Machine vision software development steered by speed, simplicity
As machine vision customers continue to expect swift vision application development, ease of use, and adoption of new technologies, image processing software manufacturers are updating their products to meet that demand. That cycle is resulting in numerous opportunities for software makers to show off their products’ versatility and muscle.
"The market is not changing as much as it is expanding," said Steve Geraghty, vice president, industrial products division, of Teledyne Dalsa. "The incorporation of newer technologies, such as infrared and 3-D imaging, is increasing the range of applications where machine vision can be applied."
Dr. Olaf Munkelt, Cofounder and Managing Director of MVTec Software GmbH (Munich, Germany), says that machine vision development is "driven by the rapid changes due to the Industrial Internet of Things (IIoT), or Industrie 4.0, demanding integrated production processes enabled by the digital interconnection of systems, machines, objects, and people."
Deep learning, in which computers learn from experience, is poised to meet these demands on the factory floor and beyond. "New software technologies such as deep learning extend application possibilities by learning normal product variation such that robust classification is possible," Geraghty said. "Deep learning allows machine vision to be used to segment and classify objects, or even beings, from cluttered or changing backgrounds, rendering very difficult applications possible."
MVTec has introduced deep learning technologies for optical character recognition (OCR). "With deep learning algorithms becoming more and more manageable, we were able to cast them into a feature that is very important in industrial applications and also within the IIoT—reading characters of any shape and under harsh industrial conditions robustly, fast, and without long training processes by the user," Munkelt said.
At the end of 2017, MVTec will release a new version of its software, which offers a large selection of functions for the use of deep learning out of the box. For example, for the first time it enables customers themselves to conduct training of convolutional neural networks based on deep learning algorithms.
Matrox Imaging has added an OCR tool to its software development kit. which can read challenging dot-matrix text.
Pierantonio Boriero, director of product management for Matrox Imaging, said, "What’s more, the use of dot-matrix text for product identification is still extremely common in the packaging industry given the continued effectiveness of inkjet printers."
Other additions to their software development kit reflect customers’ drive for even more precise vision inspection tasks. Matrox started adding dedicated shape-finding tools to its vision library after finding that many pattern-recognition applications required locating only basic, as opposed to complex, geometric shapes. An ellipse-finding tool complements an existing circle-finding tool.
"These dedicated shape-finding tools outperform general geometric pattern recognition in terms of robustness and speed while providing greater degrees of freedom," Boriero said.
For 3-D vision, Matrox added a method for extracting and then measuring a cross section from a point cloud, as many profiling applications are solved simply by analyzing cross sections using 2-D metrology. "We also made calibration more practical with the support of a self-describing calibration target and support for partially visible and extended calibration targets," Boriero said.
Because their customers continually request user-friendly products, vision software manufacturers are easing developers’ programming burden. Using data-flow models on a graphical user interface (GUI), software from Silicon Software is designed to simplify the programming of application-specific image preprocessing on field-programmable gate array (FPGA) vision processors. The technology allows software and application engineers to program FPGAs without knowledge of VHSIC hardware description language (VHDL), one of the standard languages used to describe digital systems.
"Writing FPGA-based image processing algorithms with VHDL can take months, since modifying algorithms is also a long process," said Mike Faulkner, Silicon Software’s director of business development for the Americas.
Because most of the image preprocessing takes place directly on the FPGA, vision systems achieve real-time results that PC-based image processing cannot and the software not only allows high-speed and low-latency image processing, but also reduces the volume of data to be transmitted and processed and consistently delivers repeatable performance. It also lowers system development costs by greatly accelerating the development cycle.
Matrox Imaging also has released a program geared toward those who do not want to, or cannot, code. They have developed a flowchart-based interactive development environment that aims to "further streamline the process of developing and maintaining a vision application," Boriero said.
The changes in image processing software have been more gradual than grandiose, and despite machine vision customers’ growing list of refinements and requirements, the rules of engagement remain the same. "Developers who use a vision library still want, first, access to tools that are robust, fast, and practical," Boriero said.
Not only do developers expect a library’s application programming interface to make it easy to join employee tools together through support of various coordinates’ reference frames, for example, they also expect a library to help them with the integration and implementation of the rest of the application, such as through graphical image annotation in the operator interface.
"The vision tools themselves need to be ready to deploy, needing little to no adjustments to settings in order to work in specific cases," Boriero said. That said, "Developers do want the comfort of well-thought-out controls, just in case."
Boriero also indicated that developers don’t want to code just to try or investigate something. "They want code generation to get them going more quickly when they are ready to proceed with their application," he said.
Ultimately, software manufacturers attuned to their clients’ needs will develop solutions that tackle every vision task from the simple to the complex. "While most applications we encounter can be easily solved with our standard tool set, we do encounter plenty of applications that require something unique," Geraghty said.
That could include developing custom algorithms or tool sequences specific to a customer’s application, or changing how to interact with customer programs, since some customers have preferred or predefined methods of integrating with third-party applications.
"We try not to overcomplicate the sales process with a la carte ordering, but we also recognize that users want to purchase what they use," Geraghty said.
Winn Hardin is contributing editor for AIA. This article originally appeared on Vision Online. AIA is a part of the Association for Advancing Automation (A3). A3 is a CFE Media content partner. Edited by Chris Vavra, production editor, CFE Media, firstname.lastname@example.org.