Computer Vision - Computer Vision System Methods

Computer Vision System Methods

The organization of a computer vision system is highly application dependent. Some systems are stand-alone applications which solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on if its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions which are found in many computer vision systems.

• Image acquisition – A digital image is produced by one or several image sensors, which, besides various types of light-sensitive cameras, include range sensors, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images), but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or nuclear magnetic resonance.
• 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
  • Re-sampling in order to assure that the image coordinate system is correct.
  • Noise reduction in order to assure that sensor noise does not introduce false information.
  • Contrast enhancement to assure that relevant information can be detected.
  • Scale space representation to enhance image structures at locally appropriate scales.
• Feature extraction – Image features at various levels of complexity are extracted from the image data. Typical examples of such features are
  • Lines, edges and ridges.
  • Localized interest points such as corners, blobs or points.

More complex features may be related to texture, shape or motion.

• Detection/segmentation – At some point in the processing a decision is made about which image points or regions of the image are relevant for further processing. Examples are
  • Selection of a specific set of interest points
  • Segmentation of one or multiple image regions which contain a specific object of interest.
• High-level processing – At this step the input is typically a small set of data, for example a set of points or an image region which is assumed to contain a specific object. The remaining processing deals with, for example:
  • Verification that the data satisfy model-based and application specific assumptions.
  • Estimation of application specific parameters, such as object pose or object size.
  • Image recognition – classifying a detected object into different categories.
  • Image registration – comparing and combining two different views of the same object.
• Decision making Making the final decision required for the application, for example:
  • Pass/fail on automatic inspection applications
  • Match / no-match in recognition applications
  • Flag for further human review in medical, military, security and recognition applications