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Augmented and virtual reality: The FOV in these applications is important for creating an immersive experience. In AR, it is important to match the FOV of the virtual objects with the real world objects. In VR, a wider FOV is desirable to create an immersive experience.

A shorter focal length allows the lens to take in more of the scene in front of the camera resulting in a wider angle of view. This is why shorter focal lengths are often referred to as wide-angle lenses. Conversely, a longer focal length allows the lens to take in less of the scene resulting in a narrower angle of view. This is why longer focal lengths are often referred to as telephoto lenses.

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Machine vision systems for quality control and inspection: In these applications, a narrower FOV may be desirable as it allows the camera to capture more detailed images of a specific object or area. This is useful for inspecting products for defects or monitoring production processes for quality control.

The focal length of a camera lens is directly related to the field of view (FOV) of the camera. The focal length refers to the distance between the lens and the image sensor when the lens is focused on an object at infinity. A shorter focal length will result in a wider FOV, while a longer focal length will result in a narrower FOV.

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Robotics and autonomous systems: In the field of automation and robotics, the FOV is crucial for navigation and obstacle detection. For example, in autonomous vehicles, the FOV is important for identifying other vehicles, pedestrians and objects. In autonomous robots, the FOV is important for grasping and manipulating objects.

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Additionally, the FOV also affects the resolution of the captured image. A wider FOV will result in a lower resolution image because the camera lens captures a larger area. A narrower FOV will result in a higher resolution image because the camera lens captures a smaller area with more detail. This is important to consider when designing embedded vision systems as the resolution of the captured image will affect the accuracy and performance of the vision algorithms being used.

A shorter working distance will result in a wider FOV and a larger object in the image. This is because the camera lens is closer to the object allowing it to capture more of the scene in front of it. A longer working distance, on the other hand, will result in a narrower FOV and a smaller object in the image. This is because the camera lens is farther away from the object allowing it to capture less of the scene in front of it.The working distance also affects the resolution of the captured image. A shorter working distance allows the camera to capture more detailed images of the object, while a longer working distance captures less detailed images.

In summary, selecting the right FOV for an embedded vision system involves assessing the coverage area, distance to the object, balancing FOV and resolution, considering lens focal length and testing different FOVs. By considering all these factors, you can select the right FOV for your embedded vision application.

Additionally, the sensor size also affects the image resolution. A larger sensor can capture more light and therefore, produce higher resolution images. A smaller sensor, on the other hand, will capture less light and produce lower resolution images.

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Selecting the right field of view (FOV) for an embedded vision system is crucial to the success of the system. Following are some of the key considerations while selecting the FOV for your embedded vision application:

In practice, a shorter focal length is generally preferred for applications that require a wider FOV. A longer focal length may be preferred for applications that require a narrower FOV. However, the selection of the focal length also depends on the other factors such as the sensor size, image aspect ratio and the distance to the object.

4.Consider lens focal length: The focal length of the lens also affects the FOV. A shorter focal length results in a wider FOV, while a longer focal length results in a narrower FOV.

The field of view (FOV) is an important consideration for designing embedded vision systems. It affects the camera’s ability to capture images of a certain area and the resolution of the captured image, which directly impacts the performance of the vision algorithm. The FOV is determined by various factors such as lens focal length, sensor size, image aspect ratio and working distance. It is important to consider all these factors together when selecting a camera for an embedded vision system. By understanding the concept of FOV and its relevance in embedded vision applications, we can select the right camera to achieve the desired results.

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3.Balancing FOV and resolution: The FOV and image resolution are directly related. A wider FOV results in a lower resolution image, while a narrower FOV results in a higher resolution image. Therefore, it is important to balance the FOV and resolution requirements to achieve the desired results.

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The working distance of a camera refers to the distance between the camera lens and the object being captured. Working distance is associated with field of view (FOV) of the camera because it has a direct impact on the angle-of-view and the size of the object being captured.

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1.Determine the coverage area: The first step in selecting the right FOV is to determine the coverage area. This means identifying the specific area or object that needs to be monitored or inspected.

2.Assess the distance to the object: The distance to the object also plays a key role in determining the right FOV. The closer the object, the narrower the FOV required, and vice versa.

The field of view (FOV) in an embedded camera refers to the max area at which the camera can capture images. It is typically measured in degrees and can vary depending on the lens used in the camera. In Generally, the field of view of a camera will be measured by horizontally, vertically or diagonally, and for a sensor, the FOV is more often than not a diagonal field of view angle measurement, which is often referred to as DFOV or diagonal FOV. and the image sensor aspect ratio changes directly affect the horizontal FOV (HFOV) and vertical FOV (VFOV).

5.Comparing different FOVs: It is also important to test different FOVs to determine which one works best for your embedded application. This can be done by capturing images with different FOVs and comparing the results.

Embedded vision systems are becoming increasingly prevalent in today’s technology landscape and the selection of the right camera is crucial to the success of these systems. One of the most important factors to consider when selecting a camera is the Field of View (FOV). In this article, we will explore the concept of FOV and its relevance in modern-day embedded vision applications.

The sensor size of a camera is directly related to the field of view (FOV) of the camera. The sensor size refers to the physical dimensions of the image sensor in a camera and it can have a significant impact on the FOV.A larger sensor size allows for a wider FOV. This is because a larger sensor can capture more of the scene in front of the camera, which results in a wider angle of view. A smaller sensor size, on the other hand, will result in a narrower FOV. This is because a smaller sensor can capture less of the scene in front of the camera which results in a narrower angle of view.

FOV is one of the most important factors while selecting a camera for embedded vision applications. The impact of FOV on some of the major embedded vision applications is described as following:Surveillance and traffic monitoring systems: In these applications, a wider FOV is typically desirable as it allows the camera to capture a larger area, such as an intersection or a parking lot. This allows the system to monitor a greater area and detect any potential threats or incidents.

The field of view (FOV) is an important consideration in embedded vision systems because it affects the camera’s ability to capture images of a certain area. A wider FOV allows the camera to capture more of the scene in front of it, which is useful in applications where a larger area needs to be monitored such as in surveillance or traffic monitoring systems. A narrower FOV captures less of the scene but with more detail, which is useful in applications where a specific object or area needs to be closely monitored such as in machine vision systems for quality control or inspection.

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