Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

Application of Depth Learning Algorithm in Automatic Processing and Analysis of Sports Images

With the rapid development of sports,the number of sports images has increased dramatically.Intelligent and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry.In this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various environments.At the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put forward.The mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.

Application of PCA Numalgorithm in Remote Sensing Image Processing

A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

An Algorithm for Ship Wake Detection from the SAR Images Using the Radon Transform and Morphological Image Processing

Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.

Automatic Determination of Yarn Hairiness Length Based on Image Processing and Analysis Algorithm

A new algorithm is proposed to determine the actual length and the number of the protruding fibres of yarn based on a combination of image acquisition technology. First, a yarn hairiness image is obtained by the series of image processing procedures that include grayscale transformation,skew correction,yarn binary image acquisition and yarn core binary image obtaining. Then,the hairiness is realized in single pixel width by the usage of thinning algorithm. Finally, a baseline of yarn core margin is obtained,and pixels that match 8-neighbor template correctly are found by row scanning in a certain area. From this,these pixels are judged and the real crossover points of yarn core margin and hairiness,i. e.,the starting points of hairiness,are gained. The real length of the protruding fibres is calculated by tracking hairiness from the starting point constantly.

A NEURAL NETWORK HARDWARE MODEL FOR IMAGE THINNING

A new parallel thinning algorithm was proposed.This paper suggests a set of 5x5cloning templates of cellular neural network for image thinning.The principle of the templatesdesign and hardware model are proposed in the paper.Although the connected weights betweenthe neurons are asymmetric,it is shown that the network is stable,and it can be easily realized.

Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform

An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.

Image Processing Technology of Flaws within Infrared Transmitting Glasses

We investigated image processing algorithms of the original infrared glass flaw image. Using the Laplacian edge enhancement following LSD （Line Segment Detector） algorithm, we can get a good flaw image very consistent with the original one. This study is very helpful to further enhance the infrared glass flaw inspection technique.

Image boundary extraction based on island model genetic algorithms for integrated circuit defect detection

The integrated circuit chip with high performance has a high sensitivity to the defects in manufacturing environments.When there are defects on a wafer,the defects may lead to the degradation of chip performance.It is necessary to design effective detection approaches for the defects in order to ensure the reliability of wafer.In this paper,a new method based on image boundary extraction is presented for the detection of defects on a wafer.The method uses island model genetic algorithms to perform the segmentation of wafer images,and gets the optimal threshold values.The island model genetic algorithm uses two distinct subpopulations,it is a coarse grain parallel model.The individuals migration can occur between the two subpopulations to share genetic materials.A lot of experimental results show that the defect detection method proposed in this paper can obtain the features of defects effectively.

Parallel Image Processing Technology of Surface Detection System

To improve image processing speed and detection precision of a surface detection system on a strip surface,based on the analysis of the characteristics of image data and image processing in detection system on the strip surface,the design of parallel image processing system and the methods of algorithm implementation have been studied. By using field programmable gate array(FPGA) as hardware platform of implementation and considering the characteristic of detection system on the strip surface,a parallel image processing system implemented by using multi IP kernel is designed. According to different computing tasks and the load balancing capability of parallel processing system,the system could set different calculating numbers of nodes to meet the system's demand and save the hardware cost.

A RBF classification method of remote sensing image based on genetic algorithm

The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.

THE APPLICATION OF MULTILAYER FEEDFORWARD NETWORK FOR IMAGE SEGMENTATION

The multilayer feedforward network is used for image segmentation. This paper deals with the procedure of achieving the learning patterns and the method of improving the learning rate. The experiment shows that the image segmentation can get better result from using the multilayer feedforward network.

Image processing of weld pool and keyhole in Nd：YAG laser welding based on edge predicting

Laser welding is one of high efficiency, high energy density welding methods. Quality control should be applied to ensure good welding quality. Weld pool and keyhole contains abundant information of welding quality. Good image processing algorithm is necessary in quality control system based on visual sensing. Aiming at the image captured by a coaxial visual sensing system for laser welding, an image processing algorithm is designed. An edge predicting method is proposed in image processing algorithm which is based on the fact that the local shape of weld pool can be fitted to a circle. The results show that the algorithm works well. It lays solid foundation for further quality control in laser welding.

The Watershed Algorithm for Image Segmentation

This article introduced the watershed algorithm for the segmentation, illustrated the segmation process by implementing this algo-rithm. By comparing with another three related algorithm, this article revealed both the advantages and drawbacks of the watershed algorithm.

基于ImageJ的图像处理算法设计研究

提出一种基于ImageJ软件的数字图像处理算法设计方案，该方案能够快速和准确地进行数字图像处理算法设计，是基于MATLAB或者C／C＋＋语言的有益补充。通过数字滤波和小波变换两个实例，展示了该方案在图像处理算法设计方面的优点。同时，由于ImageJ软件具有的开放式的插件架构体系，使得这种构造性的设计方案具备较好的模块性和可扩充性。

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.