Imaging Insight

In machine vision, image quality is everything. Whether a system is tasked with inspecting tiny components, reading barcodes at high speed, verifying assembly quality or guiding robots with absolute precision, the camera can only make decisions based on the light it receives. That’s why optical filters – a sometimes overlooked part of a vision system – play a critical role in ensuring accurate, repeatable imaging.

There are many lighting techniques in machine vision, but backlighting – placing an illumination source behind the object, opposite the camera – is especially effective for certain applications. While front-lighting or diffuse dome lighting might illuminate a surface, backlighting creates a clean silhouette by allowing light to pass through or around the subject. This technique is particularly useful for edge detection, shape verification and measurement tasks.

In today’s connected cities, intelligent traffic systems (ITS) and automatic number plate recognition (ANPR) are essential tools for improving safety, enforcing regulations and streamlining traffic flow. At the heart of these systems are machine vision cameras –designed to capture clear, precise images in complex, fast-changing environments.

When building a machine vision system, one of the most-critical parameters to get right is working distance (WD) – the distance from the lens’ front surface (or mechanical housing) to the object being inspected. Getting this distance correct ensures sharp focus, accurate measurements and reliable defect detection.

Many LED light sources emit light in a Gaussian- or “bell-shaped” curve: strong in the center wavelengths, tapering off at the edges. For a filter to maximize image performance, its passband should closely emulate this Gaussian curve – matching the center, width and tails. When a filter’s passband is too broad or too “flat-topped,” more unwanted ambient light (outside the LED’s strong emission region) is allowed through, increasing noise and reducing contrast.

In modern food and beverage manufacturing, getting it right means more than taste and packaging – it’s about consistency, safety and visual perfection. Machine vision cameras, when properly implemented, help ensure we catch defects, meet hygiene standards and keep up with consumer expectations. Below are ways high-quality imaging systems upgrade quality control.

When building a vision system, selecting an optical filter that emulates the bell-shaped output (Gaussian transmission curve) of the illumination source's spectrum can directly influence image clarity, contrast and overall system performance.

When building a machine vision or surveillance setup, the sensor size of the camera is a foundational choice. But its full value isn’t realized unless its paired with the right lens. The wrong lens can waste resolution, ruin field of view or degrade image quality. Here’s how to ensure the lens matches the sensor – and optimizes the system.

Machine vision has come a long way, and LED lighting has been a key driver. As inspection speeds increase, product surfaces become more challenging and lighting conditions more difficult, high-quality LED lighting solutions have evolved to meet these demands. Below is a look at how LED lighting for vision applications has developed, and what modern systems demand.

When building a machine vision system, the camera and lighting often get most of the attention. But the lens – and specifically its aperture and DOF (depth of field) – plays just as important a role in achieving clear, reliable images.

The choice of camera interface plays a crucial role in machine vision system performance. The most common three interfaces are GigE, USB3 Vision and CoaXPress.

Lighting determines how surfaces, textures and edges appear to the imaging system – and ultimately how well the application performs. Three of the most common lighting approaches are diffuse, direct and structured.

In machine vision, optical filters aren’t just add-ons, they’re essential tools for image accuracy. By carefully controlling which wavelengths pass through (and which don't), filters help maximize contrast, enhance color accuracy, highlight critical details and block ambient light that can compromise results.

In industrial imaging, lenses are fundamental components that shape how the camera captures the world. Among the many lens parameters, focal length plays a pivotal role in determining what and how much we see. Understanding focal length and its effect on the field of view (FOV) is essential for selecting the right lens for your application.

In machine vision, lighting is important. The quality, angle and consistency of illumination directly impact the ability of your vision system to capture accurate, reliable images. Among the many lighting considerations, one crucial yet often overlooked factor is uniformity – achieving even, consistent illumination across the entire field of view.