in local optimum solutions is overcome. The segmentation technique is assessed and equated with the traditional 2D Otsu method. The test results demonstrate that the proposed strategy gives better results. The algorithm is tested on the Lung Image Database Consortium image collections.

Image Segmentation Research Based on GA and Improved Otsu Algorithm

<p>In the face of the problem of high complexity of two-dimensional Otsu adaptive threshold algorithm, a new fast and effective Otsu image segmentation algorithm is proposed based on genetic algorithm. This algorithm replaces the segmentation threshold of the traditional two - dimensional Otsu method by finding the threshold of two one-dimensional Otsu method, it reduces the computational complexity of the partition from O (L4) to O (L). In order to ensure the integrity of the segmented object, the algorithm introduces the concept of small dispersion in class, and the automatic optimization of parameters are achieved by genetic algorithm. Theoretical analysis and experimental results show that the algorithm is not only better than the original two-dimensional Otsu algorithm, but also it has better segmentation effect.</p>

Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.

Evolutionary Game Algorithm for Image Segmentation

The traditional two-dimensional Otsu algorithm only considers the limitations of the maximum variance of between-cluster variance of the target class and background class; this paper proposes evolutionary game improved algorithm. Algorithm takes full consideration of own pixel cohesion of target and background. It can meet the same of maximum variance of between-cluster variance. To ensure minimum threshold discriminant function within the variance, this kind of evolutionary game algorithm searching space for optimal solution is applied. Experimental results show that the method proposed in this paper makes the detail of segmentation image syllabify and has better antijamming capability; the improved genetic algorithm which used searching optimal solution has faster convergence speed and better global search capability.

Tree Image Segmentation Based on an Improved Two-Dimensional Otsu Algorithm

Influenced by light, houses, street lamps and other factors, tree images often contain noise and complex background information. The existing Otsu segmentation algorithms have deficiencies such as poor anti-noise capability, ignoring the class cohesion and so on. Based on the traditional gray value-neighborhood average gradient Otsu segmentation method, we propose an improved two-dimensional Otsu algorithm for tree image segmentation. The algorithm takes into account the between-class distance and within-class distance, which combined the average variance concept of two categories and proposed new threshold selection method, and reduce the interference of noise effectively. To achieve the best segmentation and reduce over-segmentation of background information, a method of removing small areas and morphological processing are used to optimize segmentation results. Experimental results show that the proposed algorithm has a good inhibition effect on noise and the effect of tree image segmentation is better than that of the traditional one.

Underwater Man-Made Object Recognition on the Basis of Color and Shape Features

ABSTRACT Hou, G.-J.; Luan, X.; Song, D.-L., and Ma, X.-Y., 2016. Underwater mad-made object recognition on the basis of color and shape features. In complex underwater situations, how to realize object extraction accurately and effectively is the key technology of underwater object recognition. In this paper, the detection and recognition techniques of underwater man-made objects on the basis of color and shape features have been studied in depth. First, the objects of interest in an underwater image are extracted by applying a color-based algorithm. Then an improved two-dimensional Otsu algorithm is utilized for removing the background color noise. To recognize the shape type of a regular object, a robust algorithm based on shape signature is presented. The experimental results show that the proposed approach is effective and robust, such as an acceptable extraction rate (exceeding 80%) of the object of interest, an ideal outcome of background color noise removal, high accurate shape of the object's edge...

Image denoising and enhancement based on adaptive fractional calculus of small probability strategy

This paper presents two methods to deal with the problem that traditional image denoising algorithms may easily neglect image texture details. The first one is global adaptive fractional integral algorithm (GAFIA) which deals with common noises. It selects the optimal integral order of each pixel based on the local average gradient. The second is image denoising and enhancement algorithm based on adaptive fractional calculus of small probability strategy (AFC-SPS) which deals with salt & pepper noise. It regards the appearance of noise points as small probability events, divides them, and segments the image edges and weak textures by the improved two-dimensional Otsu algorithm. Then, the function of adaptive fractional order is constructed. Experimental results show that, both of the methods have good image denoising effect, and the AFC-SPS algorithm has a better effect than other methods in enhancing the edge and preserving the texture.

Research on Aluminum-Plastic Blister Drug Image Segmentation Method Based on Improved Otsu Theory

In the digital image processing, Otsu algorithm uses the criterion of maximum between-cluster to make image segmentation. In this paper, combined with drug defect detection requirements, the new threshold output functions is put forward which studies on the existing two-dimensional Otsu algorithm in a deep way from the computing complexity and integral effect. The improved algorithm improves the computing speed of the algorithm and optimizes the segmentation effect which is a good segmentation algorithm. The effectiveness of the proposed algorithm has been proved by relevant experiments, and the medicine image segmentation result show that the improved algorithm has a good application prospect in drug defect detection.

Edge Information Detection of Remote Sensing Image Based on Two-dimensional Otsu Algorithm

Because of the problem that the conventional edge detection operator is di‐cult to efiectively extract high{resolution remote-sensing image edge information, a high-resolution remote sensing image edge information extraction method based on two-dimensional Otsu algorithm is proposed. Firstly, used median fllter algorithm to carry out denoising for original image, and used contrast enhancement method to improve the divisibility among ground objects of remote sensing image; secondly, used two-dimensional Otsu algorithm to carry out threshold segmentation for flltered image, obtained the binary image; flnally, used a new adaptive Canny operator to detected the edge information for binary image, obtained edge information. In order to verify the feasibility and reliability of the proposed method, we selected two groups of Quickbird images for experiment. The experiment results show that compared with traditional Canny operator, edge information detection accuracy of the proposed method is higher.