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Typical Tasks of Computer Vision

September 4, 2010 Leave a comment Go to comments

Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement problems or processing problems, which can be solved using a variety of methods.


The classical problem in computer vision, image processing, and machine vision is that of determining whether the image data contains some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case: arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can at best solve it only for specific objects, such as simple geometric objects (e.g., polyhedral), human faces, printed or hand-written characters, or vehicles, and in specific situations, typically described in terms of well-defined illumination, background, and pose of the object relative to the camera.

Different varieties of the recognition problem are described in the literature:

Object recognition: one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene.

Identification: An individual instance of an object is recognized. Examples: identification of a specific person’s face or fingerprint, or identification of a specific vehicle.

Detection: the image data is scanned for a specific condition. Examples: detection of possible abnormal cells or tissues in medical images or detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data, which can be further analyzed by more computationally demanding techniques to produce a correct interpretation.

Several specialized tasks based on recognition exist, such as:

Content-based image retrieval: finding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative a target image (give me all images similar to image X), or in terms of high-level search criteria given as text input (give me all images which contains many houses, are taken during winter, and have no cars in them).

Pose estimation: estimating the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an assembly line situation.

Optical character recognition (OCR): identifying characters in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or indexing (e.g. ASCII).

Motion analysis

Either several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity at each point in the image or in the 3D scene, or even of the camera, that produces the images. Examples of such tasks are:

Egomotion: determining the 3D rigid motion (rotation and translation) of the camera from an image sequence produced by the camera.

Tracking: following the movements of a (usually) smaller set of interest points or objects (e.g., vehicles or humans) in the image sequence.

Optical flow: to determine, for each point in the image, how that point is moving relative to the image plane, i.e., its apparent motion. This motion is a result both of how the corresponding 3D point is moving in the scene and of how the camera is moving relative to the scene.

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. In the simplest case, the model can be a set of 3D points. More methods that are sophisticated produce a complete 3D surface model.

Image restoration

The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More methods that are sophisticated assume a model of how the local image structures look like, a model, which distinguishes them from the noise. By first analyzing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches.

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