Clustering-driven watershed adaptive segmentation of bubble image

In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy c means(FCM) algorithm. Through the attributes of size and shape pattern spectrum,the optimal morphological structuring element was determined.According to the optimal parameters,some image noises were removed with an improved area opening and closing by reconstruction operation,which consist of image regional markers,and the bubbles were finely separated from each other by watershed transform.The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum,and the froth image is segmented accurately.Compared with other froth image segmentation method,the proposed method achieves much high accuracy,based on which,the bubble size and shape features are extracted effectively.

Adaptive segmentation based on multi-classification model for dermoscopy images

Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method has its strengths and weaknesses, a novel adaptive segmentation framework based on multi-classification model is proposed for dermoscopy images. Firstly, five patterns of images are summarized according to the factors influencing segmentation. Then the matching relation is established between each image pattern and its optimal segmentationmethod. Next, the given image is classified into one of the five patterns by the multi-classification model based on BP neural network. Finaily, the optimal segmentation method for this image is selected according to the matching relation, and then the image is effectively segmented. Experiments show that the proposed method delivers better accuracy and more robust segmentation results compared with the other seven state-of-the-art methods.

An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving.This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively.Using 2D region proposals in an RGB image,this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network(FPS-Net)and feature extraction network(FE-Net).Subsequently,the encoder-decoder network(ED-Net)implements 3D-oriented bounding box(OBB)regression.Meanwhile,the adaptive least square regression(ALSR)method is proposed to split 3D OBB.Finally,the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem(SST).In the experiments of KITTI benchmark,our proposed 3D object detector outperforms other state-of-theartmethods.Meanwhile,collision detection algorithm achieves the satisfactory performance of 91.8%accuracy on our SHTA dataset.

Line-element based nonlinear adaptive piecewise compensating correction for LVDT sensors

In order to solve the linear variable differential transformer （LVDT） displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for piecewise compensating correction was proposed. According to the mechanical structure of LVDT, the output equation was calculated, and then the theoretic nonlinear source of output was analyzed. By the proposed line-element adaptive segmentation method, the nonlinear output of LVDT was divided into linear and nonlinear regions with a given threshold. Then the com- pensating correction function was designed for nonlinear parts employing polynomial regression tech- nique. The simulation of LVDT validates the feasibility of proposed scheme, and the results of cali- bration and testing experiments fully prove that the proposed method has higher accuracy than the state-of-art correction algorithms.

Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Error analysis of the piston estimation method in dispersed fringe sensor

Dispersed fringe sensor （DFS） is an important phasing sensor of next-generation optical astronomical telescopes. The measurement errors induced by the measurement noise of three piston estimation methods for the DFS including leastsquared fitting （LSF） method, frequency peak location （FPL） method and main peak position （MPP） method, are analyzed theoretically and validated experimentally in this paper. The experimental results coincide well with the theoretical analyses. The MPP, FPL, LSF are used respectively when the DFS operates with broadband light （central wavelength： 706 nm, bandwidth： 23 nm）. The corresponding root mean square （RMS） value of estimated piston error can be achieved to be 1 nm, 3 nm, 26 nm, respectively. Additionally, the range of DFS with the FPL can be more than 100 μm at the same time. The FPL method can work well both in coarse and fine phasing stages with acceptable accuracy, compared with LSF method and MPP method.

Ground-layer adaptive optics for the New Vacuum Solar Telescope:Instrument description and first results

Ground-layer adaptive optics(GLAO)has shown its potential for use in solar observation owing to its wide field-of-view(FOV)correction.A high-order GLAO system that consists of a multiple direction Shack-Hartmann wavefront sensor(WFS),a realtime controller with a multi-CPU processor,and a 151-element deformable mirror was developed for the 1-m New Vacuum Solar Telescope at Yunnan Observatories,Chinese Academy of Sciences.A hexagonal microlens with 9×8 subapertures is employed in the WFS.The detection FOV is 42′′×37′′,in which 9(3×3)guide regions are extracted for multiple direction wavefront sensing with a frame rate of up to 2200 Hz.To our knowledge,this is the first professional solar GLAO system used as a regularly operating instrument for scientific observations.Its installation and adjustment were performed in the summer of 2021.In this article,a detailed account of the GLAO system and its first light results and a comprehensive analysis of the performance of the GLAO system are provided.The results show that this system can effectively improve the imaging quality after compensating for the wavefront aberration due to ground-layer turbulence.

Multifeature Statistical TV Tracker

A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.

Performance modeling of the adaptive optics system on the 2.16 m telescope

The adaptive optics system on the 2.16 m telescope is modeled and simulated using an ends-to-ends Monte Carlo method in this paper.Under the optimized parameters,the Strehl ratio can be increased from about 0.02 to more than 0.2 in the J,H and K bands with a classical adaptive optics system.Based on the simulation results,the practical system design is also considered and discussed.

A dynamic holographic modelling method of digital twin scenes for bridge construction

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene.However,pre-rendering methods in the existing research work are usually used to implement holographic visualization,which is static display.The above-mentioned methods for static display have many shortcomings,such as poor adaptability,low rendering efficiency and lack of real-time.A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction.Firstly,a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes.Secondly,a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics.Finally,a prototype system was developed,and the corresponding experimental analysis was completed.The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction.The amount of scene data can be reduced to more than 30%,which significantly improves rendering efficiency and reduces glare.