CMOS and Image Processing Solutions

A CMOS image sensor converts incoming light into electrical signals that can be processed by a vision controller. The surface of the sensor is made up of a grid of pixels, each containing a photodiode and necessary circuitry. Light passes through a lens and hits the sensor, and each pixel responds by generating an electrical response. The intensity of the response is based on the signal it receives.

Unlike older sensor architectures that rely on shared readout paths, CMOS sensors perform their signal conversion directly at the pixel level, and each pixel converts the light into voltage on a local level, which allows for multiple pixels to be read together. This supports higher read-out speeds and also helps lower power consumption, making CMOS technology a standard across most machine vision systems.

Once the pixels have responded, the sensor outputs digital image data that represents brightness values from across the field of view. The resulting image is a structured set of numerical data that can then be evaluated and analyzed by image processing systems.

The last section of this guide briefly details the method in which light intensity is converted into usable data by each pixel and then transferred to the controller for processing.

Image processing starts once pixel data is captured and transferred. In industrial vision systems, this data helps detect product changes that indicate errors. Defect and stain detection typically compares local pixel regions to surrounding areas to identify deviations.

Rather than interpreting an image as a whole, image processing systems divide the image into smaller regions. Each is evaluated mathematically using brightness values and spatial relationships. Any differences that go beyond a pre-defined threshold are flagged as potential defects, and more advanced image processing techniques may incorporate adaptable thresholds or pattern-based analysis to account for natural variations. These methods help stabilize inspection results without requiring constant manual adjustments.

Image resolution is defined by how much detail a vision system can capture within a given field of view. It is determined by the number of pixels on the sensor and how those pixels are distributed across the inspected area. The density of these pixels directly affects the smallest feature that can be detected and measured. Pixel size also plays a role in image quality as large pixels collect more light, improving signal stability under different lighting conditions. Small pixels increase the spatial detail of an image, but they may require a stronger or more controlled light source.

It is important to note that higher resolution does not automatically lead to better inspection results. If the resolution exceeds what the application requires, then there is merely an increase in image data volume, but it will not improve image performance. Overall, this slows down processing and reduces the system’s efficiency.

Charge-coupled devices (CCD) and CMOS sensors both convert light into electrical signals. However, they differ in how they transfer and process data. CCD sensors move accumulated charge across the sensor to a single output node, an approach that produces uniform signal characteristics, but it increases demand on power supplies and limits the readout speed of the data. CMOS sensors process data at the pixel level and allow faster readout, and support greater integration of supporting circuitry on the sensor itself. This design promotes compact camera setups and simplifies system integration in many machine vision systems, like the VS Series.

CCD sensors still appear in specialized applications that prioritize uniformity or long exposure performance. In most industrial vision systems, however, CMOS sensors are selected for their balance of speed and efficiency.

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Machine vision can detect areas (No. of pixels), positions (point of change in intensity), and defects (change in amount of intensity) with 256-level intensity data per pixel of a CCD image sensor. By selecting systems with higher pixel levels can higher speeds, you can easily expand the number of possible applications for your industry.
The next topic will be “lenses and lighting methods” As image processing needs to detect change of intensity data using calculations, a clear image must be captured in order to ensure stable detection. The next guide will feature use of lenses and lighting methods necessary to obtain a clear image.