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Your guide to Machine Vision
Guide Topics
Machine Vision
System parts
Machine Vision Systems
Vision Techniques
Lighting Techniques
Major Illumination Types
Illumination Intensity
Illumination Basics
Specific Lighting Techniques
Polarizing Filters
Information About Lenses
Lens Basics
Lens Selection
Lens Characteristics
Image Processing Techniques
Major Image Processing Methods
Grayscale Edge Detection
Normalized Correlation Search
Binarization Stabilization Technique
Binarization Application Examples
Labeling
COUNT
Examples of Technique Application
Coated Glossy Surface, Engraved Mark Inspection
Detection of Scratches and Chips on a Low Contrast Surface
Detection of the Dimensions and Shape of Transparent Objects
Detection of Concave and Convex, Print on Curved Surface, and Contamination
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Illumination Basics

Diffuse and Regular Reflection
Identifying Non-glossy and Glossy Objects
The surface of an object can reveal various conditions, such as a range of colors and glossiness. Glossiness in particular is important when determining the correct angle for illuminating the target. For example, when light of equivalent illumination intensity is used to irradiate glossy and non glossy surfaces from the same angle. Regular reflection, which reflects at the same angle as the entry angle, is intensified on the glossy area. Diffuse reflection, which diffuses in all directions, is intensified on the non-glossy area.
Exploiting these properties allows the illumination intensity over the glossy area to be adjusted by devising an appropriate illumination angle.
Light and Position of the Camera
The above-mentioned concept can also be applied to image processing. The following figure shows a basic camera setup. Figure A shows the setting that will minimize the influence of glare, while Figure B shows the setting that will emphasize a reflective area.
Positioning the camera to avoid reflections
This may seem complicated in writing. However, as human beings, we subconsciously attempt to view an object from the best angle to capture the image. Therefore, it is much easier if we try to find the ideal positional relationship between the light and our eyes when viewing an object and then apply that to the positional relationship of a camera and light. (Of course, this method may not always work, but it is worth trying because it will resolve most problems.)


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