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The purpose of this paper is to present a low-cost, highly mobile system for performing street-level imaging. Street-level imaging and geo-location-based services are rapidly growing in both popularity and coverage. Google Street View and Bing StreetSide are two of the free, online services which allow users to search location-based information on interactive maps. In addition, these services also provide software developers and researchers a rich source of street-level images for different purposes – from identifying traffic routes to augmented reality applications. Currently, coverage for Street View and StreetSide is limited to more affluent Western countries with sparse coverage throughout south-east Asia and Africa. In this paper, we present a low-cost system to perform street-level imaging targeted towards the congested, motorcycle-dominant south-east Asian countries. The proposed system uses a catadioptric imaging system to capture 360-degree panoramic images which are geo-located using an on-board GPS. The system is mounted on the back of a motorcycle to provide maximum mobility and access to narrow roads. An innovative backwards remapping technique for flattening the images is discussed along with some results from the first 150 km which have been captured from Southern Vietnam.

The design was a low-cost prototype design using low-cost off-the-shelf hardware with custom software and assembly to facilitate functionality.

The system was shown to work well as a low-cost omnidirectional mapping solution targeted toward sea-of-motorbike road conditions.

Some of the pictures returned by the system were unclear. These could be improved by having artificial lighting (currently only ambient light is used), a gyroscope-stabilised imaging platform and a higher resolution camera.

This paper discusses a design which facilitates low-cost, street-level imaging for a sea-of-motorcycle environment. The system uses a catadioptric imaging approach to give a wide field of view without excessive image storage requirements using dozens of cameras.

This paper aims to propose an automatic imaging network design to improve the efficiency and accuracy of automated construction progress monitoring. The proposed method will address two shortcomings of the previous studies, including the large number of captured images required and the incompleteness and inaccuracy of generated as-built models.

Using the proposed method, the number of required images is minimized in two stages. In the first stage, the manual photogrammetric network design is used to decrease the number of camera stations considering proper constraints. Then the image acquisition is done and the captured images are used to generate 3D points cloud model. In the second stage, a new software for automatic imaging network design is developed and used to cluster and select the optimal images automatically, using the existing dense points cloud model generated before, and the final optimum camera stations are determined. Therefore, the automated progress monitoring can be done by imaging at the selected camera stations to produce periodic progress reports.

The achieved results show that using the proposed manual and automatic imaging network design methods, the number of required images is decreased by 65 and 75 per cent, respectively. Moreover, the accuracy and completeness of points cloud reconstruction is improved and the quantity of performed work is determined with the accuracy, which is close to 100 per cent.

It is believed that the proposed method may present a novel and robust tool for automated progress monitoring using unmanned aerial vehicles and based on photogrammetry and computer vision techniques. Using the proposed method, the number of required images is minimized, and the accuracy and completeness of points cloud reconstruction is improved.

To generate the points cloud reconstruction based on close-range photogrammetry principles, more than hundreds of images must be captured and processed, which is time-consuming and labor-intensive. There has been no previous study to reduce the large number of required captured images. Moreover, lack of images in some areas leads to an incomplete or inaccurate model. This research resolves the mentioned shortcomings.

Closed circuit television (CCTV) imaging is an increasingly used technology and it is now common place for law enforcement to access CCTV footage as an investigative tool to assist in the nomination of a person of interest, or to aid in the prosecution of an offender. The purpose of this paper is to discuss the role of imaging practitioners in the analysis and interpretation of CCTV images within a law enforcement context. It explores and addresses the limitations of CCTV imaging in evidence with a focus on the interpretation of changes in the visual representation of clothing items.

This paper demonstrates the variations observed in four dark toned garments imaged using one CCTV camera with two different recording settings – visible light and near infrared. The device used was installed and operated in a manner comparable to that used in the public domain, the resulting images indicative of those experienced in casework.

The results display a noticeable change to the tonality of each clothing item between the varied recording conditions. These inconsistencies highlight the limitations of layperson analysis and identify the importance of the inclusion of imaging practitioners when interpreting and analysing such images as evidence.

With an abundance of images in the society, layperson interpretation has become common place. Recognising the value of trained imaging practitioners who can assist law enforcement in analysis and interpretation is paramount to ensuring CCTV images as evidence are used appropriately.

Imaging is a really difficult problem when systems are implemented in the near‐field region and real‐time needs. The purpose of this paper is to consider a novel system model and present a narrowband 2‐D imaging algorithm in the near‐field region and under the real time constraint.

The first part of this paper proposes a novel approximation for the round‐trip distance of the near‐field echoed data based on a 2‐D synthetic aperture planar array. The second part of this work proposes a near‐field narrow imaging algorithm based on the above system model. Narrowband waveforms are employed in the array system, so that the range alignment and decoupling in range‐azimuth are not necessary.

The errors of the proposed approximation are much smaller as compared with those of the Fresnel approximation. For example, the errors of the proposed approximation are negligible when the antenna is fixed at (0, 0). In other cases, the errors are decreased by almost 50 percent. Compared with 2‐D plane array, the synthetic aperture plane of the paper reduces the number of antennas. Finally, numerical simulation results verify the validity of the imaging algorithm.

The near‐field difficulty is solved by the adoption of the proposed approximation. The theoretical analysis and simulation results verify the validity of the imaging algorithm in the near‐field region and under the real time constraint.

The evaluation of an implication by Imaging is a logical technique developed in the framework of modal logic. Its interpretation in the context of a ‘possible worlds’ semantics is very appealing for ir. In 1989, Van Rijsbergen suggested its use for solving one of the fundamental problems of logical models of IR: the evaluation of the implication d → q (where d and q are respectively a document and a query representation). Since then, others have tried to follow that suggestion proposing models and applications, though without much success. Most of these approaches had as their basic assumption the consideration that ‘a document is a possible world’. We propose instead an approach based on a completely different assumption: ‘a term is a possible world’. This approach enables the exploitation of term‐term relationships which are estimated using an information theoretic measure.

The use of laser direct imaging (LDI) processing for outer layer circuit pattern generation and the benefits already obtained with this process have encouraged attention to be focussed on the possibility of using laser direct imaging soldermask (LDISM) in the secondary imaging stage of printed circuit board (PCB) fabrication. As feature sizes on advanced interconnects have continued to diminish and the accompanying spacing between circuit features decreases, the requirement for accurately placing and scaling solder dams and apertures has become more critical in order to ensure that a high yield of finished product can be obtained. Consequently the traditional process of creating photographic artworks for soldermask exposure is rapidly becoming a crucial step that can have significant yield implications due to both environmental conditions and registration issues. The use of LDISMs in combination with specially developed LDI exposure systems is an enabling technology which can offer the benefits of a “standard” mask application process and the positional accuracy and individual image scaling required for guaranteeing improved yields for high density interconnect (HDI) panels.

Active employment of additive manufacturing for scaffolds preparation requires the development of advanced methods which can accurately characterize the morphologic structure and its changes during an interaction of the scaffolds with substrate and aqueous medium. This paper aims to use the method of nuclear magnetic resonance (NMR) imaging for preclinical characterization of 3D-printed scaffolds based on novel allyl chitosan biocompatible polymer matrices.

Biocompatible polymer scaffolds were fabricated via stereolithography method. Using NMR imaging the output quality control of the scaffolds was performed. Scaffolds stability, polymer matrix homogeneity, kinetic of swelling processes, water migration pathways within the 3D-printed parts, effect of post-print UV curing on overall scaffolds performance were studied in details.

NMR imaging visualization of water uptake and polymer swelling processes during the interaction of scaffolds with aqueous medium revealed the formation of the fronts within the polymer matrices those dynamics is governed by case I transport (Fickian diffusion) of the water into polymer network. No significant difference was observed in front propagation rates along the polymer layers and across the layers stack. After completing the swelling process, the polymer scaffolds retain their integrity and no internal defects were detected.

NMR imaging revealed that post-print UV curing aimed to improve the overall performance of 3D-printed scaffolds might not provide a better quality of the finish product, as this procedure apparently yield strongly inhomogeneous distribution of polymer crosslink density which results in subsequent inhomogeneity of water ingress and swelling processes, accompanied by stress-related cracks formation inside the scaffolds.

This study introduces a method which can successfully complement the standard tests which now are widely used in either additive manufacturing or scaffolds engineering.

This work can help to improve the overall performance of the polymer scaffolds used in tissue engineering.

The results of this study demonstrate feasibility of NMR imaging for preclinical characterization of 3D printed biocompatible polymer scaffolds. The results are believed to contribute to better understanding of the processes vital for improving the design of 3D-printed polymer scaffolds.

The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch.

Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor.

Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom.

The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.

Laser imaging and ranging systems offer 3‐D imaging of video quality at distances four to five times greater than conventional video and photographic systems. The Spotmap system is based on the laser radar approach, using a pulsed green solid state laser as a transmitter and detecting the time‐of‐flight and peak intensity of the reflected pulses. Images and maps of underwater scenes are obtained by scanning the laser beam across the objects in a raster pattern using a two‐axis optical scanner. The system can be used both in linescan and 2D scan modes of operation such that images can be obtained either from the linear movement of the remotely operated vehicle or from a stationary position. The system has a 90∞ cross‐track FOV and is capable of processing 8,000 points per second, which gives an imaging resolution comparable to video at ROV speeds up to 2m/s.

This paper sets out to give an overview about state‐of‐the‐art optical tomographic image reconstruction algorithms that are based on the equation of radiative transfer (ERT).

An objective function, which describes the discrepancy between measured and numerically predicted light intensity data on the tissue surface, is iteratively minimized to find the unknown spatial distribution of the optical parameters or sources. At each iteration step, the predicted partial current is calculated by a forward model for light propagation based on the ERT. The equation of radiative is solved with either finite difference or finite volume methods.

Tomographic reconstruction algorithms based on the ERT accurately recover the spatial distribution of optical tissue properties and light sources in biological tissue. These tissues either can have small geometries/large absorption coefficients, or can contain void‐like inclusions.

These image reconstruction methods can be employed in small animal imaging for monitoring blood oxygenation, in imaging of tumor growth, in molecular imaging of fluorescent and bioluminescent probes, in imaging of human finger joints for early diagnosis of rheumatoid arthritis, and in functional brain imaging.