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Computer vision is nowadays is one of the promising & evolving areas in information technology. Image processing is increasingly used in several applications such as automotive, medical or aerospace. Every computer vision application involves capturing several images with the help of camera. Image acquisition, image transmission & image security are the three important aspects of image processing system. This paper includes development of complete image processing system including improved median filtering with better PSNR & operating frequency, Image compression module with better PSNR & compression ratio, Image Encryption module using Advanced Encryption Standard.

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose the Efficient Attention Pyramid Transformer (EAPT) in this paper. More specifically, we first propose Deformable Attention, which learns an offset for each position in patches. Therefore, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information between all patches to get more complete global attention information. Finally, we also proposed a position encoding specifically for vision transformers, which can be used for patches of any dimensions and any lengths. Extensive experiments on the vision tasks of image classification, object detection, semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.

Recently, extraction of text or characters from images has become a wide field of research in the field of image analysis, neural networks, artificial intelligence and machine vision. The aim of character recognition is to extract and recognize the text from images and can be thus used to analyze contents, documents, or retrieve certain information etc. Image analysis has various processes and algorithms available for text extraction like edge detection, point detection, corner detection etc. for extracting the text from images. Many steps are involved in text recognition including pre-processing, segmentation, feature extraction, and classification. This paper addresses FAST algorithm for corner detection applied on the images for text recognition. The FAST algorithm detects and determines the presence of a corner by testing a small area around the potential center of the corner with the help of intensity value. This approach of text recognition can be efficiently used even with images with noise content or blurs.

In robot visual servoing system, effective detected the region of interest area (ROI) in target images is the first problem should be solved, but the detection is susceptible to an unstructured environment. In this paper, an instance segmentation network algorithm based on Mask Region Convolutional Neural Networks (Mask R-CNN) framework is proposed for ROI image preprocessing. As instance segmentation technology can distinguish complex environmental information, so first with this advantage filter out message such as shape or similar area to target image, then with semantic segmentation technology add special category labels to the filtered images and distinguish different individual instances in similar categories. Finally through the series steps aforementioned, robot vision system can overcome the impact of environmental factors and identify the target image. The proposed method is used to detect target image under five constraints such as occlusion and reflection, result shows the algorithm can effectively deal with challenges that brought by complex constraints, and even can predict the location data of missing information based on some image information. In addition, based on the algorithm proposed in this paper, we used one seven axis robot visual servo platform, executed visual servoing experiment under different unstructured environments, further verifies the effectiveness of our proposed method.

Image processing technology is a popular practical technology in the computer field and has important research value for signal information processing. This article is aimed at studying the design and algorithm of image processing under cloud computing technology. This paper proposes cloud computing technology and image processing algorithms for image data processing. Among them, the material structure and performance of the system can choose a verification algorithm to achieve the final operation. Moreover, let us start with the image editing features. This article isolates software and hardware that function rationally. On this basis, the structure of a real-time image processing system based on SOPC technology is built and the corresponding functional receiving unit is designed for real-time image storage, editing, and viewing. Studies have shown that the design of an image processing system based on cloud computing has increased the speed of image data processing by 14%. Compared with other algorithms, this image processing algorithm has great advantages in image compression and image restoration.

Crack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non‐uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first‐order statistics, contrast‐limited adaptive histogram equalization, and noise filtering using non‐linear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non‐uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.

Owing to recent technological advancement, computers and other devices such as phones and digital cameras running several image editing software applications can be further exploited for other operations such as digital image processing operations. This paper attempts to conduct performance evaluation of the various image processing techniques using MATLAB-based analytics. Compared to the conventional techniques and other state-of-the-art applications used for image processing, MATLAB gives several advantages. MATLAB-based technique provides easy implementation and testing of algorithms without recompilation, and provides easy debugging with extensive data analysis and visualization. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. In addition, MATLAB codes are much concise compared to c++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the code faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent image processing application-tools running on MATLAB platform. We also provide an empirical-based method of image processing using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. With the different and most recent algorithms running on MATLAB toolbox, we provide simulations of several images to evaluate the performance of our proposed technique. The simulation results largely present MATLAB as a veritable approach for image processing operations.

The demand to improve image display in TFT-LCD, implementation of design for image processing is important. In order to meet the specific requirements of low-end Thin Film Transistor-Liquid-Crystal-Display (TFT-LCD) image display. This paper adopts a novel algorithm to conduct subsequent processing of the medical image after SCALER scaling, including contrast adjustment, gamma correction and dithering. Dithering algorithm is the focus of our research. After the study of some classical video image processing algorithms, and considering the real-time requirements, an intelligent algorithm is implemented for hardware implementation and improvement. For each part, MATLAB language is firstly used for advanced simulation to verify its feasibility, and then Right-To-Left (RTL) hardware language description is carried out. The characteristics extraction from images is performed implementing RGB standard and grayscale images. The pixel intensity is analyzed for each RGB component and the variance is calculated. When a panel displays a variation of 6% related with their reference values, the panel is rejected. The results obtained from classification shows a 95.24% accuracy rate in the detection of defects. The results of the two simulations show that the design achieves the expected goal, and the processing time is shorter.

In recent days, facial emotion recognition techniques are considered important and critical due wide array of application domains use facial emotion as part of their workflow and analytics gathering. Reduced recognition rate, inefficient computation and increased time consumption are the major drawbacks of these techniques. To overcome these issues, this paper develops the new facial emotion recognition technique named as Minimal Angular Feature Oriented Neural Network based Emotion Prediction (MAFONN-EP). Initially, the input video sequence are categorized into image frames that are pre-processed by eliminating the noise by using Weighted Median Filtering (WMF) technique and to separate the background and foreground regions using Edge Preserved Background Separation and Foreground Extraction (EPBSFE) technique. Then, the set of texture patterns are extracted based on four key parts such as two eyes, nose and mouth by using the Minimal Angular Deviation (MAD) technique. Particular features are selected by employing the Cuckoo Search based Particle Swarm Optimization (CS-PSO) technique that also reduces the feature dimensionality. Finally, the Weight Based Pointing Kernel Classification (WBPKC) technique is employed for recognizing the emotion. In the experimental results, the performance of the proposed technique is analyzed and compared with different performance measures like accuracy, sensitivity, specificity, precision, recall.

This paper provides an in-depth study and analysis of image processing for graphic design through chaotic encryption combined with a wavelet transform algorithm. Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensitive to the initial conditions, so the algorithm has a certain sensitivity to tampering and localization effect. The image of the encrypted graphic design is decrypted by the chaos decryption system, and the final image information of the graphic design is obtained. In terms of the security of the graphic design image itself, the complex dynamical properties of chaos are fully utilized to encrypt it, and the algorithm has a good encryption effect after statistical characteristic analysis, attack complexity, difference analysis, adjacent pixel correlation analysis, and key sensitivity analysis. The plaintext image is decomposed in odd-even sequence using the boosting algorithm to get the sequence with an even index and the sequence with an odd index; then, the diffusion algorithm is applied to the two sequences by the prediction and update algorithm, and this process is repeated many times to get the two ciphertext sequences after scrambling, merging these two sequences, and matrixing them to get the ciphertext image. Finally, the testing of the embedded planar designed image revolves around four aspects, namely, image processing, tamper sensitivity, robustness, and imperceptibility, to examine the designed planar designed image system. In the diffusion manipulation, the key matrix for diffusion is obtained using the threshold processing method of the Local Binary Pattern (LBP) algorithm for the matrices generated by the Logistic Chaos system, which improves the randomness of the algorithm. The experimental results verify the effectiveness and security of the algorithm.

With the rapid development of digital art and machine science and technology, the production form of modern animation tends to be diversified, and with the important role of animation color in the animation film foil and rendering, more and more attention was paid by the animation design industry. Computer graphic technology is a new type of artistic creation method, and its birth and development are closely related to computer technology. The purpose of this paper is to make some analysis and induction from the perspective of science and technology and culture and to explore the application of Photoshop graphics and image processing technology in the field of animation. First, the definition of “animation” and the extension of the animation category are introduced. Then, the main functions of Photoshop image processing technology are described in detail. According to the research content, this paper designs a self-made experimental short film “future city.” After the simulation of experimental animation film and the setting and positioning of the experimental scene, Photoshop software was used to show the animation scene. The experimental results show that the color changes in the short film “future city.” After the saturation of sunny scenery is reduced by 3 points and the brightness is reduced by 20 points and the filter, mode change, and color adjustment are carried out, a complete rainy scenery can be obtained. The self-made experimental short film has guiding significance for the creation and practice.

This paper presents a suggested approach to treat a major issue in images classification namely uncertainty. Uncertainty in image classification means some pixels within each cluster are more or less likely to actually belong to this cluster. So, techniques have been used in this paper to deal with the pixels that do not belong to specific regions, helping to raise image retrieval performance. This was done by merging one of the artificial intelligence techniques, which is image processing, with one of the statistical techniques for probability, which is evidential probabilistic. In such contexts, it may be advantageous to resort to two branches: hexagonal image processing based on partial down-sampling of the image resolution in both directions by half using weighted average performance then shifting the remaining pixels in alternate rows. The other is an evidential theory which is rich and flexible formalisms for representing and manipulating uncertain information. Both hexagonal image processing and evidential theory are used to obtain high accuracy in images classification. The hierarchical nature of the hexagonal image processing addressing scheme is exploited to extract features from the image efficiently.

A convolutional neural network’s weight sharing feature can significantly reduce the cumbersome degree of the network structure and reduce the number of weights that need to be trained. The model can directly input the original image, without the process of feature extraction and data reconstruction in common classification algorithms. This kind of network structure has got a good performance in image processing and recognition. Based on the color objective evaluation method of the convolutional neural network, this paper proposes a convolutional neural network model based on multicolor space and builds a convolutional neural network based on VGGNet (Visual Geometry Group Net) in three different color spaces, namely, RGB (Red Green Blue), LAB (Luminosity a b), and HSV (Hue Saturation Value) color spaces. We carry out research on data input processing and model output selection and perform feature extraction and prediction of color images. After a model output selection judger, the prediction results of different color spaces are merged and the final prediction category is output. This article starts with the multidimensional correlation for visual art image processing and color objective evaluation. Considering the relationship between the evolution of artistic painting style and the color of artistic images, this article explores the characteristics of artistic image dimensions. In view of different factors, corresponding knowledge extraction strategies are designed to generate color label distribution, provide supplementary information of art history for input images, and train the model on a multitask learning framework. In this paper, experiments on multiple art painting data sets prove that this method is superior to single-color label classification methods.

Two-dimensional convolution plays a fundamental role in different image processing applications. Image convolving with different kernel sizes enriches the overall performance of image processing applications. In this regard, it is necessary to design of reconfigurable convolver with respect to desired kernel sizes list. In this paper, a novel approach is presented for implementation of an area-efficient reconfigurable convolver with appropriate throughput and convolution computational time for an arbitrary kernel size list. This approach is based on the adjustment of logical blocks arrangement in the conventional convolvers. The feasibility and benefits of the proposed approach are demonstrated through a case study of the design implementation on an FPGA platform using the XILINX ISE software. Compared to the well-known reconfigurable convolvers, the proposed design significantly reduces convolution computational time and improves throughput with a reasonable number of hardware resources. For instance, the proposed reconfigurable convolver only requires 0.38 ms to perform a 3 × 3 convolution on a 268 × 460 image with 8-bit pixels and only occupies 455 slices resource of Xilinx Virtex-4 (XC4VLX25) FPGA, in which the throughput of 324 million outputs per second (MOPS) is provided with 81 MHz clock frequency for kernel size of 3 × 3. On average, the MPOS of the proposed approach is approximately improved by 43.13% in relation to the other considered alternatives. Experimental results confirm that the proposed reconfigurable convolver is a very competitive design among the alternative reconfigurable convolvers.

Problem statement. Search of plants on the observable image is important function of the UAV onboard optoelectronic systems. One of methods of the observable surface image analysis is the textural analysis which allows to select homogeneous areas on the observable image and to define contours of the plants which are covered with various structures and not having boundaries accurately defined on the image.The purpose. To develop algorithm of the textural analysis of a site terrestrial or a water surface, fulfilled by the digital image processing. In paper the image textural analysis algorithm fulfilled on the basis of an evaluation of local spectral and statistical performances of the image is presented.Results. The algorithm according to which for each element of the analyzed image in a square environ from 1024 elements the local spectra oriented under different angles and local variances are calculated is developed. Calculated by digital image processing amplitudes of a spectrum and variances values characterize texture parameters in a environ of each element of the image. Points of considerable modifications of local properties define position of boundaries between various structures.The practical importance. Results of the considered algorithm trials as a part of simulation model have shown a possibility of its application in the UAV onboard image observed analysis system.

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is one among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too. Image detection on road is primarily carried out with the help of camera with Raspberry pi 3 model b+ and stimulation software. The device is built in such a way that we can identify any potholes in the respective roads and able to rectify as soon as possible with the help of the device. The data signals shared by the device will be converted to text signals from which we can get it right. These devices are fixed at top of the lamppost which is located at the corners of the road from where the device is monitoring the road at 120 degree for weekly once respectively. Keywords: Image processing, Image detection on road, Raspberry pi 3, 120 degree

The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.

Considering the dependance of materials’ properties on the microstructure, it is imperative to carry out a thorough microstructural characterization and analysis to bolster its development. This article is aimed to inform the users about the implementation of FIJI, an open source image processing software for image segmentation and quantitative microstructural analysis. The rapid advancement of computer technology in the past years has made it possible to swiftly segment and analyze hundreds of micrographs reducing hours’ worth of analysis time to a mere matter of minutes. This has led to the availability of several commercial image processing software programs primarily aimed at relatively inexperienced users. Despite the advantages like ‘one-click solutions’ offered by commercial software, the high licensing cost limits its widespread use in the metallographic community. Open-source platforms on the other hand, are free and easily available although rudimentary knowledge of the user-interface is a pre-requisite. In particular, the software FIJI has distinguished itself as a versatile tool, since it provides suitable extensions from image processing to segmentation to quantitative stereology and is continuously developed by a large user community. This article aims to introduce the FIJI program by familiarizing the user with its graphical user-interface and providing a sequential methodology to carry out image segmentation and quantitative microstructural analysis.

This paper critically surveys the aspect of digitalized image processing and segmentation with central focus on artificial intelligence. A digitalized image is composed of numerous elements that must be "analyzed," since better “phrasing”, and the "research" done on such elements might disclose a lot of strange data. This data may assist in solving a variety of business challenges, which is one of the numerous end objectives associated with image processing. Digital image analysis of image processing is a collection of methodologies applied to process computerized images. It integrates accomplishing basis assignments such noise minimization and more complex assignments such as image classification, fault diagnostics, emotional response, image fragmentation and image segmentation. Image enhancement applying neural networks has, over the past few decades, been applied widely in the clinical setting due to the advent of advanced computing ecosystems and algorithms. Medicine, industries, police departments, agriculture, defense, finance and security are some of the additional domains where the concept of image processing and segmentation can be applied.

Microscope vision analysis is applied in many fields. The traditional way is to use the human eye to observe and manually focus to obtain the image of the observed object. However, with the observation object becoming more and more subtle, the magnification of the microscope is required to be larger and larger. The method of manual focusing cannot guarantee the best focusing position of the microscope in use. Therefore, in this paper, we are studying the existing autofocusing technology and the autofocusing method of microscope based on image processing, which is different from the traditional manual focusing method. The autofocusing method of microscope based on image processing does not need the information such as the target position and the focal length of optical system, to directly focus the collected image. First of all, in order to solve the problem of large computation and difficult real time of traditional wavelet based image sharpness evaluation algorithm, this paper proposes an improved wavelet based image sharpness evaluation algorithm; secondly, in view of the situation that the window selected by traditional focusing window selection method is fixed, this paper adopts an adaptive focusing window selection method to increase the focusing window. Finally, this paper studies the extremum search strategy. In order to avoid the interference of the local extremum in the focusing curve, this paper proposes an improved hill-climbing algorithm to achieve the accuracy of focusing search. The simulation results show that the improved wavelet transform image definition evaluation algorithm can improve the definition evaluation performance, and the improved mountain climbing algorithm can reduce the impact of local extremum and improve the accuracy of the search algorithm. All in all, it can be concluded that the method based on image processing proposed in this paper has a good focusing effect, which can meet the needs of anti-interference and extreme value search of microscope autofocus.

Over the last few years, the computer vision has a huge impact in the field of information technology. The foundation for computer vision is laid by image processing. Digital Image Processing is one of the vast area of research; with three indispensable aspects as; Image De-noising, Image Compression & Image Security; with multiple applications in the field of automotive, medical, space, security systems, military & many more. This survey paper includes the evolutionary analysis of various image de-nosing techniques such as Median filtering, Pixel similarity weighted frame averaging, Gaussian filtering, Bilateral filtering, Anisotropic diffusion; that are used for betterment of system performance in terms of noise suppression, edge preservation, texture detection, Operating frequency, PSNR & many more. Moreover, the paper also includes the evolutionary analysis of multiple image compression techniques based on DCT, DWT, fractal image compression & neural network. Finally, the various image security techniques are analyzed. From the study of various literatures on image processing, it is found that combine model of image processing with all the three stages including image de-noising followed by image compression & image security is not yet discussed.

Genetic Programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree-type structure. Similarly, research is also gaining momentum in the field of Image Processing, because of its promising results over vast areas of applications ranging from medical Image Processing to multispectral imaging. Image Processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, i.e., complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for Image Processing since its inception. Many interesting GP techniques have been developed and employed in the field of Image Processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in Image Processing and provides useful resources for further research. Also, the comparison of different parameters used in different applications of Image Processing is summarized in tabular form. Moreover, analysis of the different parameters used in Image Processing related tasks is carried-out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in Image Processing but also in other fields, may increase. Additionally, guidelines are provided for applying GP in Image Processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set., 31 pages, 12 figures, and 1 table

As an interdisciplinary between quantum computing and image processing, quantum image processing provides more possibilities for image processing due to the powerful parallel computing capabilities of quantum computers. In recent years, quantum image processing attracts more and more researcher’s attention. In order to allow researchers to better understand quantum image processing technology, we have reviewed relevant literature in recent years in the paper. First, the background and mathematical concepts of quantum computing are introduced. Then, the research progress of quantum image processing is sorted out and summarized in the fileds of quantum image representation, geometric transformation, image encryption, edge detection, image segmentation, filtering and compression. Finally, we have discussed the advantages and disadvantages of quantum image processing, and pointed out the potential future research.

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concerning digital image processing. Here linear, as well as non-linear filters, are presented. It is easy to decide about the better filter for improving the image processing output from the discussion.

– Medical image segmentation is considered the most precarious element in the analysis and processing of real-life images in the clinical sector. Actually, segmentation effects affect the subsequent procedures of image evaluation, life object illustration and description, feature dimensions and the subsequent considerable task levels such as object categorization. In that case, medical image segmentation is the most essential and crucial aspect for aiding the visualization, delineation and depiction of regions of interest for any particular image. The aspect of physical segmentation of a picture is not just challenging and time-consuming task to do, but also problematic to assure accuracy considering the wide-range image modalities and unguided image quantities which have to be observed. In that case, it now becomes fundamental to evaluate the present approaches of image segmentation based on the application of computing algorithms which require the interaction of users with evaluating medical images. In the process of image segmentation, an anatomic classification requires to be extracted or defined to be projected effectively and independently. In that case, this contribution is focused on image segmentation to extract details for decision-making in the clinical sector. The paper presents generalized and relative techniques that have been categorized into three groups: pixel-centre, edge-centred and region-centred techniques. The paper also provides a highlight of the strengths and weaknesses of these techniques in reference to the appropriateness of a wide-range application in medical image segmentation.

Different processing of the images, such as the image captured, saved and retrieved from another use of the specific image, must be restructured in various ways in the process. More methods such as image restoration, picture segmentation, improvement of the picture etc can be used when processing images. Reconstructed in 3D picture 2D pictures are need to be proper. Including geometric wavelets and geometric analysis the structural work focused upon a variational and a selectable differential equation to test PDE’s which is a convergence of stochastic modelling and analysis of harmonics. This paper focuses primarily on the critical reviews of the image segmentation collection with the PDE application as a mathematical method and introduces the key tool of mathematics and techniques along with the literature-based analysis.

Image thinning is the most essential pre-processing technique that plays major role in image processing applications such as image analysis and pattern recognition. It is a process that reduces a thick binary image into thin skeleton. In the present paper we have used hybrid parallel thinning algorithm to obtain the skeleton of the binary image. The result skeleton contains one pixel width which preserves the topological properties and retains the connectivity.

Machine vision is a promising technique to promote intelligent production. It strikes a balance between product quality and production efficiency. However, the existing metal surface defect detection algorithms are too general, and deviate from electrical production equipment in the level of response time to the target image. To address the two problems, this paper designs a detection algorithm for various types of metal surface defects based on image processing. Firstly, each metal surface image was preprocessed through average graying and nonlocal means filtering. Next, the principle of the composite model scale expansion was explained, and an improved EfficientNet was constructed to classify metal surface defects, which couples spatial attention mechanism. Finally, the backbone network of the single shot multi-box detector (SSD) network was improved, and used to fuse the features of the target image. The proposed model was proved effective through experiments.

In this paper, we have explored and compared different digital image processing techniques on 2D Images of asphalt pavement for evaluating the macro-texture. Compared to the conventional tests, ima...

In the tomographic reconstruction of seismic travel time data, care must be taken to keep the propagation of data errors to the model space under control. The non-Gaussian noise distribution—especially the outliers in the data sets- can cause appreciable distortions in the tomographic imaging. To reduce the noise sensitivity well-developed tomography algorithms can be used. On the other hand, the quality of the tomogram can further be improved by using image processing tools. In the paper, a newly developed robust filter is presented, in which the Most Frequent Value (MFV) method developed by Steiner is applied. To analyze the noise reduction capability of the new filter (called Steiner-filter) and to compare it to smoothing filters based on arithmetic- and binomial mean, as well as median, medium-sized tomographic images are used. The MFV-based filter is successfully tested also in edge detection procedures.

In this paper, a control system of a mobile robot for automatic search and transportation of an object is developed. The control system is constructed using image processing and distance measurement. The system has two functions of automatic search and transportation. The targets are detected by HSV color recognition using camera image. The autonomous control is realized based on proportional integral control law to determine the angular velocity, where the control error is calculated using the image information and the angle-pixel characteristics. The effectiveness of the control system considered in this paper was confirmed through experimental results.

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.

In this work, the problems of research of ultrasonic images of joints are formulated. It is that for early diagnosis of developmental disorders of the hip joints needs to take frequent pictures, and the least harmful to health is ultrasound. But the quality of such images is not sufficient for high-quality automated measurement of geometric parameters and diagnosis of deviations. The ultrasound image of the hip joint is evaluated by quantifying the exact values of the acetabular angle, the angle of inclination of the cartilaginous lip, and the location of the center of the femoral head. To get these geometric parameters, you need to have clear images of objects. And for the operation of automated computer measurement systems, it is necessary to use such methods of pre-digital image processing, which would give clear contours of objects. Known and available image processing algorithms, in particular contour selection, face problems in processing specific medical images. It is proposed to use the developed method of sharpening to further obtain high-quality contour lines of objects. A mathematical model of the method is presented, which is a formula for converting the intensity values of each pixel of a digital image. As a result of this method, the noise component of the image is reduced, and the intensity differences between the background and the objects are increased, and the width of these differences is one pixel. The algorithm of a sequence of processing of ultrasonic images and features of its application have resulted. The results of the developed set of methods are given. The paper presents the results of processing the real image of the hip joint, which visually confirms the quality of the selection of objects on view.

The traditional teaching mode of the digital image processing, which is centered on teachers, textbooks and classrooms, hinders the requirements of the image technology development and impedes the cultivation of the innovative talents. Though the digital image processing is one of the emerging technologies, it has been rapidly developed and widely used in the electronic information technology. In order to adapt to its development, this paper has explored the teaching and practice reform on the digital image processing by different color mode and to improve the understanding of students as well as teacher by clustered image which will enhance the core idea of a particular image by exploring all the colors present in every part of image. Students can better gain their knowledge skill by observing those images in colored clustered form.

Automated dimensional inspection is commonly expensive because of the requirement of high-precision measurement devices. To perform a precision measurement, the technician must be highly skilled and fully understands the operation of the equipment. This study proposes a method for reconstructing the two-dimensional profiles of ring-shaped objects using image processing. At first, an industrial camera captures partial images of the object. After that, through several image processing procedures such as binarization, line detection, and contour recognition, the profiles in the images were detected and grouped. Then, a calibration model was introduced to calibrate and combine the contours from partial images. This process results in a point cloud consisting of every point from the outer and inner contours of the object, which can be directly used for the automatic measurement. To verify the proposed method, the data were compared with those acquired from the ATOS measurement system, revealing a favourable correlation.

The Unmanned Aerial Vehicle (UAV) has been around for a long time but has been widely used recently by humans. Their acceptance of various communications-based applications is expected to improve coverage, compared to traditional ground-based solutions. In this paper, the Deep-learning and Image Processing Process framework is expected to provide solutions to the various problems already identified when UAVs are used for communication purposes. UAVs are used in disaster relief because of their accessibility even in inaccessible places. In this paper, we propose research into Deep learning and Image Processing strategies for UAVs. In deep learning is a form of machine learning that teaches computers to do what comes naturally to people: learn by example and get a lot of attention recently and for a good reason. It achieves previously impossible results. Image processing is the process of performing a specific task on an image, finding an enhanced image or extracting useful information from it. So our paper has the idea of using in depth face recognition and photo processing a digital photo taken by the UAV to identify victims of rescue, overcoming back to the latest UAV technology some of which include blurry images, unable to identify the victim when there are too many objects and much more. The solution includes a variety of features that allow for the distribution of images. It includes features and presentation of image detection and demonstrates the effectiveness of drone use in damage applications.

With the continuous advancement of science and technology, the improvement of mobile terminal hardware performance and the large-scale popularization of smart phones have brought new experiences and methods to surveying and mapping work. This article mainly studies the application of remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. First, perform grayscale processing on the image in the experiment, then remove the noise in the image and smooth the image through the median filter method and finally use the Canny operator to perform edge detection to obtain a binarized image containing only the target object, and this is done by image feature extraction. After using three-dimensional scanning modeling to extract the image feature points, the target manager is used for sample analysis. Obtain the projection matrix through the interface, and then perform coordinate conversion to complete the positioning of the target scene. In this paper, the BRISK feature point detection algorithm with fast speed and small calculation is used to detect the target, and SVM is used for remote sensing feature classification. Experimental data show that the recognition success rate of the algorithm is 84%. The results show that mobile augmented reality technology and remote sensing image processing technology can improve the efficiency and accuracy of surveying and mapping engineering, and have strong ease of use and stability.

The automatic segmentation of coronary artery in coronary computed tomography angiography (CCTA) image is of great significance for clinicians to evaluate patients with coronary heart disease. When a 3D image is limited by the amount of available GPU memory, reducing the resolution of 3D image will easily lead to the loss of image detail information. Taking patches of image as input cannot make full use of image context information. Image segmentation based on deep learning is difficult to recover perfect smooth edges. The use of smooth loss function may filter out some small lesions on the coronary artery. In this paper, we present a novel CCTA image segmentation framework that combines deep learning and digital image processing algorithms to address these challenging problems. We first use V-Net to process the CCTA image with lower resolution, and get the basic feature map (rough segmentation result) with the same resolution as the original CCTA image. Then, the original CCTA image is concatenated to the basic feature map and input it into the patch-based cascaded V-shaped module to obtain a accurate coronary artery segmentation image. Finally, the center points of coronary segmentation image and the basic gradient image of the original coronary image are obtained by morphological operation. The center points of coronary artery segmentation image are used as seed points, region growing is performed on the binary basic gradient image until the white contour boundary is searched, so as to obtain a coronary segmentation result with full segmentation and smooth edges. The proposed method is analyzed quantitatively and qualitatively, and the results show that the method is better than the mainstream baseline. The ablation experiment also proved the effectiveness of each module.

The ability of computers that are increasingly reliable in various fields, especially in helping the image processing sector through improving image quality, is very much felt so that the empowerment of computers at any time needs to be improved. Image quality improvement can be made with various techniques, including Image Quality Improvement with Image Brightness and Image Sharpening methods. The process begins with capturing the image and then continues with increasing the intensity of brightness, image contrast and sharpening. Image processing results are indicated by changes in the resulting image and changes in the image histogram

Students’ classroom behavior recognition and emotion recognition effects directly determine the degree of teachers’ control of the classroom teaching process. At present, teachers and students belong to two groups in traditional teaching, and teachers cannot effectively mobilize students’ learning emotions. In order to improve the teaching effect, this paper combines the PSO algorithm and the KNN algorithm to obtain the PSO-KNN joint algorithm, and combines with the emotional image processing algorithm to construct an artificial intelligence-based classroom student behavior recognition model. Moreover, based on the image processing technology, this paper uses key frame detection for feature recognition, and this paper improves the recognition process based on the inter-frame similarity measurement algorithm and initial cluster center selection in the key frame extraction method of clustering. In addition, this paper analyzes the effect of the model constructed on the behavior recognition and emotion recognition of students. The research results show that the joint algorithm constructed in this paper has a high accuracy rate for students’ emotion recognition and behavior recognition, and can meet the actual teaching needs.

Here, in this study we can learn about Bird species recognition. In forest areas cameras are fixed at various locations which capture images periodically. From those images the birds living in such dense forest areas can be identified. It would be useful if we can able to classify the species of birds with the help of those images. But that is not an easy task because of the variations in the light effects, illumination and camera viewpoints. So we need to involve image processing techniques for preprocessing the captured image and also deep learning techniques are to be implemented for classifying the images. For classification purpose training is to be done with the help of image data set. Here we propose a method of discriminating birds by means of the ratio of the distance between eye and beak to that of the beak width. By combining this mythology with image processing and SVM classification technique a new bird species recognition algorithm is proposed. The proposed new methodology will improve the accuracy in classifying.

Face recognition technology has become an important quantitative examination method in the field of forensic identification of human images. However, face image quality affects the recognition performance of face recognition systems. Existing research on the effects of face image denoising and enhancement methods on the face recognition performance are typically based on facial images with manually synthesized noises rather than the noises under natural environmental corruption, and their studied face recognition techniques are limited on the traditional face recognition algorithms rather than state-of-the-art convolutional neural network based face recognition methods. In this work, face image materials from 33 real cases in forensic identification of human images were collected for quantitative analysis of the effects of face image denoising and enhancement methods on the deep face recognition performance of the MXNet system architecture based face recognition system. The results show that face image quality has a significant effect on the recognition performance of the face recognition system, and the image processing techniques can enhance the quality of face images, and then improve the recognition precision of the face recognition system. In addition, the effects of the Gaussian filtering are better than the self-snake model based image enhancement method, which indicates that the image denoising methods are more suitable for performance improvement of the deep face recognition system rather than the image enhancement techniques under the application of the practical cases.

Image segmentation is a key technology in the field of computer image processing. Among them, segmentation methods based on active contour models have been developed rapidly in recent years due to their effective processing of complex images such as medical images. These methods have achieved significant results in medical, military, and industrial fields. Present research work mainly introduces the training of computer graphics and image processing technology and the method of active contour image segmentation. It focuses on the study of image segmentation methods and focuses on the segmentation methods based on active contour models. Firstly, it summarizes two types of segmentation methods based on edge and region and summarizes their advantages and disadvantages. Then, the segmentation method based on the active contour model is studied, and several typical active contour models are comprehensively compared. Finally, the local binary fitting model and the local Gaussian distribution fitting energy model are improved and simulated. Furthermore, from the development of computer graphics and image processing technology to analyze some methods and means of training this professional talent. The experimental results of this article show that the active contour image segmentation algorithm can not only ensure the image segmentation algorithm but also reduce the number of iterations and shorten the image segmentation time. Compared with the CV, LBF, and LGIF models computational efficiency of Segmentation method is increased by 9.2 times, 2.64 times, and 1.44 times, respectively.

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

Over the last decade, the French space agency (CNES) has designed and successfully operated high-resolution satellites such as Pléiades. High-resolution satellites typically acquire panchromatic images with fine spatial resolutions and multispectral images with coarser samplings for downlink constraints. The multispectral image is reconstructed on the ground, using pan-sharpening techniques. Onboard compression and ground processing affect however the quality of the final product. In this paper, we describe our next-generation onboard/on-ground image processing chain for high-resolution satellites. This paper focuses on onboard compression, compression artefacts correction, denoising, deconvolution and pan-sharpening. In the first part, we detail our fixed-quality compression approach, which limits compression effects to a fraction of the noise, thus preserving the useful information in an image. This approach optimises the bitrate at the cost of image size, which depends on the scene complexity. This technique requires however pre- and post-processing steps. The noisy HR images obtained after decompression are suited for non-local denoising algorithms. We show in the second part of this paper that non-local denoising outperforms previous techniques by 15% in terms of root mean-squared error when tested on simulated noiseless references. Deconvolution is also detailed. In the final part of this paper, we put forward an adaptation of this chain to low-cost CMOS Bayer colour matrices. We demonstrate that the concept of our image chain remains valid, provided slight modifications (in particular dedicated transformations of the colour planes and demosaicing). A similar chain is under investigation for future missions.

The human visual system is encompassed with three components that help to produce a color sensation. The color display devices use this concept and create their full range of colors by incorporating all three primary color components. Any displayable color can be created with the help of these primaries. This paper comprises the concept of color space, which helps to understand a particular device’s color proficiencies. Since colors are a more robust descriptor, color spaces are considered to play a significant role in representing an image appropriately. The performance of many algorithms depends on the selection of an appropriate color space. There are many kinds of color spaces that can be used in real-time applications. These are RGB, nRGB, HSV, TSL, YCbCr, YUV, YES, CIE-XYZ, CMYK, etc. In this paper, color spaces and their vast classification are described in brief. Apart from these, the characteristics, applicability, limitations, mathematical conversion, and other essential factors are also explained for each color space.

Night vision devices are used for capturing images under low light conditions ( $ lux), which are significantly different from images under standard illumination. The night vision devices based on image intensifier tube have power and cost advantages over other night vision devices, such as thermal imaging for short-range surveillance. However, these vision devices suffer from scintillation noise, which makes the resulting images noisy under very low light conditions. This paper presents a comprehensive methodology that uses a consolidated image processing module to improve the performance of low-light images acquired using image-intensifier-based night vision devices. The proposed module is a combination of a two-stage image processing framework. First, a spatio-temporal filter (based on wavelet and recursive frame averaging) is used to minimize inter-frame and intra-frame noise. Then, image enhancement is performed followed by residual noise reduction using a local spatial filter before applying image tone adjustment for display mapping. The results of the proposed module demonstrate performance enhancement of night vision devices under controlled simulated light conditions as well as field conditions. Objective and subjective analysis also reveals that the proposed module can effectively reduce noise and significantly improve visual image quality by 20–30%.

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.