Applications of CSV are currently a growing research area and there are already commercial products available. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images.
Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes.
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