![]() ![]() Expansion & shrink processing: Unnecessary projections are cleared and then the original outline of the target is recovered. Example Inspection of the flaws on an iron plate surface The influence of hairlines on the target surface is eliminated to project flaws only. Contrast conversion: Surface image adjusted to better detect flaws. These functions can be used for both monochrome and color images after color binary processing and color shade scale processing have been applied. Machine vision is equipped with a variety of pre-processing functions to optimize images according to their various applications. This method offers stable results for inspection of different patterns or position deviation. Color shade-scale processing creates a gray image based on color information, resulting in a clearly visible, strong gray image on a black background. Pale color patterns are not easily recognizable with conventional gray processing (as shown on the left). Since images are processed with not only brightness but also color information, difficult applications, such as differentiation between gold and silver, are no longer a problem. Color shade processingĬolor shade-scale processing is a method to convert a color image with an enormous amount of data into a 256-level gray image by setting a specified color to be the brightest level(white). "Color shade-scale processing" is a pre-processing method developed to solve problems associated with the tremendously long processing times of color cameras as well as noise interference from excessive information and inconsistent illumination. Improved Profitability Due to Less DowntimeĬurrent demand for machine vision used in high-speed production lines requires a processing time of one-hundredth of a second.Improve Profitability Using Optimum Instruments.Improved Profitability Through Improved Introduction/Maintenance Efficiency.Improved Profitability Through Visualization and Early Upstream Action.Improved Profitability Through Labor Saving and Reliability.Improved Profitability Through Increased Production Takt/Equipment Takt.Selecting the Correct Tools for Inspection.Selecting the Correct Lens and Lighting.3D Vision-Guided Robotics Supporting Bin Picking.Appearance Inspection (Foreign Particles, Flaws, Defects).Presence Inspection (Quantity, Missing Parts).History of Machine Vision Camera Lens Lighting Factory Automation (FA) Traceability.Practical Knowledge Concept of Processing Speed Concept of Minimum Detectable Object Size Concept of Shutter Speed.Software Basics of Appearance Inspection Basics of Dimension Inspection Basics of Position Detection Position Correction Image Enhance Filters.Hardware CCD (Pixel) and Image Processing Basics Basics of Lens Selection Basics of Lighting Selection Effects of Color Cameras and Image Enhancement.
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