Which specific insights are you interested in? I agree that IoT Analytics GmbH may process my information in accordance with its privacy statement to contact me and notify me of future research updates. Machine vision (MV) has the highest return on investment (ROI) and quickest amortization time of all Industry 4.0 technologies: For machine vision, this number is also among the lowest of all Industry 4.0 technologies. "In our latest project involving the implementation of an AI-based machine vision system for quality inspection of car assemblies, we achieved amortization in half a year." MV is the combination of different technologies and methods to automate the extraction of image information for providing operational guidance/key data for machines to execute a given task, in industrial and non-industrial settings.

Innodisk, a leading global provider of industrial-grade flash storage, DRAM memory and embedded peripherals, has announced its latest step into the AI market, with the launch of EXMU-X261, an FPGA Machine Vision Platform. Powered by AMD's Xilinx Kria K26 SOM, which was designed to enable smart city and smart factory applications, Innodisk's FPGA Machine Vision Platform is set to lead the way for industrial system integrators looking to develop machine vision applications. Automated defect inspection, a key machine vision application, is an essential technology in modern manufacturing. Automated visual inspection guarantees that the product works as expected and meets specifications. In these cases, it is vital that a fast and highly accurate inspection system is used.

Machine vision computer Nuvis-7306RT is capable of microsecond real-time I/O control for in-time or in-position image capture. With an integrated constant-current lighting controller, isolated 12V camera trigger output, encoder input for position information, digital I/O for sensors/ actuators, and Neousys' patented MCU-based architecture and DTIO/ NuMCU firmware, Nuvis-7306RT can overcome latencies between the sensor input and trigger output. Rugged Edge HPC Server RGS-8805GC brings HPC CPU and GPU processing power to the edge for advanced real-time machine vision inspection. With additional PCIe expansion slots to install function cards, the server can support frame grabber cards for industrial hi-res GigE or USB cameras to acquire detailed images. Supporting NVIDIA Tesla/ RTX GPU and AMD EPYC CPU, it enables perception and discrepancy recognition for mass data analysis, machine reasoning, and near human-level accuracies for machine vision applications.

Arm has introduced a design for an automotive image signal processor to enhance driver assistance and automation technologies. The Arm Mali-C78AE image signal processor (ISP) is part of Arm's AE line of safety-capable intellectual property suitable for advanced drivers assistance systems (ADAS) and human vision applications. It's the first product announcement since Nvidia called off the $80 billion acquisition of Arm last week. The first licensee for the tech is Intel's Mobileye, which is licenses the Mali-C78AE and the next-generation EyeQ technology. ADAS tech has grown from a premium vehicle feature to a capability consumers now expect as standard in new vehicles, as the systems have helped with driver safety.

Aberrations in machine vision can affect the quality of input data. It can also derail the AI-based operations of businesses. Identifying an aberration and preventing it--or correcting it--are needed to avoid major operational problems related to machine vision optics. Machine vision uses the concept of computer vision for industrial operations. For obvious reasons, the presence of clarity while capturing images or videos is necessary for machine vision.

The application of machine learning (ML) for object detection and classification is becoming an urgent need within the embedded systems industry--especially for Internet of Things (IoT), security, advanced driving assist systems (ADAS), and industrial automation-based systems. However, object detection is a complex topic and ML is relatively new, so developing ML applications to detect objects can be difficult and cumbersome. For example, object detection has traditionally required developers to learn a framework like OpenCV and to purchase thousands of dollars in computer equipment in order to be successful. As such, traditional approaches to object detection and machine vision are not just time consuming, they are also expensive. For engineers looking to apply ML for object detection and machine vision applications without the need to become an expert in ML or spend a small fortune on equipment, the Python programmable OpenMV H7 camera module from SparkFun Electronics is an innovative solution.

The successful application of machine vision technology involves an intricately and carefully balanced mix of a variety of elements. While the hardware components that perform the tasks of image formation, acquisition, component control, and interfacing are decidedly critical to the solution, machine vision software is the engine "under the hood" that supports and drives the imaging, processing, and ultimately the results. This discussion will detail the various ways software impacts industrial machine vision systems and how it is applied to achieve a complete solution within different component architectures. We also will take a brief look at general design and specification criteria and current trends in software that might contribute to greater reliability in some machine vision tasks for industrial automation. The diverse marketplace for machine vision technology features components and systems with widely varying architectures.

Industrial companies are confronted with several new trends that will fundamentally change production and logistics processes. For example, the term "Industry 4.0," which was coined in Germany, stands for the digital networking of people, objects, and systems to create integrated production processes. In international jargon, it is referred to as the Industrial Internet of Things (IIoT). All technologies, systems, and components that are involved in the industrial value creation process are connected to each other as well as to company networks and the internet. Smart factory is another trend that forms a part of the IIoT development.

We present a method for learning, tracking, and recognizing human hand gestures recorded by a conventional CCD camera without any special gloves or other sensors. A view-based representation is used to model aspects of the hand relevant to the trained gestures, and is found using an unsupervised clustering technique. We use normalized correlation networks, with dynamic time warping in the temporal domain, as a distance function for unsupervised clustering. Views are computed separably for space and time dimensions; the distributed response of the combination of these units characterizes the input data with a low dimensional representation. A supervised classification stage uses labeled outputs of the spatiotemporal units as training data. Our system can correctly classify gestures in real time with a low-cost image processing accelerator.

We present a method for learning, tracking, and recognizing human hand gestures recorded by a conventional CCD camera without any special gloves or other sensors. A view-based representation is used to model aspects of the hand relevant to the trained gestures, and is found using an unsupervised clustering technique. We use normalized correlation networks, with dynamic time warping in the temporal domain, as a distance function for unsupervised clustering. Views are computed separably for space and time dimensions; the distributed response of the combination of these units characterizes the input data with a low dimensional representation. A supervised classification stage uses labeled outputs of the spatiotemporal units as training data. Our system can correctly classify gestures in real time with a low-cost image processing accelerator.