Innovative techniques of photogrammetry for 3D modeling

The goal of this book was to introduce the reader to the recent advances in the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). Our original motivation was the observation that more and more often, measured data in the form of images are being used as part of the simulation pipeline, and that error propagation and uncertainty quantification—starting from images but then moving through the pipeline—are important to understanding the ending result of this scientific or engineering process. To analyze the image processing pipeline, we have presented concepts that enable error propagation to be formulated with a set of basic operations: the idea of stochastic images and corresponding (numerical PDE) operations on those images. From this perspective, one approach commonly used within the engineering literature is to employ Monte Carlo sampling techniques to attempt to quantify the impact of errors and variability on an engineering pipeline. Within the uncertainty quantification world, alternative approaches such as the class of generalized polynomial chaos (gPC) methods, which make assumptions about the ability to approximate the process of interest through polynomial approximations, have gained traction as a way of accelerating the convergence of the error and uncertainty quantification process in a computationally tractable way. In this book, we have shown the results of our exploration of the use of the gPC methodology for image processing and computer vision problems. We have relied on the fact that all the prerequisites of the gPC framework (e.g., finite variance, smoothness in the stochastic space, etc.) are intrinsically satisfied in the image processing context. In particular, we have shown how the generalized polynomial chaos (gPC) approach and its corresponding rules for computation (e.g., sums, products, projections on the gPC space, etc.) lead to straightforward generalizations to image processing and computer vision techniques. We have shown that the gPC methodology, when combined with image processing and computer vision techniques, provides a powerful approach to error propagation and uncertainty quantification, and to accessing system sensitivity.

In this paper, the ball detection and tracking through image processing using python is presented. The Ball Detection and Tracking through Image Processing using Python is a project that aims to identify and track a ball in real-time using computer vision techniques. The project involves the use of a camera to capture the video of the ball, which is then processed by the software using OpenCV library in Python. The project comprises two stages, i.e., ball detection and tracking. In the first stage, the algorithm identifies the ball in the video frame by detecting the circular shape of the ball. The second stage involves tracking the ball's movement by using optical flow techniques. The software is designed to run on a Raspberry Pi or a PC, and it is capable of processing video in real-time. The output of the software is the position of the ball, which is displayed on the screen. The position can also be used to control other devices, such as a robot, to follow the ball's movement. The project has several applications, such as in sports analytics, where it can be used to track the movement of a ball in a game like football or basketball. It can also be used in industrial automation, where it can be used to track the movement of objects on a conveyor belt. Overall, the Ball Detection and Tracking through Image Processing using Python project is an excellent example of the application of computer vision techniques in real-time video processing. It has the potential to revolutionize various industries and open up new possibilities for automation and analytics.

There has been much research on computer vision in last three decades. Computer vision methods have been developed for different situations. One example is a detection of human face. For computers face detection is hard. Faces look different from different viewing directions. Facial expressions affect to the look of the face. Each individual person has a unique face. The lightning conditions can vary and so on.However, face detection is currently possible in limited conditions. In addition, there are some methods that can be used for gender recognition [3], face recognition [5] and facial expression recognition [2].Nonetheless, there has been very little research on how to combine these methods. There has also been quite little research on how to apply these methods in human-computer interaction (HCI).Finding sets of techniques that complement each other in a useful way is one research challenge. There are some applications that take advantage of one or two computer vision techniques. For example, Christian and Avery [1] have developed an information kiosk that uses computer vision to detect potential users from a distance. A similar kiosk has been developed by us in the University of Tampere [4]. There are also some games that use simple computer vision techniques for the interaction. However, there are very few applications that use several computer vision techniques together such as face detection, facial expression recognition and gender recognition. Overall, there has been very little effort in combining different techniques.In my research I develop computer vision methods and combine them, so that the combined method can detect face, recognize gender and facial expressions. After successfully combining the methods, it is easier to develop HCI applications that take advantage of computer vision. Applications that can be used by small group of people are my specific interest. These applications allow me to build adaptive user interfaces and analyze the use of computer vision techniques in improving human-computer interaction.

This paper presents a survey of Internet ofThings (IoT) applications using the Raspberry PI (RPi) SingleBoard Computer (SBC) alongside Computer Vision (CV) techniques from the field of Artificial Intelligence (AI). It presents and compares solutions across several IoTapplication areas, offering an overview of the associated hardware, software, CV methods, and algorithms. The studyexplores IoT applications in the following areas: Smart Healthcare, Face and emotion recognition, Wildlife, Smart Agriculture, Smart Homes, Security, Smart Cities, Autonomous Vehicles, Robotics, Manufacturing, and Retail. Each area is analyzed with respect to the integration of RPiand CV, showcasing their contributions to enhancingoperational efficiency and enabling innovative solutions. The presented solutions use different RPi boards, from RPi 3A+ up to the latest RPi 5.

The article discusses the importance of selfdriving cars to improve road safety and reduce the number of accidents caused by human error. Self-driving cars not only reduce human error but also help reduce driver fatigue. We further explore the use of computer vision in autonomous cars, with previous research relying on deep learning algorithms with LiDAR sensors which can be expensive. The authors propose a more cost-effective approach using simple computer vision algorithms such as color space transformation, Canny edge detection, and Hough line transformation to detect lane lines and steer the car accordingly. This approach requires less operational hardware and can be implemented using affordable boards like Raspberry Pi and Nvidia Jetson Nano. The article also highlights the reconstruction of a remote-controlled car that had a 95% accuracy using a certain set of parameters was a tool for understanding autonomous cars better.

Evaluation of Surface Finish Quality Using Computer Vision Techniques

Abstract: Amidst the rapid evolution of autonomous vehicle technologies, ensuring road safety remains a paramount challenge. Effective detection of lanes and potential hazards, including speed breakers and potholes, is critical for safe autonomous driving. In this study, we introduce an innovative Lane, Speed Breaker, and Pothole Detection System (LSPDS) utilizing YOLOv4 Tiny, a state-of-the-art object detection algorithm and computer vision techniques. Our system integrates computer vision and machine learning techniques for analysis of road conditions. By employing camera sensors, we capture the road scene and apply image processing algorithms to identify lanes, speed breakers, and potholes. Moreover, the system incorporates Firebase for user authentication and SMS services for real-time alerts. YOLOv4 Tiny is employed for accurate detection and classification of these features within the captured images, thereby enhancing the perception capabilities of autonomous vehicles.

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

I. ABSTRACT Fire hazards pose a significant threat to life and property, making early detection crucial for minimizing damage. This project focuses on AI-based fire detection and alarming using computer vision and deep learning techniques. The system employs CCTV cameras or laptop cameras to capture real-time video feeds, which are analysed using Python, OpenCV, and deep learning models to detect fire accurately. A Convolutional Neural Network (CNN) or YOLO (You Only Look Once) object detection model is trained on fire datasets to distinguish fire from non-fire scenarios, reducing false alarms. Once fire is detected, the system triggers an alarm sound and can also send notifications via SMS, email, or IoT-based alerts to concerned authorities. This AI-powered solution is designed for high accuracy and efficiency, making it suitable for smart surveillance, industrial safety, and residential fire monitoring. The integration of edge computing devices like Raspberry Pi or Jetson Nano can further enhance real-time processing. The proposed system aims to improve fire safety measures by providing automated, real-time fire detection, reducing response time, and preventing large-scale disasters. II. KEYWORDS Fire Detection, Real-time Fire Monitoring, Computer Vision (CV).

The number of road potholes is growing day by day due to the increase in vehicles. This is also increasing the number of vehicle accidents caused by lack of road maintenance. In this study, a road pothole detection system using computer vision techniques was conducted. Object detection algorithms such as the YOLO, and SSD were selected. The YOLOv3-SPP model obtained the best mAP of 68.83%, while the YOLOv3-tiny inferred an image in just 0.01s. Testing was also done on an Android device and a Raspberry Pi in order to evaluate performance on embedded systems. The SSDLiteMobileNet v2 converted to a TensorFlow Lite model outperformed all the YOLOv3 models when time is compared. In this paper we show that the model with the best performance is YOLOv3 SPP, but if time is a priority, the SSD TFLite model is the right fit.

Automation of interior, relative, and absolute orientation

Semantic segmentation, as one of the important branches in the field of computer vision, has made significant progress in recent years. However, in the field of shale exploration, the use of such computer vision techniques has not been widely explored. This study aims to fill this gap by proposing a novel visual base model for shale fracture porosity. Aiming at the unique characteristics of shale electron microscope scanning images (SEM), we introduced a fast Fourier transform module (FFT) into the feature extraction network to effectively suppress the high-frequency noise during the imaging process. By collecting and annotating 91 SEM scan images of voids and cracks in marine shale and marine-continental transitional shale, and conducting comparative experiments between our proposed model and seven classical semantic segmentation models, the results show that our method exhibits obvious advantages in both visual and quantitative metrics(4.99%, 4.35% and 5.46% improvements on the marine shale SEM image dataset and 1.90%, 0.96%, 4.81% improvements on the marine-continental transitional shale SEM image dataset). Notably, our method not only performs well in processing raw images but also maintains high segmentation accuracy after random Gaussian noise and pixel loss processing. This feature provides a new technical approach and solution in the field of shale exploration. In summary, this study has made impressive progress by combining advanced computer vision technology with the field of shale exploration. Our work not only provides an efficient and accurate image segmentation method for shale exploration but also brings useful insights and guidance for research and practice in related fields.

The growing demand for sustainable and energy-efficient buildings has highlighted the need for reliable and accurate methods to detect fenestration deterioration and assess UV radiation transmission. Traditional detection techniques, such as spectrophotometers and radiometers, discussed in Part I, are often expensive and invasive, necessitating more accessible and cost-effective solutions. This study, which is Part II, provides an in-depth exploration of the concepts and methodologies underlying UV bandpass-filtered imaging, advanced image processing techniques, and the mechanisms of pixel transformation equations. The aim is to lay the groundwork for a unified approach to detecting ultraviolet (UV) radiation transmission in fenestration glazing. By exploiting the capabilities of digital imaging devices, including widely accessible smartphones, and integrating them with robust segmentation techniques and mathematical transformations, this research paves the way for an innovative and potentially democratized approach to UV detection in fenestration glazing. However, further research is required to optimize and tailor the detection methods and approaches using digital imaging, UV photography, image processing, and computer vision for specific applications in the fenestration industry and detecting UV transmission. The complex interplay of various physical phenomena related to UV radiation, digital imaging, and the unique characteristics of fenestration glazing necessitates the development of a cohesive framework that synergizes these techniques while addressing these intricacies. While extensively reviewing existing techniques, this paper highlights these challenges and sets the direction for future research in the UV imaging domain.

To identify and constrain the target zone for the planned safod Main Hole through the San Andreas Fault (saf) near Parkfield, California, a 32-level three-component (3C) geophone string was installed in the Pilot Hole (ph) to monitor and improve the locations of nearby earthquakes. The orientation of the 3C geophones is essential for this purpose, because ray directions from sources may be determined directly from the 3D particle motion for both P and S waves. Due to the complex local velocity structure, rays traced from explosions and earthquakes to the ph show strong ray bending. Observed azimuths are obtained from P -wave polarization analysis, and ray tracing provides theoretical estimates of the incoming wave field. The differences between the theoretical and the observed angles define the calibration azimuths. To investigate the process of orientation with respect to the assumed velocity model, we compare calibration azimuths derived from both a homogeneous and 3D velocity model. Uncertainties in the relative orientation between the geophone levels were also estimated for a cluster of 36 earthquakes that was not used in the orientation process. The comparison between the homogeneous and the 3D velocity model shows that there are only minor changes in these relative orientations. In contrast, the absolute orientations, with respect to global North, were significantly improved by application of the 3D model. The average data residual decreased from 13° to 7°, supporting the importance of an accurate velocity model. We explain the remaining residuals by methodological uncertainties and noise and with errors in the velocity model.

The design and development of image processing units (IPUs) has traditionally involved trade-offs between cost, real-time properties, portability, and ease of programming. A standard PC can be turned into an IPU relatively easily with the help of readily available computer vision libraries, but the end result will not be portable, and may be costly. Similarly, one can use field programmable gate arrays (FPGAs) as the base for an IPU, but they are expensive and require hardware-level programming. Finally, general purpose embedded hardware tends to be under-powered and difficult to develop for due to poor support for running advanced software. In recent years a new option has surfaced: single-board computers (SBCs). These generally inexpensive embedded devices would be attractive as a platform on which to develop IPUs due to their inherent portability and good compatibility with existing computer vision (CV) software. However, whether their performance is sufficient for real-time image processing has thus far remained an open question. Most SBCs (especially the ultra-low-cost ones which we target) do not offer CUDA/OpenCL support which makes it difficult to port GPU-based CV applications. In order to utilize the full power of the SBCs, their GPUs need to be used. In our attempts at doing this, we have observed that the CV algorithms which an IPU uses have to be re-designed according to the OpenGL support available on these devices. This work presents a framework where a selection of CV algorithms have been designed in a way that they optimize performance on SBCs while still maintaining portability across devices which offer OpenGL ES 2.0 support. Furthermore, this paper demonstrates an IPU based on a representative SBC (namely the Raspberry Pi) along with two CV applications backed by it. The robustness of the applications as well as the performance of the IPU are evaluated to show that SPCs can be used to build IPUs capable of producing accurate data in real time. This opens the possibilities of large scale economically deployment of vision system especially in remote and barren lands. Finally, the software developed as a part of this work has been released open source.