Pascual Campoy

Photogrammetric determination of the location and orientation of a group of cameras for a perspective transformation on a new autostereoscopic display

This paper presents a methodology for determining the spacial location and orientation of a multi-viewpoint image set (MVIS) in relation to an absolute coordinate system for their projection on a new autostereoscopic display based on the angular image differentiation system patented by Montes. The different points of view from which a real scene is acquired are most of the time completely unknown a priori. To determine such camera locations and orientations, two algorithms based on photogrammetric techniques are applied. The first one named `Numerical plotting of a photo pair consists on the calculation of the relative orientation of two different photographs of the scene. The second algorithm named `Resection in space takes into account the projection of the 3D points onto the rest of the photographs to determine their absolute location and orientation. Once the absolute location and orientation of each of the images of the MVIS is known, a perspective correction is needed before its projection. This is done so because the image perspective deformations can introduce visual distortions that could be appreciated by an observer. For this purpose, a backward warping transformation is applied to each image depending on the positions of both the acquisition coordinate system, calculated through the previously mentioned algorithms, and the reproduction coordinate system.

On-line surface inspection for continuous cast aluminum strip

A general architecture for detecting and analyzing surface defects in aluminum strip is described. Information concerning visual information from the aluminum surface, surface temperature and strip dimensions--profile thickness-is processed jointly by means of an expert system in order to determine the quality level of each aluminum coil produced; control actions over the casting process, derived from this information, are also suggested by an expert system. This paper shows in deep work related to surface image analysis. The data volume to be processed, up to 20 Mbytes/s, has forced up the development of a high parallel architecture for high-speed image processing. A specially suitable lighting system has been developed for enhancing matrical image acquisition from metallic surfaces that includes reflect avoidance as well as uniform incident angle of light along the scanned portion of surface--about 120000 square mm.

Calibration system for a new 3D autostereoscopic device based on angular differentiation

This paper presents the calibration of a new autostereoscopic system called realvisor, where the image seen at each point of the screen depends on the angle from which the point is seen. In the developed prototype 40 images of the same 3D scene, which are projected through 8 objectives and 5 separate flat mirrors, should match to each other on the back of the screen. To obtain the 3D effect, the corresponding points of each image should be projected on the same place on the screen. This fact cannot be achieved without a calibration of the whole system and the subsequent correction of the images due to different distortions suffered by the images in two stages: (1) non-linear deformations produced by the film register and (2) a lack of optical and mechanical accuracy in the manufactured system. The methodology used for this non-uniform deformation of the pixels within each image is based on computing one table for each image, which indicates the correspondence between the pixels of the ideal image and the distorted one on the screen. These tables calibrate the system and are used for correcting all mentioned distortions. This technique has provided excellent results with the first prototype, obtaining a matching accuracy of less than 1 mm on the screen, enough for achieving the visual concordance and the subsequent 3D effect.

Classification techniques based on AI application to defect classification in cast aluminum

This paper describes the Artificial Intelligent techniques applied to the interpretation process of images from cast aluminum surface presenting different defects. The whole process includes on-line defect detection, feature extraction and defect classification. These topics are discussed in depth through the paper. Data preprocessing process, as well as segmentation and feature extraction are described. At this point, algorithms employed along with used descriptors are shown. Syntactic filter has been developed to modelate the information and to generate the input vector to the classification system. Classification of defects is achieved by means of rule-based systems, fuzzy models and neural nets. Different classification subsystems perform together for the resolution of a pattern recognition problem (hybrid systems). Firstly, syntactic methods are used to obtain the filter that reduces the dimension of the input vector to the classification process. Rule-based classification is achieved associating a grammar to each defect type; the knowledge-base will be formed by the information derived from the syntactic filter along with the inferred rules. The fuzzy classification sub-system uses production rules with fuzzy antecedent and their consequents are ownership rates to every defect type. Different architectures of neural nets have been implemented with different results, as shown along the paper. In the higher classification level, the information given by the heterogeneous systems as well as the history of the process is supplied to an Expert System in order to drive the casting process.

Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

Computer vision system for three-dimensional inspection

In the manufacturing process certain workpieces are inspected for dimensional measurement using sophisticated quality control techniques. During the operation phase, these parts are deformed due to the high temperatures involved in the process. The evolution of the workpieces structure is noticed on their dimensional modification. This evolution can be measured with a set of dimensional parameters. In this paper, a three dimensional automatic inspection of these parts is proposed. The aim is the measuring of some workpieces features through 3D control methods using directional lighting and a computer artificial vision system. The results of this measuring must be compared with the parameters obtained after the manufacturing process in order to determine the degree of deformation of the workpiece and decide whether it is still usable or not. Workpieces outside a predetermined specification range must be discarded and replaced by new ones. The advantage of artificial vision methods is based on the fact that there is no need to get in touch with the object to inspect. This makes feasible its use in hazardous environments, not suitable for human beings. A system has been developed and applied to the inspection of fuel assemblies in nuclear power plants. Such a system has been implemented in a very high level of radiation environment and operates in underwater conditions. The physical dimensions of a nuclear fuel assembly are modified after its operation in a nuclear power plant in relation to the original dimensions after its manufacturing. The whole system (camera, mechanical and illumination systems and the radioactive fuel assembly) is submerged in water for minimizing radiation effects and is remotely controlled by human intervention. The developed system has to inspect accurately a set of measures on the fuel assembly surface such as length, twists, arching, etc. The present project called SICOM (nuclear fuel assembly inspection system) is included into the R&D program P.I.E. created for electrical companies and is developed jointly with Iberdrola, Tecnatom and Enusa.

Three-dimensional digitizer for the footwear industry

This paper presents a developed system for digitizing 3D objects in the footwear industry (e.g. mould, soles, heels) and their introduction in a CAD system for further manipulation and production of rapid prototypes. The system is based on the acquisition of the sequence of images of the projection of a laser line onto the 3D object when this is moving in front of the laser beam and the camera. This beam projection lights a 3D curve on the surface of the object, whose image is processed in order to obtain the 3D coordinates of every point of mentioned curve according to a previous calibration of the system. These coordinates of points in all the curves are analyzed and combined in order to make up a 3D wire-frame model of the object, which is introduced in a CAD station for further design and connection to the machinery for rapid prototyping.

Quality Control of Ferrite Cores Through Artificial Vision Techniques

Abstract The present paper describes the system developed for detecting visual flaws in ferrite cores through artificial vision techniques. The physical dimension of the inspected parts, their high variety of types and their low cost have strongly determined the developed system. The whole system is described throughout the paper, analyzing specially the lighting system, the image processing algorithms and the image interpretation criteria. The obtained results are also described at the end of the paper.