Otsu Threshold Segmentation Algorithm Research Articles

A research on the relationship between landslide area changes and environmental factors in the southern Tibetan plateau

Introduction: Landslides are known to be one of the most frequent types of geological disasters. However, there is not an established method for large-scale, rapid, and high-precision landslide extraction. The quantitative impact of environmental changes on landslide development is also not well understood, which hinders accurate assessments and decision-making in environmental and disaster response. The polar regions, including the Antarctic, the Arctic, and the Tibetan plateau (TP), sensitive to global environmental changes, are significantly affected by global warming. This leads to extensive landslide development, particularly in the southern TP. This research focuses on new landslides in the southern TP, exploring extraction methods and the relationship between landslides and environmental factors.Methods: Utilizing the Google Earth Engine (GEE) and an improved Otsu threshold segmentation algorithm, we processed remote sensing images with 10 m resolution to identify landslide areas. The proposed Normalized Landslide Bare-soil Separation Index (NDLBSI) achieved an 87% pre-extraction accuracy in extracting landslides from Sentinel-2 images from 2019 to 2023. For the pre-extraction results, manual interpretation and correction were carried out, and a model correlating annual landslide changes with environmental factors was established based on least squares multivariate statistical methods.Results: Results show that a significant increase in landslide areas in the southern TP over the past 5 years, correlating with the watershed-wide increase in annual average temperature and vegetation cover, along with a decrease in snow cover area.Discussion: These changes could affect soil and rock moisture, influencing soil stability and landslide occurrence. The study provides valuable insights for large-scale landslide detection and understanding the environmental factors influencing landslides, which is of some significance for landslide hazards early warning.

Tool Damage Detection Method Based on Improved Threshold Segmentation Algorithm

In order to solve the problem that the current tool wear defects are difficult to be collected by the visual inspection system, an Otsu threshold segmentation algorithm based on particle swarm optimization is proposed.to detect tool wear The algorithm improved update strategy for inertia coefficients which effectively expanding the search scope of the algorithm and shortens the running time of the algorithm. By adding a perturbation equation to the particle swarm, solved the problem of traditional particle swarm optimization algorithms easily falling into local optima. Finally, an experimental platform is built to verify the effectiveness of the algorithm. This inspection method can achieve the identification of tool damage areas and the measurement of tool damage amount, and has advantages such as high recognition accuracy and fast running speed compared to traditional Otsu algorithm, Canny algorithm ,local threshold segmentation and so on. The research results have certain reference value for the actual tool defect detection system.

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.

Research on Weighing of Concrete Aggregate Pile Based on Binocular Vision

At present, the weighing process of concrete aggregate is cumbersome, and the weighing result has a large error. This paper proposes a method for measuring the weight of irregular bone stockpiles based on binocular vision. Firstly, based on the principle of binocular vision, the improved SMG stereo matching algorithm and OTSU threshold segmentation algorithm are used to calculate the 3D point cloud coordinates corresponding to the disparity map. Then, the 3D reconstruction of the aggregate pile and the calculation method of volume and mass are proposed. The results show that the method can accurately measure the aggregate pile weight, and the measurement error is less than 6%. The method can effectively reduce the operating cost of the concrete batching station and improve the measurement efficiency.

Classification Prediction Model Based on BP Neural Network

In this paper, a method of plant image processing combined with BP neural network was proposed to predict the disease degree of brassica napus. First, the initial image preprocessing, the roots of brassica napus. were used by Otsu threshold segmentation algorithm is combined with morphological open operation, segment the rape root image with better effect, in order to reduce image size inconsistent on the effects of extracting feature parameters, can be reconstructed images after segmentation, and then obtain the root image multiple characteristic parameters, Through chart correlation analysis to select the feature parameter information, with the characteristics of the rape root disease was positively associated with the degree of disease information to refactor pixel area and the longest diameter, finally establish the BP neural network, two characteristic parameters extracted as input, root disease degree level as the output, training network and prediction and comparison. The experimental results showed that the mean square error of the classification network for brassica olerica was 0.049, and the accuracy rate of the classification results predicted by the model was about 93%. The prediction effect was good and feasible.

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640 × 480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

An improved Otsu threshold segmentation algorithm

An improved Otsu threshold segmentation algorithm

An improved Otsu threshold segmentation algorithm

Image segmentation is widely used as a fundamental step for various image processing applications. This paper focuses on improving the famous image thresholding method named Otsu's algorithm. Based on the fact that threshold acquired by Otsu's algorithm tends to be closer to the class with larger intraclass variance when the foreground and background have large intraclass variance difference, an improved strategy is proposed to adjust the threshold bias. We analysed the relationship between pixel greyscale value and the change of cumulative pixel number, and selected the ratio of pixel grey level value to a certain cumulative pixel number as the adjusted threshold. Experiments using typical testing images were set up to verify the proposed method both quantitatively and qualitatively. Two widely used metrics named misclassification error (ME) and dice similarity coefficient (DSC) were adopted for quantitative evaluation, and both quantitative and qualitative results indicated that the proposed algorithm could better segment the testing images and get competitive misclassification error and DSC values compared with Otsu's method and its improved versions proposed by Hu and Gong (2009) and Xu et al. (2011), and the time consumption of our method can be significantly reduced.

Classification of rice planthoppers based on shape descriptors

Here, classification of rice planthopper (RPH) based on shape descriptors was addressed to solve the low semantics problem of shape features in traditional RPH (mainly including the whiteback planthopper (Sogatella furcifera (Horvath)), the brown planthopper (Nilaparvata lugens (Stal)), and the small brown planthopper (Laodelphax striatellus (Fallen))) image classification research. Images of RPH were obtained from rice field by an automatic insect image acquisition device made by ourselves and insect images were divided into single images based on OTSU threshold segmentation algorithm. In terms of the images of RPH after segmentation, Fourier descriptors and Hu moments, which are from two aspects of contour curve and shape area, were extracted to describe shape features of RPH. Then, random forest (RF), an ensemble learning algorithm, was used as the classifier to distinguish RPH efficiently. The optimal number of trees and prediction variables of RF are chosen to be 150 and 4, respectively, by minimising the out-of-bag error. Experimental results show that classification accuracy of RPH based on shape descriptors reaches up to 93.93%. Therefore, it has been verified that the classification with the method presented here is accurate and semantic.

Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.

Research and implementation of visual helicopter coning angle measurement system

A visual helicopter coning angle measurement method is presented in this paper. First, a reflective target is pasted onto the helicopter rotor tip; second, a high-speed industrial camera and LED strobe light are used to obtain grayscale images of each blade’s reflective target when the rotor is rotating; Third, target grayscale images are processed using the Otsu threshold segmentation algorithm to obtain binarized images. Next, the eigenvalues of the images are obtained using Sobel operator edge detection method, so the height difference of the reflective target and the helicopter coning angle can be calculated. A prototype measurement system is designed to prove the feasibility and scientific validity of the method. Experimental results show that the system is easy to operate, and the measurement accuracy is approximately 0.5mm.

The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

Separation of cortical arteries and veins in optical neurovascular imaging

Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics, such as the changes of cerebral blood flow, perfusion or oxygen concentration in arteries and veins under different pathological and physiological conditions. Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio. In this work, we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging (LSI) and optical intrinsic signals (OIS) imaging. The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis (eLASCA) algorithm. The vascular pattern was extracted and segmented using region growing algorithm from the eLASCA-based LSI. Meanwhile, OIS images were acquired alternatively with 630 and 870 nm to obtain an oxyhemoglobin concentration map over cerebral cortex. Then the separation of arteries and veins was accomplished by Otsu threshold segmentation algorithm based on the OIS information and segmentation of LSI. Finally, the segmentation and separation performances were assessed using area overlap measure (AOM). The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases, as well as in clinical intraoperative monitoring.

A New Preprocessing Algorithm of Hand Vein Image

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.