Investigating the Possibility of Determining the Sequence Intersecting Lines between LaserJet Printing and Handwriting in Document Examination using Color Measurement Technique

In Questioned Documents Examination, the sequence of crossing lines in the intersection of handwriting and printed area can be important clues for detecting tampered documents. Recognition of such documents is a arduous task and requires people with experience and expertise. In the present work, we investigated the possibility of determining the sequence of intersecting lines between LaserJet printing and handwriting for a series of simulated laboratory specimens in the document examination using color measurement technique. The spectral reflectance curves and color coordinates of some points on and out of the cross lines were compared. Four different commercial ballpoint pens and a black toner LaserJet were used to prepare the specimens. The color change of the intersecting lines was subjectively considered through the captured images and a visual assessment process. It was also objectively determined by determining the color difference values from the colorimetric data in CIELAB and CIELCH color spaces in the visible range. The color change evaluation showed that the order in which printing or handwriting is applied alters colorimetric results. Moreover, the investigations showed small color difference values of less than 2 units between a point of printed area individually, and intersection could be applied as a tolerance limit for pass/fail judgments.

Digital Textile Ink-Jet Printing Innovation: Development and Evaluation of Digital Denim Technology

This research explored the potential for ink-jet printing to replicate the coloration and finishing techniques of traditional denim fabric and standardized the reproduction and evaluation procedure. Although denim fabric is widely consumed and very popular, one drawback to denim is that the finishing and manufacturing processes are energy and water intensive and can cause environmental hazards as well as generation of pollution through water waste, particularly at the finishing stage. Textile ink-jet printing has the potential to replicate some of the coloration and finishing techniques of traditional denim fabric without negative environmental impacts. A two-phase research project was conducted. In Phase I (P1), an optimal standard production workflow for digital denim reproduction (including color and finishing effects) was established, and six different denim samples were reproduced based on the workflow. In Phase II, an expert visual assessment protocol was developed to evaluate the acceptance of the replicated digital denim. Twelve ink-jet printing, color science, and denim industry experts finished the assessment.

Image Quality Assessment of Low-Dose CT using Hybrid Iterative Reconstruction

Background: X-ray computed tomography is the first imaging technology that supports accurate nondestructive interior image reconstruction of an object from sufficient projection data. Low-dose computed tomography (LDCT) has been considered to relieve the harm to patients caused by X-ray radiation. However, LDCT images can be degraded by quantum noise and streak artifacts. Methods: The objective of the authors’ study is to evaluate the optimal level of the hybrid iterative reconstruction (HIR) that generates images with the best diagnostic quality on different dose and noise levels. HIR with optimizations is proposed to reduce image noise and provide better performance at a low dose. The Catphan R 504 phantom is employed to assess various image qualities (IQ). Results: For any given scanning protocols, there is linear noise reduction and linear increase of contrast-to- noise ratio (CNR) using optimal HIR. The evidence from various module tests demonstrates that the shape of the noise power spectrum is continuously shifted to low frequency with increasing HIR levels compared with that of filtered-back-projection (FBP). This may describe the difference between the human observer performance and features of the ideal low-contrast objects. Conclusion: Optimal HIR is clearly demonstrated to be a superior method for reducing image noise and improving CNR compared to FBP. Optimal HIR also inhibits texture change or spectrum shift compared with the pure IR method. Even though there are continuous noise reduction and CNR increase with HIR at increasing levels, the human observer performance does not seem to improve simultaneously due to coarser noise (low-frequency noise). HIR level 3 to 5 is optimal for their study. It is possible for the optimal HIR to offer equivalent diagnostic IQ at a lower dose compared with FBP at a routine dose.

Multi-UAV Cooperative Task Planning for Border Patrol based on Hierarchical Optimization

In order to improve the patrol efficiency of border patrol drones, based on unmanned aerial vehicle (UAV) border patrol missions in multiple complex environments, this article proposes a whale algorithm based on chaos theory to plan patrol missions for multiple drones. First, according to the terrain the corresponding environmental model is established for the topography and then solved in layers to obtain the number of drones and other information that each base needs to send to the patrol area. Further, the use of drones with cameras and other detection equipment to patrol the scene information and images extract and transfer to the terminal in real time, and further detect suspicious persons and vehicles on the screen. The final simulation results show that the proposed scheme can be effectively applied to the planning of multi-UAV coordinated missions for border patrol.

Smartphones’ Skin Colour Reproduction Analysis for Neonatal Jaundice Detection

In recent years, smartphone-based colour imaging systems are being increasingly used for Neonatal jaundice detection applications. These systems are based on the estimation of bilirubin concentration levels that correlates with newborns’ skin colour images corresponding to total serum bilirubin (TSB) and transcutaneous bilirubinometry (TcB) measurements. However, the colour reproduction capacity of smartphone cameras are known to be influenced by various factors including the technological and acquisition process variabilities. To make an accurate bilirubin estimation, irrespective of the type of smartphone and illumination conditions used to capture the newborns’ skin images, an inclusive and complete model, or data set, which can represent all the possible real world acquisitions scenarios needs to be utilized. Due to various challenges in generating such a model or a data set, some solutions tend towards the application of reduced data set (designed for reference conditions and devices only) and colour correction systems (for the transformation of other smartphone skin images to the reference space). Such approaches will make the bilirubin estimation methods highly dependent on the accuracy of their employed colour correction systems, and the capability of reducing device-to-device colour reproduction variability. However, the state-of-the-art methods with similar methodologies were only evaluated and validated on a single smartphone camera. The vulnerability of the systems in making an incorrect jaundice diagnosis can only be shown with a thorough investigation of the colour reproduction variability for extended number of smartphones and illumination conditions. Accordingly, this work presents and discuss the results of such broad investigation, including the evaluation of seven smartphone cameras, ten light sources, and three different colour correction approaches. The overall results show statistically significant colour differences among devices, even after colour correction applications, and that further analysis on clinically significance of such differences is required for skin colour based jaundice diagnosis.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Improved Identification of Portraiture of the Julio-Claudian Period with Mobile Apps

We present the results of our image analysis of portrait art from the Roman Empire’s Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization’s ancient past and increase appreciation of our diverse cultural heritage.

On-demand Electrohydrodynamic Jetting of an Ethylene Glycol and Water Mixture—System of Controlled Picoliter Fluid Deposition

On-demand electrohydrodynamic jetting also called electrohydrodynamic atomization (EHDA) is a method to jet small amounts of fluid out of a nozzle with a relatively large diameter by switching on and off an electrical field between the nozzle and the substrate. The total amount of volume deposited is up to 5 pL. The set-up consists of a vertically placed glass pipette with a small nozzle directed downward and a flat substrate placed close to the end of the nozzle. Inside the pipette, an electrode is mounted close to the entrance of the nozzle. The electrode is connected to a high voltage power amplifier. Upon switching on the electrical field, the apparent surface tension drops, the meniscus deforms into a cone and fluid starts to flow toward the nozzle deforming the meniscus. At a certain moment the cone reaches the Taylor cone dimensions and from its tip a jet emerges that decomposes into a stream of charged fL droplets that fly toward the substrate. This process stops when the pulse is switched off. After switching off, the meniscus returns slowly to its equilibrium position. The process is controlled by different time constants, such as the slew rate of the power amplifier and the RC time of the electrical circuit composed of the electrical resistance in the fluid contained in the nozzle between the electrode and the meniscus, and the capacitance of the gap between the meniscus and the flat substrate. Another time constant deals with the fluid flow during the growth of the meniscus, directly after switching on the pulse. This fluid flow is driven by hydrostatic pressure and opposed by a viscous drag in the nozzle. The final fluid flow during droplet formation is governed by the balance between the drag of the charge carriers inside the fluid, caused by the current associated with the charged droplets leaving the meniscus and the viscous drag. These different phenomena will be discussed theoretically and compared to experimental results.

DCNN-based Ship Classification using Enhanced Edge Information and Inception Module

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.