Video reconstructions in dentistry.

Medical image segmentation plays a crucial role in many medical imaging applications by automating or facilating the delineation of anatomical structure and other regions of interest. This paper makes a review on the current segmentation algorithm used for retinal images. These algorithms are divided into three categories according to their main concepts like Edge-based, Region based and Special - theory based Segmentations. The reviewed algorithms are tested using Computer Vision and Image Processing (CVIP) tools.

Shape from Shading (SFS) is a fundamental task in computer vision. By given information about the reflectance of an object’s surface and the position of the light source, the SFS problem is to reconstruct the 3D depth of the object from a single grayscale 2D input image. A modern class of SFS models relies on the property that the camera performs a perspective projection. The corresponding perspective SFS methods have been the subject of many investigations within the last years. The goal of this chapter is to give an overview of these developments. In our discussion, we focus on important model aspects, and we investigate some prominent algorithms appearing in the literature in more detail than it was done in previous works.

This paper presents a model-based vision system for dentistry that will assist in diagnosis, treatment planning, and surgical simulation. Dentistry requires an accurate three-dimensional (3-D) representation of the teeth and jaws for diagnostic and treatment purposes. The proposed integrated computer vision system constructs a 3-D model of the patient's dental occlusion using an intraoral video camera. A modified shape from shading (SFS) technique, using perspective projection and camera calibration, extracts the 3-D information from a sequence of two-dimensional (2-D) images of the jaw. Data fusion of range data and 3-D registration techniques develop the complete jaw model. Triangulation is then performed, and a solid 3-D model is reconstructed. The system performance is investigated using ground truth data, and the results show acceptable reconstruction accuracy.

Advances in both parallel processing capabilities because of graphical processing units (GPUs) and computer vision algorithms have led to the development of deep neural networks (DNN) and their utilization in real-world applications. Starting from the LeNet-5 architecture of the 1990s, modern deep neural networks may have tens to hundreds of layers to solve complex problems such as license plate detection or recognition tasks. In this manuscript, we present a review of the state-of-the-art methods related to automatic license plate recognition. Since deep networks have demonstrated a remarkable ability to outperform other machine learning techniques, we focus only on neural network based license plate recognition methods. We highlight the particular types of networks, i.e., convolutional, residual recurrent, or long-short-term-memory, used for the specific tasks of license plate detection, extraction, or recognition in different existing works. The presented summary also highlights some of the most widely used data sets for comparison and shares the results reported in the reviewed papers. We also give an overview of the effects of fog, motion, or the use of synthetic data on license plate recognition. Finally, promising directions for future research in this domain are presented.

Blurred license plate recognition is one of the most challenging problems in computer vision. A novel license plate recognition framework based on no-negative sparse representation is proposed. In the proposed algorithm, the testing image is represented as a no-negative sparse linear combination of the dictionary, which is preprocessed from standard templates. The proposed framework include two stage: singe character estimation via no-negative block correlation and multiple-characters no-negative sparse representations optimization for refining the recognition results, which simultaneously fulfil the license plate de-blurring and recognition. The experimental results show that it can achieve a high recognition rate.

In the contemporary landscape of smart transportation systems, the imperative role of intelligent traffic monitoring in bolstering efficiency, safety, and sustainability cannot be overstated. Leveraging recent strides in computer vision, machine learning, and data analytics, this study addresses the pressing need for advancements in car license plate recognition within these systems. Employing an innovative approach based on the YOLOv5 architecture in deep learning, the study focuses on refining the accuracy of license plate recognition. A bespoke dataset is meticulously curated to facilitate a comprehensive evaluation of the proposed methodology, with extensive experiments conducted and metrics such as precision, recall, and F1-score employed for assessment. The outcomes underscore the efficacy of the approach in significantly enhancing the precision and accuracy of license plate recognition using performance evaluation of the proposed method. This tailored dataset ensures a rigorous evaluation, affirming the practical viability of the proposed approach in real-world scenarios. The study not only showcases the successful application of deep learning and YOLOv5 in achieving accurate license plate detection and recognition but also contributes to the broader discourse on advancing intelligent traffic monitoring for more robust and efficient smart transportation systems.

In many cases, the Digital Surface Models (DSMs) and Digital Elevation Models (DEMs) are obtained with Light Detection and Ranging (LiDAR) or stereo matching. As an active method, LiDAR is very accurate but expensive, thus often limiting its use in small-scale acquisition. Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors. However, underperformance of stereo matching easily occurs in textureless areas. Accordingly, this study proposed a Shading Aware DSM GEneration Method (SADGE) with high resolution multi-view satellite images. Considering the complementarity of stereo matching and Shape from Shading (SfS), SADGE combines the advantage of stereo matching and SfS technique. First, an improved Semi-Global Matching (SGM) technique is used to generate an initial surface expressed by a DSM; then, it is refined by optimizing the objective function which modeled the imaging process with the illumination, surface albedo, and normal object surface. Different from the existing shading-based DEM refinement or generation method, no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized. Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM (12.5–56.3% improvement) with sound overall shape and temporarily detailed surface compared with a software solution (SURE) for multi-view stereo.

Photometric stereo is a fundamental approach in computer vision. At its core lies a set of image irradiance equations each taken with a different illumination. The vast majority of studies in this field have assumed orthography as the projection model. This paper re-examines the basic set of equations of photometric stereo, under an assumption of perspective projection. We show that the resulting system is linear (as is the case under the orthographic model; Nevertheless, the unknowns are different in the perspective case). We then suggest a simple reconstruction algorithm based on the perspective formulae, and compare it to its orthographic counterpart on synthetic as well as real images. This algorithm obtained lower error rates than the orthographic one in all of the error measures. These findings strengthen the hypothesis that a more realistic set of assumptions, the perspective one, improves reconstruction significantly.

Three-dimensional human face modeling is a very popular topic with many applications,such as facial animation, face recognition and model-based facial video communication. Therefore, how to model the facial geometry, texture intensity and illumination variation is important in computer vision and graphics. Previous works on 3D head modeling from a single face image utilized prior information on 3D head models. However, it is difficult to accurately reconstruct the 3D face model from a single face image with expression since the facial expression induces 3D face model deformation in a complex manner. The main challenge is the coupling of the neutral 3D face model and the 3D deformation due to expression, thus making the 3D model estimation from a single face image with expression very challenging. On the other hand, the illumination condition also makes the problem more difficult. In this thesis,we focus on developing a 3D face model reconstruction system including surface registration and training of 3D face models with expressional deformations as well as the estimation of the 3D neutral shape and the 3D expressional deformation from a single face image. The proposed reconstruction algorithm integrates the linear and non-linear subspace representations for a prior 3D neutral morphable model and the probabilistic manifold-based 3D expressional deformation. We incorporate the face geometry, expression deformation, texture and illumination information into the problem so that it is well constrained. The reconstructed 3D face models can also be further extended and applied to many real-world applications.

Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding

Abstract. Photo-realistic 3D model of object cannot be created by using conventional Total Station method. The aim of this study is to generate 3D building model using UAV’s dense point clouds. In this research, the ability of remotely detected information for the construction of 3D building models is found to be as specific as UAV datasets, Tacheometry Survey and Traverse Survey. Pix4D and MeshLab software were used in this study to perform image processing of 3D building. The building was built up upon using the information needed from capturing UAV images. This procedure requires the height of building to be measured through conventional method such that of UAV and the height is then compared with a 3D building model. At the end of the process, the comparison of height of building, also known as RMSE, was conducted. The images of building were then processed in Photogrammetry which is the Pix4D software. Using Pix4D, this study proceeded with several processing stages such as camera calibration, photo uploading, building of dense cloud, mesh generation, and model texturing. Those processes were done automatically by using Pix4D tools. High quality of photo-realistic 3D model of building was then successfully constructed. The accuracy assessment was done by comparing the measurement of 3D model of building. Direct measuring of building by using total station had taken place to compare the measurements between 3D model details with actual measurements. The measurement of 3D model of building was done in Pix4D software. The RMSE achieved in this study is 0.015 m which is under reasonable accuracy limit. This study proves that UAV point cloud is capable to produce a very fine 3D model.

In this research, we propose a novel algorithm which recovers the surface height directly from images obtained via perspective projection. Most existing shape from shading (SFS) algorithms are developed under the assumption of an orthogonal projection model, which is a special case of the general perspective projection model. As a result, there exist substantial reconstruction errors in their real applications especially for the case when the object is not far away from the camera. By introducing an explicit triangular element surface model and combining perspective imaging geometry, we are able to relax the orthogonal projection assumption and derive a more general reflectance model with perspective projection. We formulate the perspective SFS problem as a minimization problem parameterized by surface nodal heights, and determine the surface height by solving an equivalent linear system of equations. Simulation results for several test images are given to demonstrate the performance of the proposed algorithm. The proposed perspective SFS algorithm gives more accurate results than conventional SFS algorithms and is very attractive in practical applications.

The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.

In the areas of computer vision and computer graphics, analyzing information about three-dimensional objects from two-dimensional images is essential. Shape from shading is a very fundamental problem in computer vision, which uses features and patterns in images to infer the shape of the object in our viewing direction. The influence of solving this problem is significant. For example, a typical application of shape from shading is in astronomy, in which we can obtain landscape features of a planet by analyzing the information from the image we get.

Smart cities must have all the important characteristics to achieve their intended goals. Proper traffic management and controlling, increased surveillance and safety, and enhanced management and avoidance of incidents must be the priorities of smart cities. Meanwhile, license plate recognition (LPR) has become the most debatable topic in the research community due to various real-time applications, such as “law enforcement, toll-free processing, access control, and traffic surveillance.” Automated LPR is a technique based on computer vision to recognize vehicles with their number plates. This study discusses various “Deep Learning based LPR” techniques to detect and identify “alphanumeric characters” in number plate. The projected model works on “license based detection” and “character recognition.” This technique uses “Optical Character Recognition (OCR)” technology to detect and extract the alphanumerical numbers from the license plate. This study is based on secondary data collected from various studies conducted on Licence Plate Recognition using various Deep Learning models published in databases like Google Scholar, Science Direct, NCBI, etc. Deep learning has been used widely in applications related to computer vision in recent years with great perfection. It is a great solution for modern and traditional image processing, object detection, and feature extraction issues. It has been widely used in different stages of LPR like character segmentation, license plate recognition, and OCR.