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Introduction All image acquisition systems share a supreme objective, that is, to capture stable and meaningful optical data. Despite the tremendous potential of machine vision systems to spot the smallest of errors and increase the net throughput dramatically, their performance is heavily dependent on the image quality that they receive. In other words, inconsistent or poor visual data lowers the machine vision system’s accuracy considerably. Needless to say, getting the lens and the related parameters

Introduction to Depth of Field In the easiest words, depth of field is the distance between the nearest and farthest features in an image that appear in ‘acceptably’ sharp focus. It can also be understood as the depth of the object. Theoretically, the object under consideration is “in focus” only at a specific distance. Practically, however, vision systems can tolerate a little blurring of the image. This is because while your camera can focus sharply

1. Introduction 2. Frame Rate 3. Types of Shutters 4. How to calculate the frame rate for your vision application? 5. Conclusion Introduction Image acquisition is the first and the most crucial step of a machine vision application. Frame rate, exposure time, and shutter speed are some key terms that determine how fast the images are captured. Let’s understand each of these terms one by one. So, the frame rate is the number of individual

Introduction Incorporating the right kind of camera can carve out considerable savings in time and resources. However, choosing the right fit for your vision application from the enormous range of options available in the market may prove to be a challenging task. While commercial cameras offer good resolution and cheaper options, they cannot be used in vision systems because machine vision cameras serve entirely different purposes. Some critical features of a machine vision camera are:

Introduction  You might have probably come across the “megapixel myth” or the phrase: “the higher the camera resolution, the better the camera is”. The “megapixel myth” has some truth to it, but it works both ways. High-resolution images are sharper, capture more details, and have room for digital zooming without significant loss of detail.  However, higher resolution is not always preferable. Extremely high resolution can lead to large file sizes and increased processing times in

Introduction Machine vision has become the eye of industrial automation. Despite its relatively recent inception, it has gained traction in quite a number of industries because of its ability to provide augmented accuracy and quicker turnaround simultaneously. An MV system’s accuracy plays a pivotal role in the success of a product in the market. In other words, the efficiency of the machine vision system significantly impacts the overall efficiency and the net throughput of the

Introduction to Machine Vision Wikipedia defines machine vision as the technology and methods used to provide image-based automatic inspection and analysis for applications such as process control, automated inspection, and robot guidance, usually in the industrial landscape. A basic machine vision system consists of a digital or analogous camera, embedded systems composed of processors and software, a frame grabber, and lighting systems. Related Article– The Evolution of Machine Vision How machine vision systems work can be

Introduction The very notion of machines being able to “see” things and accomplish mind-numbing tasks with tremendous accuracy seems to be the stuff of a sci-fi movie. But vision systems have now converted this notion into a reality. You may have come across IT buzzwords like “machine vision” and “computer vision”. You may be wondering what is computer vision, and is it not the same as machine vision? While these terms are frequently used interchangeably,