In machine learning, What is computer vision, and image processing? What is the difference between them? Each of those fields focuses on an image or signal input. They structure the signal and then in exchange give us the altered output.
And what makes these fields stand out from one another? The boundaries between these realms that seem evident as their names already indicate their goals and methods. Those fields, however, draw heavily from each other’s techniques, which can blur the limits between them.
Computer Vision
Computer vision slightly different from image processing. It comes from image recognition modeling using machine learning techniques. Machine learning appears to apply computer vision to recognize patterns for image interpretation. Just like the cycle of human visual reasoning; we can differentiate between objects, identify them, sort them by their size, and so on. Computer vision, like image processing, takes images as input and provides the output of size, frequency of color, etc.
Self-driving cars may use computer vision to detect objects such as light poles, pedestrians, and other cars. These systems can build route 3d objects and forecast accidents. It can execute airbags to protect passengers in case a self-driving vehicle predicts an accident. So computer vision will make vehicles self-driving safer. Computer vision can also help shelf-management vendors by sending alerts after they notice an empty shelf.
The components of a Computer Vision system:
- Image capturer.
- Software to image recognition.
- Machine learning and pattern recognition algorithms.
- Display screen or robotic arm to execute instructions achieved from the interpretation of images.
- Illuminators.
- Camera and lens.
Image Processing
Likewise, mathematical functions refer to image processing and are not as same as a computer vision. The result of the processing of images may or may not provide detailed data. Therefore, machine learning is not necessary for image processing. Alternatively, the processing of images conducts enhanced images such as sharpening, smoothing, stretching and contrasting.
Computers vision images as 2D signals consisting of pixel columns and rows. The information taken from image processing in many implementations may provide useful data. Hospitals, for example, use image processing in processes of biomedical imaging such as CT scans, scanning, and MRIs. Healthcare professionals are getting important information about their patients with these.
Certain methods used in digital image processing:
- Independent component analysis.
- Image segmentation.
- Neural networks.
- Hidden Markov models.
- Anisotropic diffusion.
Understanding the difference between Computer vision and Image processing:
Computer vision and image processing displayed untold potential in their own unique ways. Good outcomes that obtain through the use of image recognition and computer vision in retail, healthcare, and many other industries. These technologies are capable of developing business operations which include a visual aspect. Such innovations have the ability to improve business processes such as quality assurance, inventory management, and medical imaging. Nonetheless, these systems can also mix up. Therefore, businesses need to be mindful of their gaps to make wise use of them.
Organizations can identify how such innovations will help their company by recognizing the difference between computer vision and image processing. Enterprises may use computer vision to automatically process data and produce useful results. Meanwhile, image processing uses to convert images to other types of visual data. Knowing the various advantages of these technologies, businesses can decide which innovation would suit different usage cases.
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