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The purpose of this paper is to describe the procedure for near-automation of the most commonly used manual georeferencing technique in a desktop GIS environment for historic aerial photographs strip in library archives.

Most of the archived historic aerial photography consists of series of aerial photographs that overlap to some extent, as the optimal overlap ratio is known as 60 percent by photogrammetric standard. Therefore, conjugate points can be detected for the overlapping area. The first image was georeferenced manually by six-parameter affine transformation using 2013 National Agriculture Imagery Program images as ground truths. Then, conjugate points were detected in the first and second images using Speeded Up Robust Features and Random Sample Consensus. The ground space coordinates of conjugate points were estimated using the first image’s six parameters. Then the second image’s six parameters were calculated using conjugate points’ ground space coordinates and pixel coordinates in the second image. This procedure was repeated until the last image was georeferenced. However, error accumulated as the number of photographs increased. Therefore, another six-parameter affine transformation was implemented using control points in the first, middle, and last images. Finally, the images were warped using open source GIS tools.

The result shows that historic aerial strip collections can be georeferenced with far less time and labor using the technique proposed compared with the traditional manual georeferencing technique in a desktop GIS environment.

The suggested approach will promote the usage of historic aerial photographs for various scientific purposes including land use and land cover change detection, soil erosion pattern recognition, agricultural practices change analysis, environmental improvement assessment, and natural habitat change detection.

Most commonly used georeferencing procedures for historic aerial photographs in academic libraries require significant time and effort for manual measurement of conjugate points in the object images and the ground truth images. By maximizing the automation of georeferencing procedures, the suggested approach will save significant time and effort of library workforce.

With the suggested approach, large numbers of historic aerial photographs can be rapidly georeferenced. This will allow libraries to provide more geospatial data to scientific communities.

This is a unique approach to rapid georeferencing of historic aerial photograph strips.

Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear, and friction. Diagnosis, treatment and care of pressure ulcers involve high costs for sanitary systems. Accurate wound evaluation is a critical task to optimize the efficacy of treatments and health‐care. Clinicians evaluate the pressure ulcers by visual inspection of the damaged tissues, which is an imprecise manner of assessing the wound state. Current computer vision approaches do not offer a global solution to this particular problem. The purpose of this paper is to use a hybrid learning approach based on neural and Bayesian networks to design a computational system to automatic tissue identification in wound images.

A mean shift procedure and a region‐growing strategy are implemented for effective region segmentation. Color and texture features are extracted from these segmented regions. A set of k multi‐layer perceptrons is trained with inputs consisting of color and texture patterns, and outputs consisting of categorical tissue classes determined by clinical experts. This training procedure is driven by a k‐fold cross‐validation method. Finally, a Bayesian committee machine is formed by training a Bayesian network to combine the classifications of the k neural networks (NNs).

The authors outcomes show high efficiency rates from a two‐stage cascade approach to tissue identification. Giving a non‐homogeneous distribution of pattern classes, this hybrid approach has shown an additional advantage of increasing the classification efficiency when classifying patterns with relative low frequencies.

The methodology and results presented in this paper could have important implications to the field of clinical pressure ulcer evaluation and diagnosis.

The novelty associated with this work is the use of a hybrid approach consisting of NNs and Bayesian classifiers which are combined to increase the performance of a pattern recognition task applied to the real clinical problem of tissue detection under non‐controlled illumination conditions.

We report the findings from a study exploring the experiences of individuals undergoing MRI scanning for research. Semi‐structured interviews took place before and after scanning with 17 participants; 12 were healthy volunteers and five were patients with a diagnosis of remitted depression. Themes of apprehension and curiosity prior to scanning were common in both groups. Patients were often confused about the procedure. Negative feelings were an issue at the outset, characterised by shock related to the physical surroundings, after which positive feelings, for example relaxation, were often experienced, and in the case of patients, learning more about their brain. Written information about imaging was deemed satisfactory; however the ability to ‘experience’ aspects of scanning beforehand was suggested. Scanning may be viewed as a process beginning prior to the procedure itself and involving positive and negative emotions. Increased information, reassurance and a more interactive intervention to reduce anxiety may be beneficial and may improve individuals' experience of this widely used procedure.

The purpose of this paper is to provide a framework for analysing and modelling detailed workflow of image‐guided interventions to facilitate simulation and the re‐engineering process for the development of new procedures in multi‐modal imaging environments.

The methodology presented includes a literature review on workflow simulation in surgery, focussing on radiology environments, an assessment of simulation tools, a data gathering and management framework and research on methods for conceptual modelling of the processes.

The literature review reveals that few authors attempted to analyse the phases within image‐guided interventions, and those that did, only did so partially. The framework developed for this work intends to fill the gap found in the survey. It allows the maintenance and management of large amounts of data, one of the most critical factors when modelling detailed workflow. In addition, selecting the appropriate simulation software plays an important role, saving time in later stages of the project.

The framework presented for endovascular interventions can be extended to other types of image‐guided interventions. Moreover, modelling the workflow processes in a modular way facilitates the re‐engineering process when integrating different imaging modalities during the same procedure.

The purpose of this paper is to present a robotic motion compensation system, using ultrasound images, to assist orthopedic surgery. The robotic system can compensate for femur movements during bone drilling procedures. Although it may have other applications, the system was thought to be used in hip resurfacing (HR) prosthesis surgery to implant the initial guide tool. The system requires no fiducial markers implanted in the patient, by using only non-invasive ultrasound images.

The femur location in the operating room is obtained by processing ultrasound (USA) and computer tomography (CT) images, obtained, respectively, in the intra-operative and pre-operative scenarios. During surgery, the bone position and orientation is obtained by registration of USA and CT three-dimensional (3D) point clouds, using an optical measurement system and also passive markers attached to the USA probe and to the drill. The system description, image processing, calibration procedures and results with simulated and real experiments are presented and described to illustrate the system in operation.

The robotic system can compensate for femur movements, during bone drilling procedures. In most experiments, the update was always validated, with errors of 2 mm/4°.

The navigation system is based entirely on the information extracted from images obtained from CT pre-operatively and USA intra-operatively. Contrary to current surgical systems, it does not use any type of implant in the bone to track the femur movements.

The manufacturing of intelligent and secure visual data transmission over the wireless sensor network is key requirement nowadays to many applications. The two-way transmission of image under a wireless channel needed image must compatible along channel characteristics such as band width, energy-efficient, time consumption and security because the image adopts big space under the device of storage and need a long time that easily undergoes cipher attacks. Moreover, Quizzical the problem for the additional time under compression results that, the secondary process of the compression followed through the acquisition consumes more time.

Hence, for resolving these issues, compressive sensing (CS) has emerged, which compressed the image at the time of sensing emerges as a speedy manner that reduces the time consumption and saves bandwidth utilization but fails under secured transmission. Several kinds of research paved path to resolve the security problems under CS through providing security such as the secondary process.

Thus, concerning the above issues, this paper proposed the Corvus corone module two-way image transmission that provides energy efficiency along CS model, secured transmission through a matrix of security under CS such as inbuilt method, which was named as compressed secured matrix and faultless reconstruction along that of eminent random matrix counting under CS.

Experimental outputs shows intelligent module gives energy efficient, secured transmission along lower computational timing also decreased bit error rate.

Formal quality review processes are a necessary part of any digital imaging workflow. This article illustrates a set of quality review processes implemented in the Indiana University Digital Library Program's Digital Media and Image Center.

A methodology for automatic batch review of large numbers of images is presented, along with rationale and procedures for supplemental visual review. The initial stages of an effort to further automate and centralize image quality control at Indiana University are described.

Automation of checks for objective image criteria, together with formal visual review of a sample of digitized images, is an effective means of implementing a quality review process.

The methodologies described can be used as a model for other institutions performing digital imaging projects of any size.

The recognition and positioning of start welding position (SWP) is the first step and one of the key technologies to realize autonomous robot welding. The purpose of this paper is to describe a method developed to accomplish successful autonomous detection and guiding of SWP.

The images of workpieces are snapped by charge coupled device (CCD) cameras in a relative large range without additional light. The recognized methods of SWP are analyzed according to the given definition. A two‐step method named “coarse‐to‐fine” is proposed to recognize the SWP accurately. The first step is to solve the curve functions of seam and workpieces boundaries by fitting. The intersection point is regarded as initial value of SWP. The second step is to establish a small window that takes the initial value of SWP as centre. Then, the SWP is obtained exactly by corner detection in the window. Both the abundant information of original image and the structured information of recognized image are used according to given rules, which takes full advantage of the image information and improves the recognized precision.

The detected results show that the actual and calculated positions by first step of SWP are identical for regular seam, but different for the irregular curve seam. The exact results can be calculated by the two‐step method in the paper for both regular and irregular seams. The typical planar “S‐shape” and spatial arc curved seams are selected to carry out autonomous guiding of SWP.

The experimental results are given based on the introduction of 3D reconstructed and guided method. The guided precision is less than 1.1 mm, which meets the requirements of practical production. The proposed two‐step method recognizes the SWP rapidly and exactly from coarse to fine.

The purpose of this paper is to optimise parameters of digital image analysis to investigate the deformation behaviour of woven sample and to detect the onset and variation of wrinkling that occurs due to bias‐tensioned fabric buckling.

Using models of predescribed shape, the relationship between the digitized gray scale intensities and wrinkles of the surface are analysed and conditions of specimen illumination and filtering procedures are chosen.

It is proposed to convert acquired images to binary to record the onset of buckling and to estimate critical buckling parameters of stretched woven samples. The threshold value is determined as mean value of approximated histogram of stretched specimen centre line. It is defined that profile curve and gray scale disperse presented by parameter CV can be used to obtain additional information and to compare behaviour of different samples during bias tension.

Proposed image analysis technique allows detection of the onset of buckling wave formation and evaluation of surface waviness changes in woven samples different in colour and weave type tension. However, the behaviour of fabric samples with sharp multicoloured and complicated patterns cannot be assessed by gray scale imaging.

The proposed approach can be adjusted to investigate different wrinkling problems – buckling during simple shearing or picture frame test, seam puckering, draping.