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Computer Vision Metrics: Chapter One

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Image Capture and Representation

“The changing of bodies into light, and light into bodies, is very conformable to the course of Nature, which seems delighted with transmutations.”
—Isaac Newton

Computer vision starts with images. This chapter surveys a range of topics dealing with capturing, processing, and representing images, including computational imaging, 2D imaging, and 3D depth imaging methods, sensor processing, depth-field processing for stereo and monocular multi-view stereo, and surface reconstruction. A high-level overview of selected topics is provided, with references for the interested reader to dig deeper. Readers with a strong background in the area of 2D and 3D imaging may benefit from a light reading of this chapter.

Image Sensor Technology

This section provides a basic overview of image sensor technology as a basis for understanding how images are formed and for developing effective strategies for image sensor processing to optimize the image quality for computer vision.

Typical image sensors are created from either CCD cells (charge-coupled device) or standard CMOS cells (complementary metal-oxide semiconductor). The CCD and CMOS sensors share similar characteristics and both are widely used in commercial cameras. The majority of sensors today use CMOS cells, though, mostly due to manufacturing considerations. Sensors and optics are often integrated to create wafer-scale cameras for applications like biology or microscopy, as shown in Figure 1-1.

Figure 1-1. Common integrated image sensor arrangement with optics and color filters

Image sensors are designed to reach specific design goals with different applications in mind, providing varying levels of sensitivity and quality. Consult the manufacturer’s information to get familiar with each sensor. For example, the size and material composition of each photo-diode sensor cell element is optimized for a given semiconductor manufacturing process so as to achieve the best tradeoff between silicon die area and dynamic response for light intensity and color detection.

For computer vision, the effects of sampling theory are relevant—for example, the Nyquist frequency applied to pixel coverage of the target scene. The sensor resolution and optics together must provide adequate resolution for each...

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