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In the context of security camera systems, correcting the lens distortion serves an important function for general performance and generating predictable results. Dewarped video feeds not only ease human monitoring but also improve the accuracy of detection systems that employ Computer Vision (CV).

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The type of distortion depends on a number of other factors, like the number of optical elements inside the lens, the aperture of the lens, and whether the lens is varifocal. Typically, barrel distortions are found on wide-angle lenses, while pincushion distortions are found often in telephoto zoom lenses.

Microscope slides should be prepared beforehand and should include a cover slip (plastic or glass, depending on your purpose and budget). If the objective lens should touch the slide (which should not happen anyway of the device is used correctly, see below), these covers will prevent or minimize damage caused. The “stage clips” should be used to secure the slide (and cover) to the stage. Pushing down in the back end of these stage clips will open them.

Image sensor format, sometimes referred to as optical format or sensor size, refers to the shape and size of the image sensor in a digital camera.

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The examples below demonstrate the difference: barrel distortions depict a shape’s edges as curving away from the center of the image, whereas pincushion distortions bend edges toward the center of the image.

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Different parameters, such as image sensor surface area, output resolution, focal length, etc, are tested against known distortion patterns of that lens and applied to the image data. Once the calibrator visually confirms that the parameters used are correctly dewarping an image, these parameters can be bundled together to form a corrective lens profile.

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Most camera lenses used in modern imaging devices utilize curvilinear lenses, which are circular. Curvilinear lenses are cost-effective, however, they introduce two major types of geometric distortion in the final image: barrel and pincushion. These distortions occur because as a light ray passes through the curved surface of a lens, the ray experiences varying degrees of refraction depending on where the ray intersects with said curvature. This causes portions of the image to be magnified or compressed unevenly, more specifically on the edges of an image.

Most modern camera systems utilizing CV-based analytics, like facial, person, or vehicle detection, require LDC to perform optimally. This is because the cost of incorporating distorted training data within a single CV model outweighs the cost of performing LDC on every camera. If a CV model were to include distorted training data, the model would be larger, thus decreasing the speed at which the model could perform detections. Keeping a CV model lean and applying LDC on every CV-based camera makes it easier to make the model better across cameras with different lens profiles.

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Despite the drawback of a reduced field of view, the benefits of improved accuracy and reliability in security applications often outweigh this limitation. It is therefore highly recommended to enable LDC on all security cameras, especially ones utilizing computer vision analytics to ensure optimal performance of the system. When planning physical security deployments, coverage should be based on the field of view with LDC enabled to ensure optimal analytic performance.

LDC reduces the geometric distortion by utilizing a lens profile created specifically for a particular lens. Each profile is created by identifying the distortion characteristics of a lens through an analysis of known patterns and calibration charts.

Lens Distortion Correction (LDC) is a digital image processing technique used for rectifying the distortions introduced by the inherent optical properties of camera lenses. Correcting these distortions is critical for accurate representations of objects and improving the reliability of computer vision applications like facial recognition. LDC profiles are often built specifically for a lens based on known distortion characteristics and patterns, and then applied after the image is captured.

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Eyepiece: It is also referred to as the ocular. This area is used to view objects through the microscope. It is located at the tip of the microscope.

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The first step is to go over all parts of the microscope and get a good idea of where everything is and what everything does – especially any dials or other interactive features. This is best done while standing in front of the microscope where you would normally use it, but you should also do a “walk around” to view any features that are hidden from view when using the device.

LDC is a key part of modern imaging, however, this technique does come with one notable drawback. Once LDC is employed, the overall field of view is effectively reduced. In the example above, one can observe that some of the details captured at the edges of the frame are lost once LDC is enabled. This is because the LDC algorithm involves some cropping and remapping of certain parts of the image, thus consuming some of the detail captured in the outer areas of the image.

202193 — The focal length of a lens determines what your camera can focus on, and how your images are going to turn out.

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When deploying a security surveillance system, LDC should be enabled to ensure proper undistorted coverage and optimal analytical performance.