Performance of an image analysis processing system for hen tracking in an environmental preference chamber

A three-dimensional (3D) laser scanning system is the main part of reverse engineering. It is a comprehensive technological system, including numerical control (NC), a laser, computer-aided design/manufacturing (CAD/CAM), precision mechanisms, computing image sampling, and image processing. Generally, it consists of a motion control system, an image sampling system, and an image processing system, etc. This paper focuses on introducing control principles, hardware constitutions, and software algorithms of the servo motion system, and analyzes and presents the hardware constitutions, software algorithms, and experimental results of the image sampling and processing system in great detail. Finally, the authors come to the conclusion that the errors all occur within the range ±0.1 mm.

Assessment of the Miocene aquifer in Wadi El Farigh area by using GIS techniques

Edge detection is a fundamental step in many machine vision or image processing applications and systems. The importance of edge detection increases as one seeks to increase the level of automation in the image processing system. The paper reports an edge detection operator which is fast in application to grey scale images and is sensitive to 'difficult' edges but not sensitive to artifacts such as scanner interlacing artifacts. The edge detection operator presented is simple but provides good detection of edges and clean and continuous edges across most of the image.

An image processing system that incorporated some retinal properties was investigated for the processing of two dimensional images. The system was required to carry out basic image processing tasks such as edge detection. A new filtering technique was deduced from the physiological findings on the distribution of the receptive fields of the retinal ganglion cells. This filtering technique was then incorporated in the design of an image processing system in, which the spatial resolution increased linearly towards the centre of, the image. The design, was based on a discrete polar distribution of processing areas on an inhomogeneous triangular sampling grid. This resulted in a highly localized processing system which simplified the development of the higher image processing tasks such asboundary following. The retinal image processing system was simulated on a VAX 11/750. The computational cost of conducting operations such as edge detection, boundary detection and boundary following, using the designed system, was evaluated and compared with that of the conventional image processing system.

Statistical kinetics for pulverized coal combustion

The 2022 International Conference on Image Processing and Intelligent Information Systems (IPIIS 2022) was successfully held online during October 22-23, 2022 in Zhengzhou, China. In the context of the conference, scholars and experts in the fields of image processing and intelligent information systems, involved both in experimental and theoretical research, were invited from various universities and institutions. The conference focused on the most recent advancements in topics such as 'Wireless Sensor Networks', 'Digital Signal Processing', 'Embedded Systems' and others.
 The main goal of IPIIS 2022 was to present the related research work, to promote the cooperation of experts and scholars in these fields, to share the research achievements both on a national and international level. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the editorial committee.
 Because of restrictions on travel and indoor activities related to COVID-19, we were forced to hold the conference online only. Attendees were notified well in advance that the on-site conference was changed to an online conference, where the timeline and order of the talks were defined. The video conference was held on the original set dates. Due to the large variety of topics and the relatively large number of attendees, the conference was separated into main and parallel sessions. Each plenary speaker gave an oral presentation of 30-40 minutes accompanied by his slides' material, allocated for questions and discussion.
 The organizing and editorial committees of IPIIS 2022 hope you can enjoy the reading of this volume. Additionally, we want to thank all authors and participants for providing their valuable contributions for these proceedings, as well as the reviewers for their constructive recommendations and feedback aiding to improve the presented articles.
 IPIIS Organizing Committee
 Zhengzhou, China

Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

With increasing applications of image processing in manufacturing, it has become necessary to provide industry with powerful as well as flexible means of adapting the technology to the solution of different kinds of tasks at hand. Besides the standard components of an image processing system, the availability ofvarious software tools to fill the gap between design, digitization, measurement and the manufacturing phases should be emphasized. The users could e.g., desire to transfer grabbed images into certain computer graphics data format for further processing. They may want to apply image processing for the measurement of machine parts interactively or automatically, or they may want to convert the pictures processd, e.g. with edge detectors, into some CAD data format, or even to generate a numerical control file to machine the parts directly from the image processing data. With this motivation, two software packages FIPS—Flexible Image Processing System, and IQIS— Integrated QUality Inspection System have been developed for manufacturing and quality inspection and have been used by industry for various purposes from simple image digitization and processing, luminance adjustment, data and file format conversion, generation of control files for numerical control in laser cuffing, to direct control of robot movement, and interactive as well as automatic machine part measurement.

Currently, the fast evolution of cameras in recent years has made them promising tools for diagnostics of Tokamak. The solution presented in this paper consists of a prototype of high-speed visible image acquisition and processing system (HVIAPs) dedicated for experimental advanced superconducting tokamak shape and position control. Graphics processing unit (GPU) and field-programmable gate array (FPGA) are typically used as accelerators or co-processors in addition to a CPU. Such a heterogeneous computing system can combine the advantages of its individual components. The main imaging equipment components used for this system are four high-resolution fast cameras equipped with fiber interface. Image data from the cameras are received by the frame grabber card based on the FPGA and transmitted to the GPU via the peripheral component interconnect express interface. The software support for the system includes low-level drivers and application programming interface libraries for all components and the algorithm developed for image processing. The offline image process results are compared to equilibrium fitting code, which is a commonly used reconstruction method, with an average error of 1.5 cm. The total processing time for one frame is less than 0.3 ms.

In this paper, a multi-input computer system for image acquisition and processing is presented. This system is equipped with a 2-wavelength thermal channel and any 4 other ones, which can be used to capture visual, X-ray, ultrasound images, etc. Novel image processing tools are described, mainly for geometrical and 3D transformations.

ABSTRACT: The aim of this paper was to verify the viability of digital image processing in the measurement of rice seedling and its potential use as seed vigor test. The experiment took place at the Laboratório Didático de Análise de Sementes, located in Capão do Leão (RS), using seeds of eight rice cultivars: Irga 424, Puitá Inta CL, BRS Esmeralda, SCS 116, Guri Inta CL, BRS Pampa, BRSGO Serra Dourada and Olimar. An image processing system (IPS) was used to measure length and area of shoot and root as an index of seed vigor in rice seeds. The experiment design was completely randomized with five replications. The evaluated variables were root and shoot length in graded ruler and by image, root and shoot area, and field emergence. Results obtained with the IPS were correlated with other quality tests though the Pearson tests. Determination of shoot length and leaf area of the seedlings obtained through IPS is effective for assessing the vigor of rice seeds.

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.

In this article, hardware and software tools is proposed to process image in satellite image processing systems so that the parameters can be calculated and make image viewable according to user requirements. This is a basic approach to solve real-time problems for water bodies like oceans. The extraction of Water bodies is difficult because of mountain and building shadow. There are three water indices for water bodies extraction namely, Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Automated Water Extraction Index (AWEI) that extract water in real-time with onboard image processing system using FPGA. By selecting any input image there are given three options of NDWI, MNDWI and AWEI. When any one option is selected then the output is obtained according to selected indices. The proposed system of onboard real-time image processing using hardware software combination reduces total time to procure satellite image and extraction of water bodies.

Due to many drawbacks such as human error, tremendous energy and time consumption in traditional method of pipeline inspection, this paper proposes an automated pipeline diagnostic using image processing and intelligent system. The primary focus of the developed system is underwater pipeline network due to higher inaccessibility and defect rate. Comparatively, many methods were used in image processing along the years and Convolutional Neural Network (CNN) was identified as the most effective method for this case study based on the literature review. Narrowing down into CNN context, the author has identified and compared the mean accuracy of transfer learning process of two pre-trained convolutional neural networks which were also the winners of ImageNet Large Scale Visual Recognition Challenge (ILSVRC) for the year 2012 and 2014. They are known as AlexNet and GoogLeNet. This was done by initially modelling pipes with various defects in CATIA and surface recording was simulated similar to ROV recording. Then these videos were automatically converted into image frames, pre-processed and fed into system as training material. After sufficient training, the system was able to detect and distinguish the pipeline defects. GoogLeNet was identified as the network with the highest mean accuracy of 99.87%, hence was finalised as the systems network architecture. MATLAB 2017b was used to develop the system. To further evaluate the performance of the system, a mini lab rig was set up replicating underwater environment with pipeline models with dents, holes and cracks. Similarly, the inspection videos were recorded and the system was able to detect and distinguish the defects on the pipeline alongside their location and percentage coverage with mean accuracy of 99.87% as well, proving the functionality of the system in real condition. The mechanical properties of the pipelines and characterisation of pipeline defects were also reviewed thoroughly before developing the inspection system.

Image processing system is a system that has been developed to date. By using an image processing system, an image can be processed into the required information. In this study, an image processing system was created using the You Only Look Once (YOLO) algorithm which is a part of the Convolutional Neural Network (CNN) [1]. This system can be applied to electric wheelchairs, especially for detecting objects from the road surface they pass. The system that will be created is to detect objects in front of the wheelchair using image processing with the You Only Look Once (YOLO) algorithm method for the safe movement of electric wheelchairs. So when the wheelchair is running, it will simultaneously detect obstacles in front of it, both from the condition of the road surface and objects in front that will block the path of the electric wheelchair. Then, the detection results will be displayed on the monitor screen in front of the driver. As a result, detection can be used for braking control of the movement of electric wheelchairs to support security systems, but here the focus is more on object detection.