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Two-dimensional (2D) measurement technology has become more popular than before, thanks to the widespread availability of smartphones and smart devices. However, most existing 2D body measurement systems have background constraints and may raise privacy concerns. The purpose of this research was to test the idea of designing a 2D measurement system that works with a color-coded measurement garment for background removal and privacy protection. Clothing consumers can use the proposed system for daily apparel shopping purposes.

A 2D body measurement system was designed and tested. The system adopted a close-fitted color-coded measurement garment and used neural network models to detect the color-code in the garment area and remove backgrounds. In total, 78 participants were recruited, and the collected data were split into training and testing sets. The training dataset was used to train the neural network and statistical prediction models for the 2D system. The testing dataset was used to compare the performance of the 2D system with a commercial three-dimensional (3D) body scanner.

The results showed that the color-coded measurement garment worked well with the neural network models to process the images for measurement extraction. The 2D measurement system worked better at close-fitted areas than loose-fitted areas.

This research combined a color-coded measurement garment with neural network models to solve the privacy and background challenges of the 2D body measurement system. Other researchers have never studied this approach.

Accurate presentation of the rock microstructure is critical to the grain-scale analysis of rock deformation and failure in numerical modelling. 3D granite microstructure modelling has only been used in limited studies with the mineral pattern often remaining poorly constructed. In this study, the authors developed a new approach for generating 2D and 3D granite microstructure models from a 2D image by combining a heterogeneous material reconstruction method (simulated annealing method) with Voronoi tessellation.

More specifically, the stochastic information in the 2D image is first extracted using the two-point correlation function (TPCF). Then an initial 2D or 3D Voronoi diagram with a random distribution of the minerals is generated and optimised using a simulated annealing method until the corresponding TPCF is consistent with that in the 2D image. The generated microstructure model accurately inherits the stochastic information (e.g. volume fraction and mineral pattern) from the 2D image. Lastly, the authors compared the topological characteristics and mechanical properties of the 2D and 3D reconstructed microstructure models with the model obtained by direct mapping from the 2D image of a real rock sample.

The good agreements between the mapped and reconstructed models indicate the accuracy of the reconstructed microstructure models on topological characteristics and mechanical properties.

The newly developed reconstruction method successfully transfers the mineral pattern from a granite sample into the 2D and 3D Voronoi-based microstructure models ready for use in grain-scale modelling.

This paper aims to obtain a texture dental model with real images and improve the rendering effect of the dental model.

The paper proposes a semiautomatic method to construct a realistic dental model with real images based on two-dimensional/three-dimensional (2D/3D) registration. First, a 3D digital dental model and three intraoral images are obtained by a 3D scanner and digital single-lens reflex camera. Second, the camera projection poses for every intraoral images are calculated by using the single-objective optimization algorithm. Third, with camera poses, the preliminary projection texture mapping is performed; besides, the seam between two textures is marked. Finally, the marked regions are fused based on the image pyramid to eliminate obvious seams.

The paper provides a method to construct a realistic dental model. The method can map three intraoral images to the dental model. The experimental results show that the textured dental model without obvious distortion, dislocation and seams is constructed with simple interactions.

The proposed method can be applied to the digital smile design system to improve the communication efficiency between doctors, patients and technicians.

The purpose of this paper is to investigate two-dimensional outer totalistic cellular automata (2D-OTCA) rules other than the Game of Life rule for image scrambling. This paper presents a digital image scrambling (DIS) technique based on 2D-OTCA for improving the scrambling degree. The comparison of scrambling performance and computational effort of proposed technique with existing CA-based image scrambling techniques is also presented.

In this paper, a DIS technique based on 2D-OTCA with von Neumann neighborhood (N_vN) is proposed. Effect of three important cellular automata (CA) parameters on gray difference degree (GDD) is analyzed: first the OTCA rules, afterwards two different boundary conditions and finally the number of CA generations (k) are tested. The authors selected a random sample of gray-scale images from the Berkeley Segmentation Data set and Benchmark, BSDS300 (www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) for the experiments. Initially, the CA is setup with a random initial configuration and the GDD is computed by testing all OTCA rules, one by one, for CA generations ranging from 1 to 10. A subset of these tested rules produces high GDD values and shows positive correlation with the k values. Subsequently, this sample of rules is used with different boundary conditions and applied to the sample image data set to analyze the effect of these boundary conditions on GDD. Finally, in order to compare the scrambling performance of the proposed technique with the existing CA-based image scrambling techniques, the authors use same initial CA configuration, number of CA generations, k=10, periodic boundary conditions and the same test images.

The experimental results are evaluated and analyzed using GDD parameter and then compared with existing techniques. The technique results in better GDD values with 2D-OTCA rule 171 when compared with existing techniques. The CPU running time of the proposed algorithm is also considerably small as compared to existing techniques.

In this paper, the authors focused on using von Neumann neighborhood (N_vN) to evolve the CA for image scrambling. The use of N_vN reduced the computational effort on one hand, and reduced the CA rule space to 1,024 as compared to about 2.62 lakh rule space available with Moore neighborhood (N_M) on the other. The results of this paper are based on original analysis of the proposed work.

Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology.

A novel and effective semi‐automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models.

The physical models made in this way proved to have sufficient quality and shortened the process time considerably.

It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram‐based segmentation method is time‐consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi‐automatic method was developed to select the threshold optimum value easily.

Computer tomography (CT) for 3D reconstruction entails a huge number of coplanar fan‐beam projections for each of a large number of 2D slice images, and excessive radiation intensities and dosages. For some applications its rate of throughput is also inadequate. A technique for overcoming these limitations is outlined.Design methodology/approach – A novel method to reconstruct 3D surface models of objects is presented, using, typically, ten, 2D projective images. These images are generated by relative motion between this set of objects and a set of ten fanbeam X‐ray sources and sensors, with their viewing axes suitably distributed in 2D angular space.Findings – The method entails a radiation dosage several orders of magnitude lower than CT, and requires far less computational power. Experimental results are given to illustrate the capability of the techniquePractical implications – The substantially lower cost of the method and, more particularly, its dramatically lower irradiation make it relevant to many applications precluded by current techniquesOriginality/value – The method can be used in many applications such as aircraft hold‐luggage screening, 3D industrial modelling and measurement, and it should also have important applications to medical diagnosis and surgery.

The purpose of this paper is to present an approach to improve the accuracy of estimating feature points of human body on a vision‐based motion capture system (MCS) by using the variable‐density self‐organizing map (VDSOM).

The VDSOM is a kind of self‐organizing map (SOM) and has an ability to learn training samples incrementally. The authors let VDSOM learn 3D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3D feature point could not be estimated correctly, the VDSOM is used for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. This ability is used to recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them.

Experimental results show that the approach is effective for estimation of human posture robustly compared with the other methods.

The proposed approach is interesting for the collaboration between an MCS and an incremental learning.

Additive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.

The authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.

The direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.

The direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.

This paper aims to propose a novel real-time three-dimensional (3D) model-based work-piece tracking method with monocular camera for high-precision assembly. Tracking of 3D work-pieces with real-time speed is becoming more and more important for some industrial tasks, such as work-pieces grasping and assembly, especially in complex environment.

A three-step process method was provided, i.e. the offline static global library generation process, the online dynamic local library updating and selection process and the 3D work-piece localization process. In the offline static global library generation process, the computer-aided design models of the work-piece are used to generate a set of discrete two-dimensional (2D) hierarchical views matching libraries. In the online dynamic library updating and selection process, the previous 3D location information of the work-piece is used to predict the following location range, and a discrete matching library with a small number of 2D hierarchical views is selected from dynamic local library for localization. Then, the work-piece is localized with high-precision and real-time speed in the 3D work-piece localization process.

The method is suitable for the texture-less work-pieces in industrial applications.

The small range of the library enables a real-time matching. Experimental results demonstrate the high accuracy and high efficiency of the proposed method.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.