Extreme high-speed imaging in powerful and unlocks new capabilities, but also generates more data and has programming requirements than the machine vision industry is accustomed to. Understand these requirements and learn how a high-performance CoaXPress frame grabber addresses these requirements for ease of use

Reducing cost price of your project or product increases the sales potential and success of it. In the design phase you make crucial decisions that affect the cost price. In our presentation we focus on machine vision hardware (camera + lens + processing hardware ). We provide you with tools and knowledge to optimize your decision during the design phase to reduce the cost price for machine vision hardware. You will understand the main cost drivers and cost driving decisions when defining the machine vision hardware. We will summarize it in a set of vision hardware design rules that you can use in your upcoming projects.

Rugged IP67 cameras are the ideal choice for a myriad of industrial applications requiring superior imaging performance and durability in harsh conditions. With industrial environments often exposed to harsh elements including water, humidity, dirt, dust, shock, vibration, chemicals and extreme temperatures, there is a growing demand for the optimization of industrial and automated processes. Rugged IP67 cameras overcome these challenges because they are designed to operate reliably and effectively in these demanding conditions. Learn about the advantages of using rugged IP67 cameras in industrial applications such as automotive, manufacturing, mining, oil & gas, pharmaceutical, food & beverage, inspection, robotics, aerospace, security, machine vision, packaging, process control, traffic & transportation, military, agricultural, and MORE.

In machine vision applications with high levels of contrast (brightness/darkness) it may be difficult or impossible to find an exposure time that properly accommodates the range of photons striking the physical pixels. Some pixels that are receiving photons from light sources or other highly reflective areas in the scene may completely fill up (“saturate”) before the end of the exposure period. All of these pixels will thus be assigned a pure white value, regardless of their relative brightness to one another. Reducing the exposure time to avoid this may cause pixels in the darker portions of the image to not receive any photons, thus all of these would be assigned a zero (black) value regardless of their relative brightness. High Dynamic Range (HDR) imaging refers to the ability to capture such high contrast scenes in a way that maintains the relative order of brightness between all pixels, with few or no bright pixels saturating or dark pixels being assigned a zero value. here are many established methods of doing HDR depending on the type of imaging application (e.g. moving or static object, color or monochrome). Depending on the requirements of the application, HDR data can be provided in linear form with the exact relationship between all pixels maintained, or can be provided in a piece-wise non-linear form where the hierarchy of brightness is maintained, but in a compressed value range for the brighter pixels. Sequential image fusion has been the most classical method of achieving HDR. Multi-slope integration, dual sensor fusion, single exposure-dual gain and double exposure are other methods being used in machine vision applications. This presentation explains various on-camera HDR methods used in machine vision with focus on high resolution, high speed cameras equipped with HDR for various machine vision applications.

This presentation will discuss the exciting direction of line scan imaging with a focus on the latest trends in sensor technology, new speed benchmarks, interface evolution, and more.