Image Acquisition Components

By July 29, 2020September 6th, 2022Image Acquisition
Image Acquisition Components

Image Acquisition Components

A machine vision model’s accuracy is one of the most important factors that can help make your product successful and reliable in the market. For this, you have to have robust image acquisition components that can deliver quality images. The speed and overall throughput of an organization’s manufacturing processes are often dependent upon the speed of the machine vision system.  

However, many users still find your machine vision system to be inaccurate and inefficient. Systems work great in a lab with samples but fail to meet the accuracy requirements on the production line. One of the faults responsible for these discrepancies is building the system in a lab and trying to port it directly to a manufacturing environment. That’s the reason this approach fails to produce desirable results lies in the image acquisition process.

What Is An Image Acquisition?

According to Raghava Kashyapa (Machine Vision Expert), In image processing and machine vision, image acquisition is the action of retrieving an image from a source, usually hardware systems like cameras, sensors, etc. It is the first and the most important step in the workflow sequence because, without an image, no actual processing is possible by the system. The image that is acquired by the system is usually completely unprocessed. 

Why is Image Acquisition Important?

In the image acquisition process, incoming light energy from an object is converted into an electrical signal by the combination of sensors that are sensitive to the particular type of energy. These minute subsystems work together to provide your machine vision algorithm with the most accurate representation of the object. 

While the sensor system and cameras mostly rely on the technology available, users have complete control over illumination.

According to buzztech.in In image processing, it is defined as the action of retrieving an image from some source, usually a hardware-based source for processing. It is the first step in the workflow sequence because, without an image, no processing is possible.

Here Are The Major Image Acquisition Components:

Trigger

Usually, modern imaging and vision applications work with triggered image taking. The sensor in an industrial camera is principally continuously exposed as they do not have a mechanical shutter like a single-lens reflex camera.

Machine Vision Sensor

A completely free-running camera reads the input from the sensor permanently. Upon an “image query”, the current image is captured completely. After this, new image acquisition is started and then this completely captured image is transferred to the PC. Sensors, PLC, and push buttons for manual operation can perform these image queries. Triggers also depend on the type of camera you have installed in the system. 

Also Read: CAMERA FUNDAMENTALS IN MACHINE VISION

Camera

In a machine vision system, the cameras are responsible for taking the light information from a scene and converting it into digital information i.e. pixels using CMOS or CCD sensors. Raghava believes that the sensor is the foundation of any machine vision system. Many key specifications of the system correspond to the camera’s image sensor. These key aspects include resolution, the total number of rows, and columns of pixels the sensor accommodates. The higher the resolution, the more data the system collects, and the more precisely it can judge discrepancies in the environment. However, more data demands more processing, which can significantly limit the performance of a system. 

Machine Vision Camera

Based on the image format, cameras could be of three major types:

  • 2D cameras
  • 3D cameras
  • Hyperspectral cameras

Based on the acquisition type, cameras could be classified into two major categories:

  • Line Scan cameras
  • Area scan cameras

While cameras and sensors are crucial, they alone are not sufficient to capture an image.

Optics

Raghava explained that the light from the source has to be focused adequately by a lens on the sensor for it to capture the image with maximum clarity. The lens should provide appropriate working distance, image resolution, and magnification for a vision system. To calibrate magnification precisely, it is necessary to know the camera’s image sensor size and the field of view that is desirable. Some of the most commonly used lenses include:

  • Standard Resolution Lenses

These lenses are optimized for focusing to infinity with low distortion and vignette. 

  • Macro Lenses

Specified in terms of their magnification relative to the camera sensor, they are optimized for ‘close-up’ focusing on negligible distortion. 

  • High-Resolution Lenses

These lenses offer better performance than standard resolution lenses and are suitable for precise measurement applications.

  • Telecentric Lenses

These are specialized lenses that produce no distortion and result in images with constant magnification regardless of the object’s distance.

Illumination

Raghava Kashyapa Says – Illumination is arguably the most important factor in a machine vision system. The lighting should provide uniform illumination throughout all the visible object surfaces. The illumination system should be set up in a way that avoids glare and shadows. Spectral uniformity and stability are key. 

Ambient light and daytime need to be considered as well. Between techniques such as backlighting, bright field lighting, grazing, low angle linear array, and darkfield lighting, several illumination techniques could be used. Techniques that demonstrate the highest contrasts significantly help in increasing the efficiency of a machine vision system.

Related Article: The Ultimate Guide to Machine Vision Camera Selection

Machine Vision Illumination

Conclusion

The aforementioned Image Acquisition Components are part of the image acquisition system. The objective of the entire image acquisition process is to create an image that is usable by the machine vision algorithm. The imaging system’s quality is largely responsible for the success of a machine vision system.

Leave a Reply

Schedule A Demo
close slider