Techniki analizy obrazu dla potrzeb systemów inspekcji przemys̷lowej Autor:

In this paper, we will derive a phenomenological model of the bidirectional reflectance distribution function of non-LAMBERTIAN metallic materials typically used in industrial inspection. We will show, how the model can be fitted to measured reflectance values and how the fitted model can be used

Abstract. We propose an autonomous control system for Aerial Service Vehicles capable of performing inspection tasks in buildings and industrial plants. In this paper, we present the applicative domain, the high-level control architecture along with some empirical results. The system has been

A research group at the University of Stuttgart has set up an experimental measurement robot for industrial close range inspection. During a test run, the feasibility of a multi-sensor/actor system has been shown. The system uses optical sensors to perform different tasks including object

-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industrial use. After

Laser ultrasonics represents a practical means for fast and efficient inspection of complex components. Depending of the application different system configurations allow to obtain optimized results with this non-contact, fast and flexible inspection technique. Despite the complexity

Abstract – An LED (Light-Emitting Diode) lens with a transparent and curved surface is more difficult to detect surface defects than electronic components by current computer vision systems. The purpose of this research is to apply the block discrete cosine transform (BDCT), Hotelling 2 T statistic, and grey clustering method to detect visual defects of LED lenses. An image with equal sized blocks is converted to DCT domain and some representative energy features of each DCT block are extracted. These energy 2 features of each block are integrated by the T statistic and the suspected defect blocks can be determined by the multivariate statistical method. Then, the grey clustering algorithm is conducted to further confirm the block locations of the real defects. Finally, a simple segmentation method is applied to separate the defect areas. Experimental results demonstrate the defect detection rate of the proposed method is better than those of current techniques.

Approval of the thesis:

Over the last few years the authors have been involved in research aimed at developing a machine vision system for locating and identifying surface defects on materials. The particular problem being studied involves locating surface defects on hardwood lumber in a species independent manner. Obviously, the accurate location and identification of defects is of paramount importance in this system. In the machine vision system that has been developed, initial hypotheses generated by bottom-up processing for defect labeling are verified using top-down processing. Thus, the label verification greatly affects the accuracy of the system. For this label verification, a rule-based approach, and a k-nearest neighbor approach, and a neural network approach have been implemented. An experimental comparison of these approaches together with other considerations have made the neural net approach the preferred choice for doing the label verification in this vision system. 1.

Abstract- Industrial computed tomography (CT) inspection of hardwood logs has some unique requirements not found in other CT applications. Sawmill operations demand that large volumes of wood be scanned quickly at high spatial resolution for extended duty cycles. Current CT scanning geometries