A Novel Approach for Foreign Substances Detection in Injection Using Clustering and Frame Difference

This paper focuses on developing a novel technique based on machine vision for detection of foreign substances in injections. Mechanical control yields spin/stop movement of injections which helps to cause relative movement between foreign substances in liquid and an ampoule bottle. Foreign substances are classified into two categories: subsiding-slowly object and subsiding-fast object. A sequence of frames are captured by a camera and used to recognize foreign substances. After image preprocessing like noise reduction and motion detection, two different methods, Moving-object Clustering (MC) and Frame Difference, are proposed to detect the two categories respectively. MC is operated to cluster subsiding-slowly foreign substances, based on the invariant features of those objects. Frame Difference is defined to calculate the difference between two frames due to the change of subsiding-fast objects. 200 ampoule samples filled with injection are tested and the experimental result indicates that the approach can detect the visible foreign substances effectively

Characterizing human collective behaviours of COVID-19 in Hong Kong

People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Hong Kong has implemented stringent public health and social measures (PHSMs) to curb COVID-19 epidemic waves since the first COVID-19 case was confirmed on 22 January 2020. People are likely to engage in collective behaviour online during extreme events, such as the COVID-19 crisis, to express their awareness, actions and concerns. Here, we offer a framework to evaluate interactions among individuals emotions, perception, and online behaviours in Hong Kong during the first two waves (February to June 2020) and found a strong correlation between online behaviours of Google search and the real-time reproduction numbers. To validate the model output of risk perception, we conducted 10 rounds of cross-sectional telephone surveys from February 1 through June 20 in 2020 to quantify risk perception levels over time. Compared with the survey results, the estimates of the risk perception of individuals using our network-based mechanistic model capture 80% of the trend of people risk perception (individuals who worried about being infected) during the studied period. We may need to reinvigorate the public by engaging people as part of the solution to live their lives with reduced risk

Discrete differential geometry driven methods for architectural geometry

With the rapid growth of free form architectures, the demand for architectural geometry technologies increases dramatically in recent years. Architectural geometry contains knowledge highly relevant to computer graphics and geometry especially discrete differential geometry. In addition, architectural geometry provides new context for some well-established concepts and brings new challenges and new objectives. In this dissertation, we tackle four challenges in architectural geometry. These four proposed solutions cover various processes including architecture maintenance, architecture construction, architecture texture mapping and architecture decoration. In Chapter 4, we present a data model synchronization method that preserves semantic information across editing operations relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. The implementation synchronized changes in the 3D geometry with a CityGML data model. In Chapter 5, we present a simple yet effective method for constructing 3D self-supporting surfaces with planar quadrilateral (PQ) elements. Starting with a self-supporting surface in triangle mesh, we first compute the principal curvatures and directions of each triangular face using a new approach, yielding more accurate results than existing methods. Then, we smooth the principal direction field to reduce the number of singularities and partition all faces into two groups in terms of principal curvature difference. For each face with small curvature difference, we compute a stretch matrix that turns the principal directions into a pair of conjugate directions. Finally, applying a mixed-integer programming solver to the mixed principal and conjugate direction field, we obtain a planar quadrilateral mesh. Experimental results show that our method is computationally efficient and can yield high-quality PQ meshes that well approximate the geometry of the input surfaces and maintain their self-supporting properties. In Chapter 6, we present a simple and robust algorithm to compute quad layout. We first propose an interpolation strategy to find tracing directions for singularities: given a singularity with index k (which is a multiple of 1/4), there are exactly 4-4k searching directions produced. We then trace integral curves with a rounding strategy to encourage them to go through mesh vertices, which can effectively reduce the number of short segments. Finally, we partition the triangle mesh along the integral curves to extract the quadrilateral patches. Our method does not require any numerical solver and is easy to implement and computationally efficient. Computational results show that our results have consistently fewer quad patches than those of the existing methods. For models with rich geometric details, our method can save up to 50\% patches in the quad layout. In Chapter 7, we present research in contrast-enhanced high-relief modeling. We present a simple and effective method to generate contrast-enhanced high-reliefs. Our key idea is a depth compression function with only two variables for 3D models. In this function, normals and mean curvatures are utilized to enhance the contrast of the resulting high-reliefs. To calculate the two variables in depth compression function, we construct an optimization framework with two objectives, volume minimization and contrast maximization. The variables are obtained when the two terms reach a balance point. Our method narrows down the solution space to only two variables and is easy to implement and produces good-quality high-reliefs. We show results on a range of real 3D models including real-world and synthetic models. In summary, we present four algorithms in architectural geometry. These solutions cover many applications including semantic information update, free form surface construction, surface parameterization and architecture surface decoration. We demonstrate the effectiveness of the proposed methods through extensive evaluation and comparison with the state-of-the-art methods.Doctor of Philosoph

Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture

Accurate motion capture plays an important role in sports analysis, the medical field and virtual reality. Current methods for motion capture often suffer from occlusions, which limits the accuracy of their pose estimation. In this paper, we propose a complete system to measure the pose parameters of the human body accurately. Different from previous monocular depth camera systems, we leverage two Kinect sensors to acquire more information about human movements, which ensures that we can still get an accurate estimation even when significant occlusion occurs. Because human motion is temporally constant, we adopt a learning analysis to mine the temporal information across the posture variations. Using this information, we estimate human pose parameters accurately, regardless of rapid movement. Our experimental results show that our system can perform an accurate pose estimation of the human body with the constraint of information from the temporal domain

Highlight Removal of Multi-View Facial Images

Highlight removal is a fundamental and challenging task that has been an active field for decades. Although several methods have recently been improved for facial images, they are typically designed for a single image. This paper presents a lightweight optimization method for removing the specular highlight reflections of multi-view facial images. This is achieved by taking full advantage of the Lambertian consistency, which states that the diffuse component does not vary with the change in the viewing angle, while the specular component changes the behavior. We provide non-negative constraints on light and shading in all directions, rather than normal directions contained in the face, to obtain physically reliable properties. The removal of highlights is further facilitated through the estimation of illumination chromaticity, which is done by employing orthogonal subspace projection. An important practical feature of the proposed method does not require face reflectance priors. A dataset with ground truth for highlight removal of multi-view facial images is captured to quantitatively evaluate the performance of our method. We demonstrate the robustness and accuracy of our method through comparisons to existing methods for removing specular highlights and improvement in applications such as reconstruction

Dynamic Human Body Modeling Using a Single RGB Camera

In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones

Semantic Parametric Reshaping of Human Body Models

Abstract-We develop a novel approach to generate human body models in a variety of shapes and poses via tuning semantic parameters. Our approach is investigated with datasets of up to 3000 scanned body models which have been placed in point to point correspondence. Correspondence is established by nonrigid deformation of a template mesh. The large dataset allows a local model to be learned robustly, in which individual parts of the human body can be accurately reshaped according to semantic parameters. We evaluate performance on two datasets and find that our model outperforms existing methods