Imaging Insight

Machine vision cameras are the backbone of any inspection, measurement or automation system. But as sensor technology, interfaces and processing demands evolve, older cameras can quietly become a bottleneck, limiting performance, accuracy and scalability. If your system is struggling to keep up, it may be time for an upgrade.

Achieving accurate, repeatable results depends on far more than just selecting the right camera or lens. One of the most overlooked components in many imaging systems is the optical filter. When properly selected, filters dramatically improve image quality, reduce variability and ensure consistent performance across changing environments.

In machine vision and industrial inspection, understanding the difference between infrared vs thermal imaging is essential for choosing the right technology. Although the two are often confused or used interchangeably, they rely on different wavelength regions, serve different purposes and are optimized for specific types of applications.

Machine vision is an essential technology for automation, quality control, robotics and industrial inspection. But with so many types of machine vision systems available today, it can be challenging to determine which configuration is right for your application.

Choosing the right lens for a machine vision system involves more than focal length, resolution and working distance. The lens mount, the mechanical interface between the camera and lens, is one of the most important factors in ensuring proper imaging performance. The mount not only affects compatibility, but also impacts back focal distance, sensor coverage, optical stability and the overall footprint of the system.

Lighting is one of the most influential factors in machine vision performance. The right illumination can dramatically improve contrast, reduce noise and stabilize inspection results, while the wrong setup can cause missed defects, blurry images or inconsistent measurements.

Quality standards continue to rise in manufacturing environments while defect tolerance grows increasingly narrow. Traditional machine vision systems, typically relying on standard color or monochrome sensors, can struggle to catch flaws that are invisible to the human eye or obscured by lighting, surface finishes or material properties. Because of this, multispectral imaging has become a powerful tool for advanced inspection.

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.