A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images

The major studied in this dissertation is the non-destructive testing method of silkworm cocoon's quality, based on the digital image processing and photoelectricity technology. Through the images collection and the data analysis, procession and calculation of the tested silkworm cocoons with the non-destructive testing technology, internet applications automatically reckon all items of the classification indexes. Finally we can conclude the classification result and the purchase price of the silkworm cocoons. According to the domestic classification standard of the silkworm cocoons, the author investigates various testing methods of silkworm cocoons which are used or have been explored at present, and devices a non-destructive testing scheme of the silkworm cocoons based on the digital image processing and photoelectricity technology. They are dissertated about the project design of the experiment. The precisions of all the implements are demonstrated. I establish Manifold mathematic models, compare them with each other and analyze the precision with technology of databank to get the best mathematic model to figure out the weight of the dried silkworm cocoon shells. The classification methods of all the complementary items are designed well and truly. The testing method has less error and reaches an advanced level of the present domestic non-destructive testing technology of the silkworm cocoons.

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

Remarkable technological developments for efficient image recognition (i.e., image acquisition and image data processing) have been reported in the past decade. Such advances in imaging and image processing technologies have driven significant progress in mobile electronics and machine vision applications. In particular, for image acquisition devices, two types of natural eyes (i.e., chambered and compound eyes) have inspired the development of novel multifunctional imaging devices with unique optical geometries. For image data processing devices, novel computing devices based on memristor crossbar arrays, such as electronic synapses, have been developed. More recently, the integration of imaging and image processing devices in a single unit further enhances the system‐level efficiency. Herein, such recent advances in the bio‐inspired artificial vision and neuromorphic image processing devices, aimed at providing efficient image recognition, are reviewed. First, various imaging devices inspired by the structural and functional features of natural eyes are introduced. Second, artificial synapses and their operation principles are thoroughly discussed. Third, the neuromorphic vision sensor that integrates the imaging and image processing devices is reviewed. Finally, a brief summary and future outlook are presented.

The digital image acquisition and processing technology already mature also in domains and so on visual communication obtains day by day[1]. However, the image acquisition and processing technology had many kinds, for example white balance processing and image denoising methods. The traditional image acquisition and processing systems are implemented using software by the PC or DSP chip[2], and sometimes software in series can not meet the requirement of high speed image processing. this article has given FPGA in the image acquisition and processing simple application, applied FPGA in the image acquisition and processing to be possible to enhance the image processing the speed, reduced takes the logical unit. The system designed by above-mentioned way achieves a satisfying effect by experimental verification.

The human–machine interface with head control can be applied in many domains. This technology has the valuable application of helping people who cannot use their hands, enabling them to use a computer or speak. This study combines several image processing and computer vision technologies, a digital camera, and software to develop the following system: image processing technologies are adopted to capture the features of head motion; the recognized head gestures include forward, upward, downward, leftward, rightward, right-upper, right-lower, left-upper, and left-lower; corresponding sound modules are used so that patients can communicate with others through a phonetic system and numeric tables. Innovative skin color recognition technology can obtain head features in images. The barycenter of pixels in the feature area is then quickly calculated, and the offset of the barycenter is observed to judge the direction of head motion. This architecture can substantially reduce the distraction of non-targeted objects and enhance the accuracy of systematic judgment.

This paper gives a point to detect transmission line broken stocks defect problems by using image processing and recognition technology, which has obtained the good effect in tests. By comparing with a set of template, the wire broken stocks defects can be extracted from the complex background identify. The method can be applied to power line inspection by helicopters and unmanned aerial vehicle (uav) in a variety of ways.

In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects

Skin cancer is a major health issue in the present day especially melanoma skin cancer. In general most of the skin cancers are cured if they are detectedin the early stage. With the rapid growth of skin cancer, there is a need for an automated computerized diagnosis mechanism of skin cancer in the early stage is required. Many of the skin cancer images have similar visual characteristics. It is an important challenging task to extract the features from the skin cancer images. The automated computerized diagnosis mechanism helps to improve the accurate analysis of skin diseases which helps the dermatologists to accelerate the diagnostic time and improve the better treatment for the patients. This paper mainly presents the comparative study on traditional image processing and current technologies of different image processing techniques for skin cancer image classification, preprocessing techniques, Feature extraction, and image segmentation datasets.

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

With the development of electronic technology, computer software and hardware technology, image processing and technology related to human vision, the technology of machine vision image processing has also achieved rapid development. With the rapid development of science and technology, digital image processing technology has been widely used in various fields, among which image segmentation is an extremely important technology. Image segmentation is an important intermediate technology in digital image processing. It relies on the underlying technology of image digital processing to serve high-level applications such as pattern recognition. Processing the information interaction between machine and the real world is the first step in the study of artificial intelligence, and the goal of computer vision research is to make the computer have the ability to perceive and understand image information similar to human. This paper expounds the basic principle of image segmentation technology, analyzes and discusses the methods of image segmentation, and studies the application of image segmentation technology in digital image processing.

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.

With the continually development of computer technology and modern communication technology, radio frequency technology and image processing technology become the important component of modern security system. In particular, the application of the intelligent access control system of the two technologies becomes a hot research topic in the field. This article first briefly analyzes the principle of radio frequency technology and establishes intelligent access control system based on radio frequency technology and image processing technology. It analyzes the theory and application compatibility of this system which provides technical support and theoretical basic for the researches in this field to some extent.

With the development of Transportation Highway and railroad build, mining tunnel geological exploration in the road construction in the proportion of great. This paper presents a design of image processing software of Geological Engineering images for automatic analysis and processing. At present, the technology of image processing, most algorithms are based on the specific image information of specific analysis, and the face image is very complicated, different regions, and even the same construction sections in different areas of the face image may have very big difference. For the tunnel excavation face of digital image processing algorithms have little, need to start from scratch. This paper describes the use of digital image processing technology of Geological Engineering image image segmentation, found on the rock face, through the comparison of edge detection operator and Sobel Gauss - Laplasse operator methods advantages and disadvantages, a value of two images as the processing object image processing algorithm. The technology of Geological Engineering image analysis on tunnel construction period prediction plays a very important role.

With the development of Transportation Highway and railroad build, mining tunnel geological exploration in the road construction in the proportion of great. This paper presents a design of image processing software of Geological Engineering images for automatic analysis and processing. At present, the technology of image processing, most algorithms are based on the specific image information of specific analysis, and the face image is very complicated, different regions, and even the same construction sections in different areas of the face image may have very big difference. For the tunnel excavation face of digital image processing algorithms have little, need to start from scratch. This paper describes the use of digital image processing technology of Geological Engineering image image segmentation, found on the rock face, through the comparison of edge detection operator and Sobel Gauss - Laplasse operator methods advantages and disadvantages, a value of two images as the processing object image processing algorithm. The technology of Geological Engineering image analysis on tunnel construction period prediction plays a very important role