Understanding computer and machine vision
As the technology continues to evolve, machine and computer vision are shifting towards real- time, AI-powered, and Edge-based implementations. This article explores the history, evolution, and current landscape of machine/ computer vision, diving into the technologies, topologies, and interfaces that enable them. The history of computer and machine vision Whilst machine and computer vision seem like brand-new cutting- edge technology, its origins actually date back to the 1950s and 1960s, when researchers began exploring the possibility of enabling machines to interpret visual data. At these early stages, computer vision was primarily an academic pursuit, focused on recognizing simple patterns such as handwritten text and basic geometric shapes. Early efforts relied on rudimentary image processing techniques like edge detection and thresholding, laying the foundations for future advancements. Fast forward to the 1970s and feature extraction methods, including edge detection algorithms such as the Sobel operator, allowed for more sophisticated object recognition. Researchers also began developing techniques for 3D reconstruction,
practical applications, particularly in industrial automation. The emergence of digital imaging technology enabled early vision systems to be deployed for tasks such a defect detection, part inspection, and barcode reading. During this period, industries started adopting machine vision for quality control in manufacturing, leveraging these early systems to improve efficiency and accuracy across production lines. The 1990s saw further advancements, particularly in statistical image analysis and pattern recognition. The introduction of early neural networks into image processing signaled the beginning of AI-driven vision, though the technology was still in its infancy. Meanwhile, improvements in hardware, especially computational power, were making it possible to process images much faster and more efficiently. Feature-based vision
techniques, such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF), began cropping up in the 2000s, massively improving object recognition. This period also saw the rapid expansion of 3D vision technologies, including stereo cameras and structured light systems, which allowed for more precise depth perception in automated vision systems. However, the 2010s were the real turning point thanks to the rise of deep learning. The introduction of convolutional neural networks (CNNs) revolutionized computer vision by surpassing traditional algorithmic approaches. In 2012, the success of AlexNet in the ImageNet competition demonstrated that AI-powered models could achieve human-level accuracy in image classification, sparking widespread interest
Written by Harry Fowle, Electronic Specifier
The ability for machines to interpret and analyze visual data is critical to many industries of today, enabling automated systems to extract meaningful information from images or video streams. At its core, this technology mimics how we perceive the world around us but leverages computational power, advanced algorithms, and specialized hardware to process vast amounts of visual data with high precision and speed. The impact of machine and computer vision extends across a number of industries. In manufacturing, vision systems play a crucial role in quality control and defect detection. In automotive, vision enables autonomous vehicles to perceive and navigate their surroundings. Healthcare benefits from medical imaging applications, such as AI-assisted diagnostics, while retail and security leverage vision for facial recognition and behavioral analysis. Modern machine vision systems rely on a combination of hardware and software components:
Figure 2: Camera performing 360° inspection of products on a conveyor
Figure 1: Modern machine vision system
ranging from simple 2D cameras to advanced LiDAR and hyperspectral imaging systems Processing units: GPUs, TPUs, FPGAs, and AI accelerators process visual data in real time, supporting deep learning-based decision-making Algorithms: classical techniques, such as edge detection and template matching, coexist with modern
AI-driven approaches like convolutional neural networks (CNNs) and transformer-based vision models Interfaces and communication protocols: standardized interfaces (e.g., USB3 Vision, GigE Vision, MIPI) and industrial protocols (EtherCAT, Modbus) facilitate seamless integration with broader automation systems
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moving beyond simple two- dimensional image analysis.
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The 1980s was a big decade for the technology, finally seeing its first
Sensors and cameras: these capture images and video,
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we get technical
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