The Imaging Source blog

IAMD Shenzehn 2018

Published on July 18, 2018

From June 27-29, 2018, The Imaging Source and its partner Sunvison welcomed visitors to Integrated Automation Motion & Drives SHENZEHN. The Imaging Source's newest 37 and 38 series monochrome and color industrial cameras featuring the highly-sensitive, low-noise Sony Pregius global-shutter sensors and USB 3.1 (gen. 1) Type-C interface attracted the most attention - especially the 12 MP DFK 38UX253 and the higher-speed (238 fps at 1.6 MP) DFK 37AUX273.

The Imaging Source and <strong>Sunvision</strong> at <strong>IAMD Shenzehn 2018</strong>.

With one of the fastest growing industrial sectors in the world, southern China hosts many important manufacturing tradeshows such as IAMD Shenzehn where the focus on the transformation and integration of new manufacturing processes (e.g. such as the use of artificial intelligence and theoretical modelling in manufacturing processes) are key topics. At IAMD Shenzehn, six sectors were highlighted: intelligent manufacturing, industrial robotics, intelligent control systems, mechanical transmission systems, machine vision and industrial IoT.

<strong>IAMD Shenzehn 2018: </strong>Excellent customer service from The Imaging Source partner, <strong>Sunvision</strong>.

Take the Next Step: Upgrade from CCD to CMOS Sensors Now!

Published on June 25, 2018

In 2015, Sony discontinued all CCD sensors stating that the new Pregius Global Shutter CMOS sensors offer significantly better sensor technology in terms of image quality, sensitivity and performance.

New CMOS Cameras The Sony CCD sensors are global shutter sensors with resolutions between 0.3 MP and 12 MP.

Sony now offers a Pregius global shutter CMOS sensor for each individual CCD sensor with better features and performance at lower costs.

The Imaging Source offers cameras with Pregius sensors from 0.4 MP to 12 MP with GigE, USB 3.0 or USB 3.1 (gen. 1) interface.

Pregius Sensor Resolution MP Sensor Format Pixel Size FPS
IMX287 728×544 0.4 MP 1/2.9 inch 6.9 µm 539
IMX273 1456×1088 1.6 MP 1/2.9 inch 3.45 µm 238
IMX252 2048×1536 3.2 MP 1/1.8 inch 3.45 µm 120
IMX265 2048×1536 3.2 MP 1/1.8 inch 3.45 µm 60
IMX250 2448×2048 5.1 MP 2/3 inch 3.45 µm 75
IMX264 2448×2048 5.1 MP 2/3 inch 3.45 µm 38
IMX255 4096×2160 8.9 MP 1 inch 3.45 µm 42
IMX267 4096×2160 8.9 MP 1 inch 3.45 µm 35
IMX253 4096×3000 12.3 MP 1.1 inch 3.45 µm 30
IMX304 4096×3000 12.3 MP 1.1 inch 3.45 µm 26

We strongly recommend that you begin the migration from CCD sensors to CMOS now. Pixel size and sensor format are important factors when transitioning to CMOS sensors and beginning the process now allows for testing of the new CMOS cameras in your application.

If you have questions about switching to CMOS sensors, please contact us.

Upcoming Trade Shows: June - November 2018

Published on June 6, 2018

Get ideas and answers to your questions about machine vision by visiting The Imaging Source at one of the many upcoming trade shows during the Summer and Fall. The Imaging Source and its resellers will present a range of imaging hardware and machine vision solutions including the new USB3 Vision standard compliant USB 3.1 (gen.1) board-level and industrial cameras as well as the stereo 3D system, IC 3D.

The Imaging Source Reseller Trade Shows

We invite you to take a closer look at our products and discuss your needs with our sales staff. Please feel free to call us at any time or write an email.

Steel Manufacturer Uses Machine Vision to Improve Efficiency and Quality

Published on May 8, 2018

Identifying and tracking steel during production can present manufacturers with special challenges. In nearly every industry, barcoding has long been a key technology used to quickly and accurately track, trace and retrieve items (with varying levels of automation) - allowing for significant improvements in inventory and stock-control systems. When a Japanese manufacturer sought improvements in identifying and tracking their products, they turned to machine vision from The Imaging Source for a solution.

<strong>Machine vision and barcode tracking:</strong> Large steel products are easily tracked and checked for quality issues using barcode tracking with machine vision.

Challenge: Develop a Robust Barcode Recognition System

Linear (1D) barcodes have provided reliable track and trace functionality for decades. Even though barcoding is a straightforward, largely automated task, 1D scanning proves most robust when barcode orientations are tightly controlled which usually requires precise positioning of the product. Many steel products are, however, heavy or otherwise cumbersome which makes consistent orientation of barcodes difficult - leading many mills to opt for the continued use of manual processes (e.g. quickly sprayed or chalked hand-written characters with manual reading and data entry). Loud, busy, poorly-lit factory environments, worn labels/characters and human factors (e.g. fatigue, state-of-mind etc.) all contribute to mistakes on the shop floor - costing the manufacturer time and money.

Solution: Zoom Cameras Capture Barcodes and Other Important Visual Information

The mill's process engineers chose The Imaging Source's GigE color zoom cameras and barcode-recognition software such as IC Barcode. The Imaging Source's zoom cameras feature global as well as rolling shutter sensors from 1.3MP up to 5MP and include a motorized zoom, focus and iris which are powered by the GigE interface via PoE. The cameras' optical zoom function allows the system to capture not only the barcode data as the items go by but also important visual information about product quality - even when distances between camera and object change or positioning is less than optimal.

<strong>Zoom cameras installed on the production line:</strong> Barcodes and important visual information about product quality are captured - even under less than optimal conditions.

Via the cameras' GigE interface, image data is transferred to the host PC. Unlike laser-scan systems, image-based barcode recognition is not limited to 1D barcodes. The image-based system allows production managers to use 1D or 2D barcodes or alternatively to use them both. IC Barcode software, for example, locates and reads 1D and 2D barcodes in any orientation and can also be configured to scan only specific barcode symbologies and orientations or set to a region of interest to speed up detection and decoding. When present, IC Barcode converts the barcode's image data into usable information which is then recorded by the host PC for future retrieval.

A variety of surface defects in steel are common. Therefore, an image-based system delivers additional quality control benefits: The Imaging Source's integrated zoom lens quickly adjusts to capture additional images of the steel allowing quality control managers to carry out vision-based surface inspection of the steel assets. The vision system reduces costly errors while improving efficiency, accuracy and worker safety.

High-Dynamic-Range Imaging with Modern Industrial Cameras

Published on May 7, 2018

Detail, contrast and brilliant natural colors: Example of HDR image created with multiple exposures and image processing via IC Measure.

While resolution and speed (frame rate) were the classic criteria when selecting a suitable industrial camera, sensitivity and dynamic range are becoming increasingly important - especially for cameras used in the automotive sector. In particular, real-world scenes with significant amounts of brightness variation (such as driving) benefit from the advantages offered by a sensor with a wide dynamic range. Take, for example, a car driving out of a tunnel into bright daylight: Sensors with a low dynamic range typically deliver images that are largely under- or overexposed which means a loss of detail (i.e. data) from these areas. If driver-assistance systems rely on this data, such a loss could prove fatal. Here it is absolutely necessary to realize the greatest dynamic range possible in order to acquire important detail in very bright as well as very dark areas.

Increasing Dynamic Range: Two Approaches

In order to increase the dynamic range of final images, basically two approaches are possible: hardware improvements to increase the dynamic range of the sensor and improvements via software algorithms.

The dynamic range of a CMOS sensor depends on the maximum number of electrons a sensor's pixels can hold until they are saturated (saturation capacity) and the dark noise of the pixel (i.e. the noise that occurs when reading out the charge). So to realize an increase in dynamic range, one can try to further reduce the dark noise or increase the saturation capacity. Whereas dark noise is dependent upon sensor electronics, an increase in the pixel's saturation capacity can be achieved either by larger pixels (since more pixel surface area means exposure to more photons thereby generating a greater charge), or by intrinsic improvements to pixel structure. Recently, Sony Pregius sensor technology in particular has impressively demonstrated with no changes in pixel size that improvements in pixel design with simultaneous reduction in dark noise can deliver a remarkable increase in dynamic range. Sony's IMX 265 Pregius sensor, for example, achieves a dynamic range of 70.5 dB at a pixel size of 3.45µm. The consequence of higher saturation capacity is an enlarged range of measurement that can be covered by a pixel. In order to suitably quantize this larger range, more than 8 bits are usually required for modern CMOS sensors; the Sony IMX 264 sensor, for instance, delivers a 12-bit quantized signal.

Improvements in Dynamic Range via Algorithms

In addition to improvements to the sensors themselves, dynamic range can also be algorithmically increased. These algorithmic improvements are based on image data acquired using different exposure times. Probably the most well-known method of this type uses "time varying exposure" (i.e. several complete images acquired with different exposure times) as the data basis. This method is now used in many smartphones and common image processing programs as well as in photography and is therefore known to a wide audience outside the machine vision market.

The basic assumption is that the final pixel values of a sensor are approximately linearly dependent on the incident light quantity and the exposure time, so that if the pixel is not saturated, the underlying incident light quantity (or a quantity proportional to it) is determined for a known exposure time. In the case of saturated pixels, the corresponding pixel values are used for shorter exposure times. In this way, the quantity of incident light can be determined for a larger area than would have been the case with only one exposure. The advantage of the exposure sequence is that the luminance can be determined over an enlarged range without any local resolution loss. Nevertheless, it is important to remember that multiple exposure times are necessary which can lead to unwanted artifacts - especially in the case of moving objects (e.g. ghosting).

Modern CMOS sensors such as Sony Pregius usually have multi-exposure functions to take native images with different exposure times without having to manually change the exposure time between shots.

Spatially Varying Exposure

To avoid artifacts caused by multiple exposures, modern sensors offer "Spatially Varying Exposure" technology. This technique exposes certain groups of pixels on the sensor at different exposure times. A common variant, for example, alternately exposes two image lines using different exposure times. Since the exposures start simultaneously, artifacts caused by movement within the scene are minimized. However, in this case there is no 1:1 correspondence of differently exposed pixels and the pixels of the final HDR image must be calculated by interpolation. This process inevitably means a loss of resolution and can lead to artifacts - especially along edge structures. Furthermore, the calculation of the final image via the requisite interpolation is more computationally intensive than the data calculation from an exposure series.

Fig. 1: Different techniques used to calculate an HDR image. (a) Time varying exposure: Two shots with different exposure times. (b) Spatially varying exposure: two image lines have different exposure times. (c) Spatially varying exposure: Another variant using four different exposure times.

Display of HDR Images and Tone Mapping

When displaying HDR images, one is often directly confronted with the (in comparison to human visual perception) small dynamic range offered by display devices. While HDR displays with a wider dynamic range are now available, they are still far from widespread. If an HDR image is to be displayed on a device with a lower dynamic range, its dynamic range must be reduced by a process called tone mapping. How the reduction is to take place is not clearly defined but depends on the desired goal. This can be, for example, the best possible approximation of the actual scene characteristics or the achievement of a certain subjective, artistic quality. Basically, a distinction is made between global and local tone mapping algorithms. In the case of global algorithms, the same transformation is performed for all pixels regardless of location which makes these algorithms very efficient and allows real-time data processing. Local algorithms act in local pixel neighborhoods and try, for instance, to maintain the best possible contrast in these neighborhoods. The local tone mapping algorithms are more CPU-intensive but usually deliver images with higher contrast.

Fig. 2: Example of an exposure series consisting of two shots: (a) with a shorter and (b) with a longer exposure time. (c) HDR image calculated from the exposure bracket and visualized by tone-mapping.

The Imaging Source long ago recognized the importance of maximum dynamic range for machine vision applications and so offers HDR image data acquisition as well as visualization or saving of data via tone mapping in its end-user software products and programming interfaces. Many programming hours were invested in creating user-friendly algorithms - resulting in automatic modes for the algorithms which automatically adapt all parameters to the scene and require no user intervention - delivering high-contrast shots with brilliant natural colors. In particular, when supported by the camera, the end-user software IC Measure uses HDR functionalities as standard and presents HDR images to the user.

Fig. 3: The Imaging Source's end-user software IC Measure supports native (sensor dependent) exposure series and the visualization of HDR images via tone mapping.

The above article, written by Dr. Oliver Fleischmann (Project Manager at The Imaging Source), was published in the April 2018 edition (02 2018) of the German-language industry journal inspect under the title, "High-Dynamic-Range Imaging in modernen Industriekameras."

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About The Imaging Source

Established in 1990, The Imaging Source is one of the leading manufacturers of industrial cameras, frame grabbers and video converters for production automation, quality assurance, logistics, medicine, science and security.

Our comprehensive range of cameras with USB 3.1, USB 3.0, USB 2.0, GigE, FireWire 400, FireWire 800 interfaces and other machine vision products are renowned for being innovative, high quality and for constantly meeting the performance requirements of demanding applications.

Automated Imaging Association ISO 9001:2015 certified

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