{"title":"图像处理论坛-北京论坛2022","authors":"Thomas Längle, M. Heizmann","doi":"10.1515/teme-2023-0093","DOIUrl":null,"url":null,"abstract":"For many technical applications, obtaining sensory information about objects, a scene or the environment is crucial. These include, for example, determining product quality in quality assurance and sensor-based sorting, sensing the environment for robotics and automated vehicles, and many other tasks in measurement and automation technology. In all of these applications, machine vision systems have key advantages over other sensor principles and over the inspection by humans: The actual observation process—image acquisition—is contact-free, the data have a high information content due to their multi-dimensional nature, and a variety of image acquisition methods can be used to capture very different properties of the scene with high informative value. What is outstanding about machine vision, however, is that it emulates the most important human sense—the visual sense—so that many image processing procedures can be understood relatively easily by humans. On the other hand, technical image acquisition is not bound to the limitations of the human sense of sight (e. g., spectral sensitivity, temporal response, temporal and spatial resolution, reproducibility, objectivity, fatigue). Cameras and the images they capture also play an increasing role in daily life, which is immediately apparent from the omnipresence of smartphones with (now often multiple) cameras. This is accompanied by a high level of maturity in sensor technology and image data processing, which in turn benefits the technical applications of image processing. In machine vision systems, components of various disciplines, including optics, lighting technology, sensor technology, signal processing, system theory, computer science and information technology, interact with each other to","PeriodicalId":56086,"journal":{"name":"Tm-Technisches Messen","volume":"6 1","pages":"407 - 409"},"PeriodicalIF":0.8000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image Processing Forum – Forum Bildverarbeitung 2022\",\"authors\":\"Thomas Längle, M. Heizmann\",\"doi\":\"10.1515/teme-2023-0093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For many technical applications, obtaining sensory information about objects, a scene or the environment is crucial. These include, for example, determining product quality in quality assurance and sensor-based sorting, sensing the environment for robotics and automated vehicles, and many other tasks in measurement and automation technology. In all of these applications, machine vision systems have key advantages over other sensor principles and over the inspection by humans: The actual observation process—image acquisition—is contact-free, the data have a high information content due to their multi-dimensional nature, and a variety of image acquisition methods can be used to capture very different properties of the scene with high informative value. What is outstanding about machine vision, however, is that it emulates the most important human sense—the visual sense—so that many image processing procedures can be understood relatively easily by humans. On the other hand, technical image acquisition is not bound to the limitations of the human sense of sight (e. g., spectral sensitivity, temporal response, temporal and spatial resolution, reproducibility, objectivity, fatigue). Cameras and the images they capture also play an increasing role in daily life, which is immediately apparent from the omnipresence of smartphones with (now often multiple) cameras. This is accompanied by a high level of maturity in sensor technology and image data processing, which in turn benefits the technical applications of image processing. 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Image Processing Forum – Forum Bildverarbeitung 2022
For many technical applications, obtaining sensory information about objects, a scene or the environment is crucial. These include, for example, determining product quality in quality assurance and sensor-based sorting, sensing the environment for robotics and automated vehicles, and many other tasks in measurement and automation technology. In all of these applications, machine vision systems have key advantages over other sensor principles and over the inspection by humans: The actual observation process—image acquisition—is contact-free, the data have a high information content due to their multi-dimensional nature, and a variety of image acquisition methods can be used to capture very different properties of the scene with high informative value. What is outstanding about machine vision, however, is that it emulates the most important human sense—the visual sense—so that many image processing procedures can be understood relatively easily by humans. On the other hand, technical image acquisition is not bound to the limitations of the human sense of sight (e. g., spectral sensitivity, temporal response, temporal and spatial resolution, reproducibility, objectivity, fatigue). Cameras and the images they capture also play an increasing role in daily life, which is immediately apparent from the omnipresence of smartphones with (now often multiple) cameras. This is accompanied by a high level of maturity in sensor technology and image data processing, which in turn benefits the technical applications of image processing. In machine vision systems, components of various disciplines, including optics, lighting technology, sensor technology, signal processing, system theory, computer science and information technology, interact with each other to
期刊介绍:
The journal promotes dialogue between the developers of application-oriented sensors, measurement systems, and measurement methods and the manufacturers and measurement technologists who use them.
Topics
The manufacture and characteristics of new sensors for measurement technology in the industrial sector
New measurement methods
Hardware and software based processing and analysis of measurement signals to obtain measurement values
The outcomes of employing new measurement systems and methods.