# Computer Vision and Image Processing Approaches for Corrosion Detection
## 2 Background
### Computer Vision and Image Processing  
Computer vision involves developing algorithms and techniques that enable machines to simulate or replicate human vision using machine learning. The machine or computer has the ability to perceive and analyze patterns using visual inputs. Images and videos are the most common visual inputs for computer vision. Moreover, object detection and object identification are the outputs of computer vision. Image processing is one of many techniques used in computer vision. Other techniques, such as instance segmentation, semantic segmentation, object tracking, object detection, and image classification, are also important and widely used in computer vision. Corrosion detection benefits from the use of computer vision as it is capable of identifying and categorizing areas of corrosion within images, along with other machine learning techniques such as the use of the convolutional neural network (CNN) and the genetic algorithm. It is a non-destructive test and can be operated from a distance. Other examples of computer vision applications include face detection, car detection, and iris classification.  
Image processing involves adjusting the various parameters and features of an image to enhance and transform it for improved clarity and quality. The images can be transformed using many techniques such as stretching, smoothing, sharpening, deblurring, filtering, inpainting, restoration, compression, and segmentation. These digital images comprise specific values and locations, which are represented by pixels, visual components, and other picture elements. Each of the images consists of rows and columns of pixels. The two-dimensional signal is processed in accordance with the given or specific task. The inputs for image processing are images and videos, which are similar to computer vision. Moreover, the outputs are the transformed input images and videos. Image processing is considered to be a subset of computer vision since it is a technique used in computer vision. Hence, corrosion detection utilizes image processing techniques since it can be used to segment, filter, sharpen, and enhance the corroded area in images. Hence, corrosion can be detected in the images using the aforementioned processes. Other examples of image processing applications include illumination correction, noise reduction, contrast enhancement, and image rescaling.  
The main difference between computer vision and image processing can be understood as follows. Image processing involves processing and manipulating raw images into new images using different features. Moreover, computer vision involves extracting meaningful information or data from the images. Computer vision will not enhance features in images, but it will gain a high level of understanding from them. Machine learning techniques allow machine vision to interpret images in the same way humans do. Furthermore, image processing focuses on processing, whereas computer vision focuses on pattern recognition and data understanding. Moreover, computer vision requires training with a significant amount of data. Nevertheless, image processing does not require training and can be implemented straight away.