Kinds Of Image Processing Research Articles

Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.

In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation.

A Study of anImproved Edge Detection Algorithm for MRI Brain Tumor Images Based on Image Quality Parameters

Biomedical imaging with emerging technology plays a vital role in medical diagnosis and treatment. MRI is one such biomedical imaging technology. The incidence of brain tumor in increasing all over the world. Accurate detection of size and location of brain tumor helps in right diagnosis and treatment. In any kind of image processing, image segmentation is a fundamental step. Edge detection is an important technique in the analysis of images, which plays a crucial role in detecting the contour of brain tumor. In this paper, an improved algorithm called the Luminance edge detection algorithm is proposed and compared with the existing edge detection algorithms like Prewitt, Sobel, and Canny for the MRI images of the brain. PSNR and SSIM were calculated to compare the image quality of different edge detection techniques. Results revealed that the PSNR and SSIM values were slightly higher for the images obtained by the Luminance edge detection algorithm when compared to the images obtained by Prewitt, Sobel, and Canny edge detection algorithm. It proves that the proposed algorithm is better than the other edge detection algorithms in producing a quality image of the MRI brain. Hence it can be concluded that the proposed algorithm was better in detecting the contour of brain and brain tumor than the other edge detection techniques.

Image registration by using multi-variate spline functions

Image registration is a kind of image processing that merges a series of pictures into a global picture. Any picture of this series has a common part with another one and they may be produced in different time, by different equipments, and under different factors such as weather, illumination angle etc. Based on multivariate splines defined on arbitrary triangulations, in this paper a new image registration method is presented. Experiment results show that, comparing with other methods, our results have better merging texture with simpler and easier calculation.

From image processing to computer vision: plant imaging grows up.

Image analysis is a field of research which, combined with novel methods of capturing images, can help to bridge the genotype-phenotype gap, where our understanding of the genotype has until now been leaps and bounds ahead of our ability to work with the phenotype. Methods of automating image capture in plant science research have increased in usage recently, as has the need to provide objective and highly accurate measures on large image datasets, thereby bringing the phenotype back to the centre of interest. In this special issue of Functional Plant Biology, we present some recent advances in the field of image analysis, and look at examples of different kinds of image processing and computer vision, which is occurring with increasing frequency in the plant sciences.

Performance evaluation of simple linear iterative clustering algorithm on medical image processing.

Simple Linear Iterative Clustering (SLIC) algorithm is increasingly applied to different kinds of image processing because of its excellent perceptually meaningful characteristics. In order to better meet the needs of medical image processing and provide technical reference for SLIC on the application of medical image segmentation, two indicators of boundary accuracy and superpixel uniformity are introduced with other indicators to systematically analyze the performance of SLIC algorithm, compared with Normalized cuts and Turbopixels algorithm. The extensive experimental results show that SLIC is faster and less sensitive to the image type and the setting superpixel number than other similar algorithms such as Turbopixels and Normalized cuts algorithms. And it also has a great benefit to the boundary recall, the robustness of fuzzy boundary, the setting superpixel size and the segmentation performance on medical image segmentation.

A gesture recognition system with retina-V1 model and one-pass dynamic programming

Dynamic Programming (DP) algorithm has been studied from 1940s and successfully applied to pattern recognition fields such as continuous speech recognition, hand writing recognition, gesture recognition and so on. In this paper, we propose a novel hand gesture recognition system which includes three kinds of image processing: skin area segmentation, motion estimation by a retina-V1 model, and a gesture discrimination algorithm of One-Pass Dynamic Programming (One-Pass DP). A HSV-RGB filter is used to extract skin area in the color image, and the simple motion of hand area is estimated in eight directions by a retina-V1 model which is a computational model of primary visual cortex. Then the motions are used to compose 40 basic templates of gestures. In other words, hand gestures are considered as combinations of templates of simple motions, and One-Pass DP is used to recognize the pattern of gestures. Experiments dealt with individual and compound gestures were executed by online processing, and the results confirmed the effectiveness of the proposed system.

Bayesian-Optimal Image Reconstruction for Translational-Symmetric Filters

Translational-symmetric filters provide a foundation for various kinds of image processing. When a filtered image containing noise is observed, the original one can be reconstructed by Bayesian inference. Furthermore, hyperparameters such as the smoothness of the image and the noise level in the communication channel through which the image observed can be estimated from the observed image by setting a criterion of maximizing marginalized likelihood. In this article we apply a diagonalization technique with the Fourier transform to this image reconstruction problem. This diagonalization not only reduces computational costs but also facilitates theoretical analyses of the estimation and reconstruction performances. We take as an example the Mexican-hat shaped neural cell receptive field seen in the early visual systems of animals, and we compare the reconstruction performances obtained under various hyperparameter and filter parameter conditions with each other and with the corresponding performances obtai...

2A1-E09 路面及びランドマークの認識による移動ロボットの行動決定(移動ロボットのための視覚)

Creatures usually perceive their environment using their nerves and decide their motions. For example they detect obstacles to avoid them, and detect landmarks to recognize their positions. As regards the visual information, the usage occupies one of the most important information for creatures, and also important for robots. In this research we use visual information for operating autonomous mobile robot. Two different kinds of image processing are executed. One is road detection for recognizing the places that robot can pass. The other is landmark detection for knowing the position on the desired route. We experimented using these method for robot control.

数字全息和小波变换在数字博物馆版权保护中的应用

A new method to protect the copyright of digital museum based on digital holography is proposed. The Fresnel hologram of watermark image is embedded in the object to be protected through discrete wavelet transform (DWT). After the watermark detection, the copyright information appears in the reconstructed hologram. With the higher redundancy feature in the hologram, the proposed technique can actually survive several kinds of image processing. Experimental results prove that the presented method has good robustness in image protection.

Combining fuzzy theory and a genetic algorithm for satellite image edge detection

Edge detection of an image by remote sensing is an important tool for the detection of oil spilled on sea. There are many algorithms for edge detection by image processing. Each algorithm has its own advantages and disadvantages for different images. In this paper, the author makes some improvements to the Pal–King fuzzy edge detection algorithm and proposes an algorithm combining improved fuzzy theory and a genetic algorithm for the detection of oil spilled on the sea by remote sensing. The Pal–King fuzzy detection algorithm has a good capability for the detection of fuzzy and thin edges. However the complex and large calculation and the fixed threshold value do not suit some kinds of image processing. The algorithm presented in this paper is composed of two parts: an improved fuzzy enhancement algorithm, which simplifies the complex G and G −1 calculation in the Pal–King algorithm; a genetic algorithm with which we are able to obtain the threshold value precisely and quickly for image processing. Finally the paper gives the results of image processing using the algorithm mentioned above and the Pal–King algorithm. Through comparison, we can conclude that the processing results obtained using the algorithm suggested in this paper are more legible than by the Pal–King algorithm.

A fractal concentration–area method for assigning a color palette for image representation

Displaying the remotely sensed image with a proper color palette is the first task in any kind of image processing and pattern recognition in GIS and image processing environments. The purpose of displaying the image should be not only to provide a visual representation of the variance of the image, although this has been the primary objective of most conventional methods, but also the color palette should reflect real-world features on the ground which must be the primary objective of employing remotely sensed data. Although most conventional methods focus only on the first purpose of image representation, the concentration–area ( C– A plot) fractal method proposed in this paper aims to meet both purposes on the basis of pixel values and pixel value frequency distribution as well as spatial and geometrical properties of image patterns. The C– A method can be used to establish power-law relationships between the area A(⩾ s) with the pixel values greater than s and the pixel value s itself after plotting these values on log–log paper. A number of straight-line segments can be manually or automatically fitted to the points on the log–log paper, each representing a power-law relationship between the area A and the cutoff pixel value for s in a particular range. These straight-line segments can yield a group of cutoff values on the basis of which the image can be classified into discrete classes or zones. These zones usually correspond to the real-world features on the ground. A Windows program has been prepared in ActiveX format for implementing the C– A method and integrating it into other GIS and image processing systems. A case study of Landsat TM band 5 has been used to demonstrate the application of the method and the flexibility of the computer program.

Image Processing and Scientific Misconduct

Abstract In a recent microscopy discussion group, a correspondent questioned the use of a contrast enhancement method applied to a microscopy image for publication. What kinds of image processing, folk asked, are “acceptable” in a general sense? What kinds of image processing should be noted in the text or caption and what kind of images should be archived? It is an interesting set of questions, and I face them wearing three hats, I am a forensic pathologist who performs forensic image interpretation in the investigation of homicide and assault, an anatomic pathologist with an interest in confocal microscopy, and a computer scientist with training in image processing and computer vision. Image processing is part and parcel of my everyday work. I use tools such as contrast enhancement, debarring, and photogrammetry for image interpretation. I build tools for visualization in my confocal work. I collaborate in the design of data acquisition devices for the evaluation of crime scenes and bodily injury.

Wavelet Transformations in Signal Detection

In the analysis of transient signals such as those encountered in speech, or in certain kinds of image processing, standard Fourier analysis is often not satisfactory. This is because the basis functions of Fourier analysis (sines, cosines, complex exponentials) extend over infinite time whereas the signals to be analysed are short-time transientsA new method for dealing with transient signals has recently appeared in the literature [1-6]. The basis functions are referred to as wavelets, and they employ time compression (or dilation) rather than a variation of frequency of the modulated sinusoid. Hence all the wavelets have the same number of cycles . The analyzing wavelets must satisfy a few simple conditions, but are not otherwise specified. There is therefore a wide latitude in the choice of these functions and they can be taylored to specific applications. The wavelets are well founded on rigorous mathematical theory, and the expansions are robust. We have applied them to detect ventricular delayed potentials (VLP) in the electrocardiogram