One of the easiest ways to understand a computer vision system is to consider it the eyes of a machine. This system improves quality, efficiency, and operations.

If you're ready to dip your toes into the world of video production, one of the most crucial pieces of equipment you'll need is a camera. But with the myriad of options out there, how do you pick the one that's right for you? Let's explore some of the main types of video production cameras and what they bring to the table.

Action cameras are the compact, rugged companions you'll want for capturing high-paced, intense footage. Whether you're skydiving, surfing, or mountain biking, these cameras can handle it all.Popular models like GoPro are well-known for their small size, wide field of view, and durability. However, keep in mind that they lack some features found in other camera types, like interchangeable lenses or large sensors.

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Computer vision and machine vision are overlapping technologies. An intelligent system needs a computer and a particular software tool to function, while computer vision doesn’t need to be linked to a machine.

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Cinema cameras are the big guns. They're primarily used in the film industry and are ideal for high-quality video production. These cameras can capture a wide range of colors and have a higher dynamic range, which means they're great at dealing with both extremely bright and extremely dark scenes.Cinema cameras also have interchangeable lenses, allowing for a wide range of creative choices. But remember, they're bulkier and more expensive than other types of cameras, and they often require additional gear like rigs, external monitors, and audio equipment.

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Machine vision systems include distinct elements or may be combined into a single unit, such as a smart camera that combines the functions of the discrete components into a single package. Irrespective of whether it is using a distinct or integrated system, the efficiency of the intelligent system also depends on the nature of the parts getting analyzed. The more effective the orientation or placement of the component is, the better the system’s performance.

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Understanding the anatomy and capabilities of systems can help guarantee that an application is suitable for camera-based systems. In reality, a camera can capture whatever a human eye can see, and deciphering and reporting the data can be a little daunting. However, with the help of a vendor knowledgeable in the systems, lighting, and techniques, we can save a significant amount of time in the long run.

About us: Viso.ai provides the leading end-to-end Infrastructure for Computer Vision Applications. The Viso Suite platform enables industry leaders to build, deploy, and scale high-performance computer vision applications. Get a demo for your company.

If you want to learn more about how Deep Learning brought great advances in the field of Computer Vision, I recommend you to check out the following articles.

Edge Intelligence, or Edge AI, is the next big trend, moving machine learning for image recognition from the cloud to physical edge devices connected to the cameras. This is relevant for the future of machine vision applications since advances in edge computing (on-device processing) make it possible to apply deep learning capabilities in traditional computer vision tasks. For example, deep learning machine vision is useful for camera inspection systems and vision systems for quality control.

Machine vision is the ability of a computer to see the environment. For example, a video camera runs with analog-to-digital conversion and digital screen processing. The image data is then sent to a computer or robot controller.

When selecting the right machine vision camera, you have to consider several factors. Initially, it is essential to ensure the camera you’ve chosen is suitable for the application you are looking for. For instance, a camera used for robot guidance won’t have the same characteristics as the one used for production control. The choice of any feature will depend on both your budget and your application.

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Let’s see how these discrete elements work together when evaluating a product in a manufacturing operation — which is a typical example of an automated system in practice.

Machine vision systems are embedded components that use data extorted from online images to automatically guide manufacturing and production operations, such as go/no testing and quality control processes. These systems can also play a role in inspection operations and automated assembly verification. They can do so using their capabilities to direct material handling equipment to position products or materials required in a particular process.

Machine vision systems are designed to find defects in manufactured products, assess their dimensional precision, and ensure component integrity. These systems depend on advanced image processing algorithms and HD cameras to analyze products in real-time, ensuring that they stick to stringent quality standards. Such applications reduce production costs, minimize waste, and improve product quality.

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You can't talk about video production cameras without mentioning smartphones. Nowadays, the camera quality on high-end smartphones rivals that of some dedicated cameras. They're always with you, incredibly convenient, and the video quality is more than good enough for social media and some professional applications.From built-in image stabilization to 4K or even 8K video recording, smartphone cameras pack a punch. But, they do have limitations in terms of lens choice and sensor size, compared to other types of cameras.When choosing a camera for video production, remember there's no one-size-fits-all solution. Your decision should depend on your needs, your budget, and the type of content you plan to create. Whether it's a cinema camera for a short film, a DSLR for a wedding shoot, or an action camera for your next adventure, there's a perfect camera out there for every videographer.

The purpose of the system in location applications is to find the physical entity and detect its position and orientation. In an inspection application, a system validates certain features, including the presence or absence of the correct label on a bottle or chocolates in a box. In identification applications, the purpose of a vision system is to read different codes and alphanumeric characteristics.

Machine Vision can enhance the diagnostic abilities of radiologists. It can precisely detect diseases and anomalies within medical images. It can also quickly and accurately analyze vast datasets, such as MRI or CT scans helping medical professionals to make critical decisions about patient treatment plans and care. This has opened up new avenues for early disease detection and customized medicines.

Machine vision cameras are based on sensors with special optics to capture images, process, evaluate, and measure different characteristics through hardware and computer software for precise decision-making. If engineered with the correct optics and resolution, a machine vision camera can detect small object details almost invisible to the human eye.

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These systems have a wide range of applications in different industries that can automate time-consuming, repetitive jobs that would otherwise be tiring to a human operator. The use of such systems allows for the examination of products or components in a process. It results in higher yields, increased quality, low defect rates, reduced costs, and higher consistency of process results.

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Human eyes can sense the range of electromagnetic (EM) wavelengths from 390 to 770 nm. However, video cameras are responsive to wavelengths much broader than this, with some machine vision systems operating at X-ray, infrared, or ultraviolet (UV) wavelengths. Machine vision supports a computer’s ability to see.

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Machine vision systems, also called vision inspection systems or automated vision systems, include many parts commonly found in most systems. Even though each of these parts has its function, they have a particular role in an intelligent system when working together.

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Machine vision cameras are used in various industries, including pharmaceuticals, industrial manufacturing, semiconductors, food and beverages, electronics, automotive, and packaging and printing. Moreover, it has a lot of applications in pattern recognition, location analysis, and inspection with cameras.

Machine vision plays an important role in transforming autonomous vehicles. A multitude of sensors, including cameras, are used by vehicles to perceive their surroundings, interpret road signs, recognize pedestrians, and navigate safely. The Machine Vision algorithms process the data from the sensors thereby enabling autonomous vehicles to make quick decisions, avoid collisions, and enhance transportation systems. With advancements, these vehicles can also reduce accidents and congestion, reshaping the future of mobility.

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DSLR (Digital Single Lens Reflex) and mirrorless cameras are the go-to options for many videographers. They're versatile, portable, and offer excellent image quality. You can switch out lenses on these cameras, giving you tons of flexibility when it comes to framing and perspective.DSLR cameras use a mirror mechanism to direct light from the lens to the viewfinder. When you press the shutter button, the mirror flips up, the shutter opens, and the light hits the image sensor.On the other hand, mirrorless cameras don't have this mirror mechanism. Instead, the light passes directly onto the image sensor, which displays the image on the rear screen. Because of this, mirrorless cameras are generally smaller and lighter than DSLRs, making them great for on-the-go shoots.

For example, computer vision can process images or videos online, along with images from motion detectors, infrared sensors, or other sources. The next megatrend is Edge AI, moving computer vision from the cloud to the edge, close to the sensor gathering data. Machine vision is a sub-class of computer vision.

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The machine vision algorithms can analyze vast agricultural fields to identify areas that are affected by pests or diseases in real time with the help of aerial drones. This would help the farmers target interventions precisely where needed, reducing the application of blanket pesticides and minimizing the environmental impact.

A machine vision system uses a sensor in the robot to see and identify a physical entity using a computer. Various industrial processes, including recognition of optical characters, material inspection, currency recognition, object recognition, pattern recognition, and electronic component analysis, use this system.

The major components of an intelligent camera system are lighting, sensors, communications systems, lenses, and vision processing systems. The camera sensor converts the light into an online digital image. It is then sent to the processor for analysis.

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Camcorders have been around for ages, and they're still a viable option for video production. They're compact, easy to use, and often include features like zoom lenses and in-built microphones.While they don't offer the same image quality as DSLRs, mirrorless, or cinema cameras, camcorders are perfect for long recordings, like concerts or conferences, thanks to their long battery life and large storage capacity.

Written by Emily Malone Marketing Manager for Venture — a full-service video production agency that specialises in producing creative videos & campaigns that get real results.