	Vision Systems

Machine Vision Systems Overview

Pixels

The video signal sent from a camera includes brightness/ darkness information that changes with time. However, the time (position on the screen) cannot be determined with this signal. When a scanning line is divided up based on a clock pulse with a specified frequency as shown below, the horizontal position on the screen can be determined. Since the vertical position is originally determined by the number of the scanning line, the screen is divided like a grid. Each element in the grid is called a pixel. A target image is recognized as a combination of white and black pixels. All processes are performed based on pixels.

Binary digital conversion

A video signal generated by a camera starts out as an analog signal. To use the video signal for various inspections and measurements, the analog signal must be converted into a digital signal. To convert from analog to digital, a threshold level is set for the video signal. The areas brighter than the threshold level are defined as “white” and the areas darker than the binary level are defined as “black.” Digital signals corresponding to a white pixel are defined as “1” (= HI), and those corresponding to a black pixel are defined as “0” (= LO).

Grayscale processing

In addition to the binary conversion method, the grayscale processing method is also used in image processing devices. The CV Series employs the grayscale processing method, which is based on the brightness graduation data of the image captured by the camera. The binary conversion method recognizes only white or black (1 or 0) data. The grayscale processing method divides the brightness graduation into 8 bits (256 levels), and obtains a differentiation result based on all the data. Therefore this method offers more shade resolution and therefore more accurate detection.

Greyscale processing versus binarization

Color processing (Color binary conversion by color extraction)

The color video signal from the camera is converted into RGB digital data by the A/D conversion of the image. This data is used for differential operation to obtain data of R- (minus) G, B-G and R-B from the received RGB data. These six color information parameters are used to check the matching degree with the color specified. This is achieved by setting the range on the screen and then extracting the color that matches the one previously specified. Then, each pixel is binary-converted into an extracted pixel or an unextracted one. This differential operation process ensures a stable extraction even for dark colors and high-speed processing.

Color Shade-Scale processing

Color information data is divided into 256 levels.

Based on the extracted color, colors are divided into 256 levels. The extracted color is specified as level 255, and other colors with a greater difference in color shade data from the extracted color are specified as closer to level 0. Unlike color binary conversion, color Shade-Scale processing utilizes 256-level shade data, and therefore this processing ensures stable detection even when the color of a target varies due to individual differences. Like color binary conversion, the six color parameters are used for internal operation.

Example

An example showing red (R) is explained below. (The same explanation applies to the other color parameters.)

Features of color Shade-Scale processing

	Even when the ambient brightness changes or the color of a target varies due to individual differences, color Shade-Scale processing ensures more stable detection than binary conversion.
	Since color Shade-Scale processing utilizes 256- level data, it is more effective for position measurement than binary conversion. (Sub-pixel processing is possible.)

Filter function

The filter function removes or enhances the noise and distortion of the captured image data to increase contrast. The filter allows for conversion of the original image data into a simpler form.

EXPAND

The target pixel is replaced with the pixel that is the brightest among the target pixel and surrounding eight pixels. This filter is useful for restoring any parts of letters that are missing. A white image becomes one size larger.

SHRINK

The target pixel is replaced with the pixel that is the darkest among the target pixel and surrounding eight pixels. This filter removes fine noise components. A white image becomes one size smaller.

Filter processing uses a 3 x 3 pixel area around the target pixel.
EXPAND filter diagram

The target pixel is at the center of the 3 x 3 pixel area.

Example



Before	After (EXPAND)	After (SHRINK)

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