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Acivs 2012 Advanced Concepts for Intelligent Vision Systems |
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Sept. 4-7 2012 Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic |
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Acivs 2012 Abstracts
Invited papers
Paper 184: Machine Vision Solutions for the Forest Industry
This invited talk summarizes computer vision based research and applications for forest industry developed by Machine and Pattern Recognition Laboratory (MVPR) at Lappeenranta University of Technology (LUT) in Finland. The main focus is on the paper and board making industry. Our approach is application-oriented based on practical industrial needs. Typically industrial manufacturing consists of several process steps. At each step it is important to recognize important phenomena affecting production, to measure these phenomena, and finally, to analyze these measurements for the further control of the production. In this presentation it is shown how machine vision can be used for vision-based quality management in the papermaking industry. The objective of the research is the overall management of the whole papermaking process and the quality assessment of the paper-based end product before and after printing. The general goal is that the production is resource-efficient and environmentally sound, using less raw material, water, and energy.
Paper is a challenging media to use due to paper characteristics which affect very much printing quality. Thus, it is important to predict the quality of printing on paper or board, especially in case of images. It is necessary that printed materials look good enough to a consumer. For example, advertisement must obtain positive attention and a high-quality journal to be comfortable to read. Thus, a paper manufacturer should know which kind of quality it offers to a printing house. The quality should not be too high or too low but just sufficient for a known purpose, i.e., so called the wanted quality. Solving this problem leads to the need of the quality assessment before printing and after printing. In the both cases the visual quality assessment is usually done by manually or semiautomatically either observing manufacturing processes or test prints. In this presentation, machine vision solutions are considered where the quality prediction is performed using automatic image processing and analysis systems, without the human interaction, used in the different process steps of pulping, papermaking, and printing. The results obtained from industrial research projects consist of on-line control solutions in industrial manufacturing, off-line laboratory level tests, and frameworks for modeling connections between human perception and physical measurements based on the overall visual quality index of an image or on the regions of interest in an image.
Paper 185: Vision Realistic Rendering
Vision-realistic rendering (VRR) is the computer generation of synthetic images to simulate a subject's vision, by incorporating the characteristics of a particular individual’s entire optical system. Using measured aberration data from a Shack-Hartmann wavefront aberrometry device, VRR modifies input images to simulate the appearance of the scene for the individual patient. Each input image can be a photograph, synthetic image created by computer, frame from a video, or standard Snellen acuity eye chart -- as long as there is accompanying depth information. An eye chart is very revealing, since it shows what the patient would see during an eye examination, and provides an accurate picture of his or her vision. Using wavefront aberration measurements, we determine a discrete blur function by sampling at a set of focusing distances, specified as a set of depth planes that discretize the three-dimensional space. For each depth plane, we construct an object-space blur filter. VRR methodolgy comprises several steps: (1) creation of a set of depth images, (2) computation of blur filters, (3) stratification of the image, (4) blurring of each depth image, and (5) composition of the blurred depth images to form a single vision-simulated image.
VRR provides images and videos of simulated vision to enable a patient's eye doctor to see the specific visual anomalies of the patient. In addition to blur, VRR could reveal to the doctor the multiple images or distortions present in the patient's vision that would not otherwise be apparent from standard visual acuity measurements. VRR could educate medical students as well as patients about the particular visual effects of certain vision disorders (such as keratoconus and monocular diplopia) by enabling them to view images and videos that are generated using the optics of various eye conditions. By measuring PRK/LASIK patients pre- and post-op, VRR could provide doctors with extensive, objective, information about a patient's vision before and after surgery. Potential candiates contemplating surgery could see simulations of their predicted vision and of various possible visual anomalies that could arise from the surgery, such as glare at night. The current protocol, where patients sign a consent form that can be difficult for a layperson to understand fully, could be supplemented by the viewing of a computer-generated video of simulated vision showing the possible visual problems that could be engendered by the surgery.
Paper 186: Anomaly detection in machine perception systems
Anomaly detection in engineering systems is cast as a problem of detecting outliers to the distribution of observations representing a state of normality. We focus on anomaly detection in machine perception. We argue that in addition to outlier detection, anomaly detection in machine perception systems requires other detection mechanisms. They include incongruence detection, data quality assessment, decision confidence gauging, and model drift detection. These mechanisms are elaborated and their application illustrated on a problem of anomaly detection in a sports video interpretation system.