Camera and light placement for automated assembly inspection

Includes bibliographical references.Visual assembly inspection can provide a low cost, accurate, and efficient solution to the automated assembly inspection problem, which is a crucial component of any automated assembly manufacturing process. The performance of such an inspection system is heavily dependent on the placement of the camera and light source. This article presents new algorithms that use the CAD model of a finished assembly for placing the camera and light source to optimize the performance of an automated assembly inspection algorithm. This general-purpose algorithm utilizes the component material properties and the contact information from the CAD model of the assembly, along with standard computer graphics hardware and physically accurate lighting models, to determine the effects of camera and light source placement on the performance of an inspection algorithm. The effectiveness of the algorithms is illustrated on a typical mechanical assembly.This work was supported by National Science Foundation grant number CDR 8803017 to the Engineering Research Center for Intelligent Manufacturing Systems, National Science Foundation grant number MIP93-00560, an AT&T Bell Laboratories PhD Scholarship, and the NEC Corporation

AN INTELLIGENT SYSTEM FOR THE DEFECT INSPECTION OF SPECULAR PAINTED CERAMIC TILES

Product visual inspection is still performed manually or semi automatically in most industries from simple ceramic tile grading to complicated automotive body panel paint defect and surface quality inspection. Moreover, specular surfaces present additional challenges to conventional vision systems due to specular reflections, which may mask the true location of objects and lead to incorrect measurements. Some sophisticated optical inspection methods have already been developed for high precision surface defect inspection in recent years. Unfortunately, most of them are highly computational. Systems built on those methods are either inapplicable or costly to achieve real-time inspection. This thesis describes an integrated low-cost intelligent system developed to automatically capture and extract regular defects of the ceramic tiles with uniformly colored specular coatings. The proposed system is implemented on a group of smart cameras using its on-board processing ability to achieve real-time inspection. The results of this study will be used to facilitate the design of a robust, low-cost, closed-loop inspection system for a class of products with smooth specular coatings. The experimental results on real test panels demonstrate the effectiveness and robustness of proposed system

Gyroscope Pivot Bearing Dimension and Surface Defect Detection

Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective

CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition

Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two worlds. A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information. The proposed model is fast to compute and allows for the integration of all intrinsic components. To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created. The evaluation results demonstrate that the new model outperforms existing methods. Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges. Datasets, models and higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201

Deep Reflectance Maps

Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM

CHARACTERIZATION OF SEED DEFECTS IN HIGHLY SPECULAR SMOOTH COATED SURFACES

Many smooth, highly specular coatings such as automotive paints are subjected to considerable performance demands as the customer expectations for appearance of coatings are continually increasing. Therefore it is vital to develop robust methods to monitor surface quality online. An automated visual assessment of specular coated surface that would not only provide a cost effective and reliable solution to the industries but also facilitate the implementation of a real-time feedback loop. The scope of this thesis is a subset of the inspection technology that facilitates real-time close loop control of the surface quality and concentrates on one common surface defect the seed defect. This machine vision system design utilizes surface reflectance models as a rational basis. Using a single high-contrast image the height of the seed defect is computed; the result is obtained rapidly and is reasonably accurate approximation of the actual height

Automated visual assembly inspection

Includes bibliographical references (pages 699-700).This chapter has discussed an intelligent assembly inspection system that uses a multiscale algorithm to detect errors in assemblies after the algorithm is trained on synthetic CAD images of correctly assembled products. It was shown how the CAD information of an assembly along with fast rendering techniques on specialized graphics machines can be used for the automation of the work-cell camera and light placement. The current emphasis in the manufacturing industry on concurrent engineering will only cause this integration between the CAD model of products and its manufacturing inspection to grow in value