page_title: Computer vision
section_title: Applications
hierarchical_section_title: Computer vision / Applications
caption_attribution_description: English: Generate and reconstruct 3D shapes via modeling multi-view depth maps or silhouettes [1]
context_page_description: Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
  Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
  The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner or medical scanning device.
context_section_description: Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:
  Automatic inspection, e.g., in manufacturing applications;
  Assisting humans in identification tasks, e.g., a species identification system;
  Controlling processes, e.g., an industrial robot;
  Detecting events, e.g., for visual surveillance or people counting, e.g., in the restaurant industry;
  Interaction, e.g., as the input to a device for computer-human interaction;
  Modeling objects or environments, e.g., medical image analysis or topographical modeling;
  Navigation, e.g., by an autonomous vehicle or mobile robot; and
  Organizing information, e.g., for indexing databases of images and image sequences.
caption: Learning 3D shapes has been a challenging task in computer vision. Recent advances in deep learning has enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view depth maps or silhouettes seamlessly and efficiently [20]
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