Computer Vision Stages

Computer Vision Stages

Computer vision is a mixture of image processing and pattern recognition. Image processing is a field related to the process of transforming photos or images (pictures) that is part of computer vision models. This process aims to obtain better image quality. Pattern recognition in this field is related to the process of identifying objects in the image as well as interpreting the image. This process aims to extract data or messages that are informed by images.

Stages of how it works first are by image analysis. What image analysis does is explore the scene in the form of the main characteristics of the object through an investigation process. An application program will look at binary numbers that represent visual data to recognize and understand their special features and characteristics. More specifically, image analysis programs are used to search for objects in the image. An edge in the image is created between the object and its background or between 2 specific objects. This edge will be detected as a result of the brightness level on the different sides with one of the limits.

The next stage of computer vision relates to some of the initial manipulations of binary information. Improving or revising the quality of the image, so that it can be analyzed and processed further and more effectively is called Image Processing. Image processing will increase the ratio of signal to noise (signal-noise ratio = s/n). These signals are the data that will represent the object in the image. On the other hand, noise is all forms of interference, lack of blurring, contained in an object.

Understanding the data in the image. In the computer vision process, this is the final step, in which specific objects and their relationships are identified. In this section, it is necessary to relate to artificial intelligence techniques. Understanding relates to template matching contained in a scene. This method uses a search program (search method) and pattern matching techniques (pattern adjustment method).

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