A single composite image with an extended intensive range is generated by combining disjoining regions from different images of the same scene. The set of images is obtained with a charge-couple device (CCD) set for different flux integration times. By limiting differences in the integration times so that the ranges of output pixel values overlap considerably, individual pixels are assigned the value measured at each spatial location that is in the most sensitive range where the values are both below saturation and are most precisely specified. Integration times are lengthened geometrically from a minimum where all pixel values are below saturation until all dark regions emerge from the lowest quantization level. the method is applied to an example scene and the effect the composite images have on traditional low-level imaging methods also is examined. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-96. This technical report is available at ScholarlyCommons: https://repository.upenn.edu/cis_reports/248 Extended Intensity Range Imaging MS-CIS-93-96 GRASP LAB 366

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Extended Intensity Range Imaging

This paper describes work which has been carried out to investigate a rotating stereoscopic imaging system consisting of two line-scan cameras for measurement. Algorithms have been proposed and experimentally tested which allow three-dimensional co-ordinates to be derived from a defined object workspace. The use of stereoscopic imaging is not new; however, what is novel in this approach is the method used to produce panoramic images of the workspace surrounding the rotating line-scan camera. This system is particularly suited to applications which require the `all-round' observation of a scene. These include autonomous vehicle guidance and path-planning for complex manipulator manoeuvres. The results indicate that the positions of targets can be determined, at best, to a measurement accuracy of within mm in the X axis (horizontal), mm in the Y axis (vertical) and mm in the Z axis (range) at a camera-to-object range of 1.5 m.

http://iopscience.iop.org/article/10.1088/0957-0233/9/3/005/pdf

3D measurement using rotating line-scan sensors

This thesis addresses the problem of estimating the surface reflection model of objects observed in a terrestrial scene, illuminated by natural illumination; that is, a scene which is illuminated by sun and sky light alone. This is a departure from the traditional analysis of laboratory scenes, which are illuminated by idealised light sources with positions and radiance distributions that are precisely controlled. Natural illumination presents a complex hemispherical light source which changes in both spatial and spectral distribution with time, terrestrial location, and atmospheric conditions.

An image-based approach to the measurement of surface reflection is presented. The use of a sequence of images, taken over a period of time, allows the varying reflection from the scene due to the changing natural illumination to be measured. It is shown that the temporal change in image pixel values is suitable for the parameters of a reflection model to be estimated. These parameters are estimated using regression techniques. Two such regression methods are considered: a traditional non-linear method and the probabilistic approach of simulated annealing. It is shown that simulated annealing provides consistent performance in this application.

This work focuses on the use of physically-based models of illumination, surface reflection and camera response. Using such models allows the system to produce quantitative, as opposed to qualitative, results and allows radiometric measurements to be made from image pixel values. The use of accurate models of daylight illumination allows scenes illuminated by skies of varying atmospheric conditions to be considered. The results obtained by the presented methods may be used for a variety of tasks ranging from object recognition to the automated generation of virtual environments.

Results are presented which show that the proposed method is suitable for the wide variety of camera positions, surface orientations and sky conditions that may be experienced. The method is also shown to be tolerant of image noise and may be used on single or multiple pixels within each image. These results are based on the analysis of synthetic image sequences generated using a validated lighting simulation system. Results are also presented for real data recorded using a camera.

/pdf/surface-reflection-model-estimation-from-naturally-1q1w2ud07t.pdf

Surface reflection model estimation from naturally illuminated image sequences

Non-ideal characteristics of image acquisition systems are discussed. A model is formulated which describes the behavior of an image acquisition system, including effects of the camera, lens and frame grabber. Procedures for measuring the model parameters are developed. A procedure for inverting the model to obtain luminance measurements from frame-grabber values is given, along with a discussion of its limitations. This image correction procedure is tested for two solid-state cameras, and the results are analyzed. System noise measurements provide estimates of the reliability of the results, and the effects of averaging succesive frames to reduce the effects of noise are discussed.

Characterization And Correction Of Image Acquisition System Response For Machine Vision

Experience from actual web inspection systems has shown that the quality of the image is crucial for the performance of the whole inspection system. Even the most sophisticated image processing methods or computer hardware cannot compensate for low quality images. In this paper the key characteristics affecting the image formation and test set-ups to measure the numerical values of these characteristics are introduced. The determination of the imaging properties is based on imaging known test targets and subsequent image analysis. Modulation transfer function, vignetting, noise and pixel-to-pixel non-uniformity values for typical image forming systems are given. The sensitivity of these characteristics is estimated by measuring their numerical values as a function of some key imaging parameters like f-number or the spectral range of illumination. The information content of defective surface samples picked from a steel manufacturing process is compared to the results of the test set-up measurements to demonstrate the relation between the quality of the image formation in web inspection and the measured imaging characteristics. As a conclusion, an image formation evaluation procedure is proposed. The possibility to optimize the performance of the image formation using the information from the evaluation procedure is discussed. 

Key words: visual surface inspection, web inspection, imaging.

Evaluation procedure for imaging in web inspection

Solid state (CCD, CID, or multiplexed photosensor) television cameras are the most widely used input devices in machine vision, because they are relatively inexpensive, rugged, and reliable. However, the design, specification, and testing of these cameras typically are geared to their primary use in producing images that will ultimately be observed by humans; the intended applications for these cameras are as diverse as parking lot security and home entertainment. Because the video information produced by the camera is not used in the same ways by people and machine vision systems, there is no a priori reason to expect that a camera designed for one use will be optimal for another. In our work we have examined what makes a camera suitable for machine vision use. This paper describes which characteristics are important to the camera's performance machine vision applications and why. We show how these characteristics can be measured and standardized using simple tests suitable for production screening or more extensive tests suitable for use in the laboratory. Tests for important camera characteristics, including transfer function, noise, and resolution, are described and test results for representative solid state cameras are presented. Finally, we discuss how such measurements can be useful in designing or selecting the components of a machine vision system: the video capture systems, the cameras, and the image processing algorithms.

Willard Howard

Papers

How to select cameras for machine vision