COMPUTER VISION Computer vision is concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner. Typical Applications of computer vision 1. Recognition: The classical problem in computer vision, image processing and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. 2. Motion: Several tasks relate to motion estimation, in which an image sequence is processed to produce an estimate of the velocity or to track objects 3. Scene reconstruction: Given one or (typically) more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene. 4. Image restoration: The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images using various filters Functions Computer vision systems Typical functions which are found in many computer vision systems. 1. Image acquisition: A digital image is produced by one or several image sensor which, besides various types of light-sensitive cameras, includes range sensors, tomography devices, radar, ultra-sonic cameras, etc. 2. Pre-processing: Before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to assure that it satisfies certain assumptions implied by the method. Examples are Noise reduction and Contrast enhancement 3. Feature extraction: Image features such as lines and edges are extracted from the image data. 4. Detection/Segmentation: A decision is made about which image points or regions of the image are relevant for further processing. For example segmentation of one or multiple image regions which contain a specific object of interest. 5. High-level processing: It deals with classifying a detected object into differ

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