2 research outputs found
Inexpensive solution for real-time video and image stitching
Image stitching is the process of joining several images to obtain a bigger view of a
scene. It is used, for example, in tourism to transmit to the viewer the sensation of being in another place. I am presenting an inexpensive solution for automatic real time video and image stitching with two web cameras as the video/image sources. The proposed solution relies on the usage of several markers in the scene as reference points for the stitching algorithm. The implemented algorithm is divided in four main steps, the marker detection, camera pose determination (in reference to the markers), video/image size and 3d transformation, and image translation. Wii remote controllers are used to support several steps in the process. The builtâin IR camera provides clean marker detection, which facilitates the camera pose determination. The only restriction in the algorithm is that markers have to be in the field of view when capturing the scene.
Several tests where made to evaluate the final algorithm. The algorithm is able to
perform video stitching with a frame rate between 8 and 13 fps. The joining of the two
videos/images is good with minor misalignments in objects at the same depth of the marker,misalignments in the background and foreground are bigger. The capture process is simple enough so anyone can perform a stitching with a very short explanation.
Although realâtime video stitching can be achieved by this affordable approach,
there are few shortcomings in current version. For example, contrast inconsistency along the stitching line could be reduced by applying a color correction algorithm to every source videos. In addition, the misalignments in stitched images due to camera lens distortion could be eased by optical correction algorithm.
The work was developed in Appleâs Quartz Composer, a visual programming environment. A library of extended functions was developed using Xcode tools also from Apple.Orientador: MonâChu Che
Uncertainty Modeling in Robotic Maintenance Using Computer Vision
RĂSUMĂ Lâinspection visuelle automatisĂ©e est une technologie en expansion dans le domaine industriel, notamment pour lâidentification et la rĂ©paration de piĂšces aĂ©ronautiques critiques dĂ©fectueuses. Pour mesurer des dĂ©fauts de surface, des images prises par un ou plusieurs systĂšmes dâacquisition sont traitĂ©es. Le traitement de ces images requiert de nombreux algorithmes de dĂ©tection, de recalage, de segmentation et de classification des dĂ©fauts observĂ©s. Dans le cas dâune acquisition robotisĂ©e, la cinĂ©matique du robot doit Ă©galement ĂȘtre connue et prise en compte. Tous ces systĂšmes nĂ©cessitent une Ă©tape prĂ©liminaire dâĂ©talonnage pour pouvoir extraire des informations prĂ©cises des images capturĂ©es. Chaque Ă©tape dâun tel procĂ©dĂ© possĂšde plusieurs sources dâincertitude dues Ă des facteurs internes ou externes. LâĂ©valuation de ces incertitudes permet de quantifier la qualitĂ© dâune mesure et dâidentifier les parties dâun processus ayant une sensibilitĂ© accrue. Devant la multitude de systĂšmes prĂ©sents dans une technologie dâinspection, lâobjectif de ce projet est dâestimer les incertitudes associĂ©es au procĂ©dĂ© dâacquisition 2D dâAV&R, i.e. lâĂ©talonnage dâune camĂ©ra et des algorithmes de dĂ©tection de dĂ©fauts associĂ©s. Les piĂšces dâinspection utilisĂ©es dans ce projet sont des aubes de soufflante. Plus particuliĂšrement, seuls les dĂ©fauts proches du bord dâattaque et du pied de lâaube sont considĂ©rĂ©s, zone considĂ©rĂ©e comme critique. Huit acquisitions sont prises pour capturer lâensemble de la zone critique de lâaube. Pour Ă©valuer les incertitudes des deux mĂ©thodes dâĂ©talonnage de camera dâAV&R, la mĂ©thode de Monte Carlo est utilisĂ©e. DĂ» Ă des contraintes de correspondance de rĂ©fĂ©rentiels et aux coĂ»ts liĂ©s Ă lâimplĂ©mentation de cette mĂ©thode, lâalgorithme dâĂ©talonnage du focus et de la profondeur de champ est adaptĂ© Ă un langage de programmation plus appropriĂ©. Une mĂ©thode dâestimation de la pose de la camĂ©ra pour des points coplanaires, appelĂ©e Pose from Orthography and Scaling with Iterations (POSIT), est implĂ©mentĂ©e pour un setup similaire Ă celui dâAV&R afin de proposer une alternative Ă leur seconde mĂ©thode dâĂ©talonnage. Parmi toutes les mĂ©thodes utilisĂ©es dans la routine de dĂ©tection, un algorithme dĂ©terministe de dĂ©tection de contours appelĂ© Slope Detection est trĂšs sensible. En particulier, trois paramĂštres clĂ©s jouent un rĂŽle important dans la variation du nombre de dĂ©fauts dĂ©tectĂ©s et du nombre de fausses dĂ©tections : la taille du filtre, le seuil dâintensitĂ© et la taille minimale de dĂ©fauts dĂ©tectĂ©s. La corrĂ©lation entre les deux sorties de dĂ©tection et les trois paramĂštres sensibles est quantifiĂ©e pour une acquisition. Lâimportance de chacun des paramĂštres est Ă©valuĂ©e Ă lâaide dâune rĂ©gression par forĂȘt dâarbres dĂ©cisionnels. Les incertitudes des algorithmes de dĂ©tection dĂ©pendent des incertitudes dâĂ©talonnage de la camĂ©ra. Ătant donnĂ© que lâensemble des incertitudes dâĂ©talonnage nâest pas connu, une sĂ©rie dâimages obtenues aprĂšs Ă©talonnage est utilisĂ©e pour Ă©valuer lâincertitude de chaque pixel en prenant lâĂ©cart-type de lâintensitĂ© comme incertitude standard. Cela permet donc de tenir en compte les variabilitĂ©s prĂ©sentes au sein des processus dâĂ©talonnage de la camĂ©ra dans lâĂ©valuation des incertitudes de dĂ©tection. Lâincertitude du nombre de dĂ©fauts dĂ©tectĂ©s et du nombre de fausses dĂ©tection fournis par Slope Detection est Ă©valuĂ©e au moyen de simulations de Monte Carlo.----------ABSTRACT The automated visual inspection is a fast growing technology used in many industrial applications such as for the identification and the repair of damaged critical aeronautical parts. Measuring surface defects consists in taking images with one or several acquisition systems in order to apply digital image processing techniques. These methods use many different algorithms from detection and image registration to segmentation and classification of defects. For a robot-based acquisition, the robot kinematics must be taken into account. The aforementioned systems need a preliminary step of calibration to extract accurate information from the acquired images. Each step of this technology contains several sources of uncertainty due to internal and external factors. The evaluation of those uncertainties allows to quantify the quality of a measurement and identify the parts of the process having higher sensitivity to errors. Considering this large number of systems present in an inspection technology, the main objective of this project is to the estimate the uncertainties associated to the 2D acquisition process of AV&R, i.e. the camera calibration and the related defect detection algorithms. The inspection parts used in this project are fan blades. In particular, the assessment of detection uncertainties is focused on the defects near the leading edge and the root of the blade, the most critical zone. Eight acquisitions are acquired to cover the entire zone. To evaluate the uncertainty of both calibration methods developed by AV&R, the Monte Carlo method is used. Due to technical constrains on reference frames correspondence and the costs involved in the implementation of this method, the focus and depth of field calibration algorithm is adapted to a suitable programming language able to run Monte Carlo simulations. A camera pose estimation algorithm, called Pose from Orthography and Scaling with Iterations (POSIT), is implemented on a setup similar to the one used by AV&R in order to propose an alternative to their second calibration method. Among all the methods involved in the detection routine, a deterministic edge detection algorithm called Slope Detection is highly sensitive. In particular, three key parameters play an important role in the number of detected defects and the number of false detections: the filter size, the intensity threshold and the minimum size of detected defects. The correlation between both detection outputs and the three sensitive parameters is quantified for one particular acquisition. The importance of each variable is also estimated using a random forest regressor. The uncertainty of the detection algorithms relies on the uncertainty of the camera calibration methods. Since not all calibration uncertainties are known, a series of acquired post-calibration images is used to assess the uncertainty of each pixel considering the intensity standard deviation as the standard uncertainty. Therefore, the variability present within the camera calibration methods is taken into account in the evaluation of the detection uncertainties. The uncertainty of the detected defects and the number of false detections provided by Slope Detection is evaluated using Monte Carlo simulations