Sensor for High Speed, High Precision Measurement of 2-D Positions

A sensor system to measure the 2-D position of an object that intercepts a plane in space is presented in this paper. This sensor system was developed with the aim of measuring the height and lateral position of contact wires supplying power to electric locomotives. The sensor comprises two line-scans focused on the zone to be measured and positioned in such a way that their viewing planes are on the same plane. The report includes a mathematical model of the sensor system, and details the method used for calibrating the sensor system. The procedure used for high speed measurement of object position in space is also described, where measurement acquisition time was less than 0.7 ms. Finally, position measurement results verifying system performance in real time are given

Camera calibration from road lane markings

Three-dimensional computer vision techniques have been actively studied for the purpose of visual traffic surveillance. To determine the 3-D environment, camera calibration is a crucial step to resolve the relationship between the 3-D world coordinates and their corresponding image coordinates. A novel camera calibration using the geometry properties of road lane markings is proposed. A set of equations that computes the camera parameters from the image coordinates of the road lane markings and lane width is derived. The camera parameters include pan angle, tilt angle, swing angle, focal length, and camera distance. Our results show that the proposed method outperforms the others in terms of accuracy and noise sensitivity. The proposed method accurately determines camera parameters using the appropriate camera model and it is insensitive to perturbation of noise on the calibration pattern.published_or_final_versio

Practical recommendations for hyperspectral and thermal proximal disease sensing in potato and leek fields

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17 degrees, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop

Calibração de sistema de visão com câmera de linha

O trabalho prevê o desenvolvimento de um algoritmo de calibração de um sistema de visão composto por uma câmera de linha sobre uma esteira, incluindo parâmetros não lineares de distorção da lente. A captura de imagens bidimensionais ocorre, nesse sistema, pelo movimento de objeto sobre a esteira, passando pelo campo de visão da câmera. Cada fotografia registra uma linha que, unidas de forma sequencial, formam uma representação 2D do objeto. O procedimento de calibração explora a identificação do modelo do sis tema por Aprendizado de Máquina, permitindo descrever matematicamente a projeção de objetos 3D em uma imagem. A proposição é: parametrizar e montar o sistema de visão; projetar e confeccionar o padrão de calibração; implementar algoritmo de calibração com tecnologia de Aprendizado Profundo; realizar ensaios de calibração; avaliar os resulta dos em termos de convergência e erro de reprojeção; e revisão do método, discutindo seu impacto e enunciando possibilidades de desenvolvimento futuroThe work envisages the development of a calibration algorithm for a vision system comprised of a line scan camera placed on a conveyor belt, encompassing non-linear lens distortion parameters. In this system, two-dimensional image capture occurs as objects move across the conveyor belt, traversing the camera’s field of view. Each photograph records a line which, when sequentially joined, forms a 2D representation of the object. The calibration procedure leverages Machine Learning to identify the system’s model, enabling the mathematical description of the projection of 3D objects onto an image. The proposition is to parameterize and assemble the vision system; design and create the ca libration pattern; implement the calibration algorithm using Deep Learning technology; conduct calibration tests; evaluate the results in terms of convergence and reprojection error; and review the method, discussing its impact and outlining possibilities for future developmen

External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Line scan camera calibration for fabric imaging

textFabric defects inspection is a vital step for fabric quality assessment. Many vision-based automatic fabric defect detection methods have been proposed to detect fabric flaws efficiently and accurately. Because the inspection methods are vision-based, image quality is of great importance to the accuracy of detection result. To our knowledge, most of camera lenses have radial distortion. So our goal in this project is to remove the radial distortion and achieve undistorted images. Much research work has been done for 2-D image correction, but the study for 1-D line scan camera image correction is rarely done, although line scan cameras are gaining more and wider applications due to the high resolution and efficiency on 1-D data processing. A novel line scan camera correction method is proposed in this project. We first propose a pattern object with mutually parallel lines and oblique lines to each pair of parallel ones. The purpose of the pattern design is based upon the fact that line scan camera acquires image one line at a time and it's difficult for one scan line to match the "0-D" marked points on pattern. We detect the intersection points between pattern lines and one scan line and calculate their position according to the pattern geometry. As calibrations for 2-D cameras have been greatly achieved, we propose a method to calibrate 1-D camera. A least-square method is applied to solve the pinhole projection equation and estimate the values of camera parameter matrix. Finally we refine the data with maximum-likelihood estimation and get the camera lens distortion coefficients. We re-project the data from the image coordinate to the world coordinate, using the obtained camera matrix and the re-projection error is 0.68 pixel. With the distortion coefficients ready, we correct captured images with an undistortion equation. We introduce a term of unit distance in the discussion part to better assess the proposed method. When testifying the undistortion results, we observe corrected image has almost identical unit distance with standard deviation of 0.29 pixels. Compared to the ideal distortion-free unit distance, the corrected image has only 0.09 pixel off the average, which proves the validity of the proposed method.Textile and Apparel Technolog

A Framework for Optical Inspection Applications in Life-Science Automation

This thesis presents possible applications for Computer Vision based systems in the field of Laboratory Automation and applicable camera-based, multi-camera-based or flatbed scanner based imaging devices. A concept of a software framework for CV applications is developed with respect to hardware compatibility, data processing and user interfaces. An application is implemented using the framework. It aims at the detection of low-volume liquids in microtiter plates, a labware standard. Using this algorithm, it is possible to cover a wide range of labware on different imaging hardware

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Vision par ordinateur: outils fondamentaux

National audienceLa deuxième édition revue et augmentée de cet ouvrage présente les outils fondamentaux de la vision par ordinateur dans un langage mathématique accessible aux étudiants de niveau licence en mathématiques ou informatique. Il donne également de nombreux exemples d'utilisation de la vision par ordinateur dans deux domaines de technologie de pointe : la robotique et l'imagerie médicale. L'ouvrage est complété par 185 références bibliographiques commentées tout le long du texte

On Single-scanline Camera Calibration

International audienceA method for calibrating single scanline CCD cameras is described. It is shown that the more classical 2D camera calibration techniques are necessary but not sufficient for solving the 1D camera calibration problem. A model for single scanline cameras is proposed, and a two-step procedure for estimating its parameters is provided. It is also shown how the extrinsic camera parameters can be determined geometrically without making explicit the intrinsic camera parameters. The accuracy of the calibration method is analyzed through an application example