Computer Vision

Computer vision is a multidisciplinary field that could be called a subfield of artificial intelligence and machine learning. Because of this, computer vision borrows and reuses techniques from a range of disparate engineering and computer science field. Computer vision methods and systems are highly application dependent. Some systems can stand-alone to solve specific measurement or detection problems. Others are sub-systems of a large design which work in the larger system for control of mechanical actuators, planning, information database, and man-machine interfaces. The specific implementation of a computer vision subsystem will also depend on its intended functionality.

In computer vision, image processing, and machine vision the purpose of determining whether or not an image data contains a specific object, feature, or activity. Computer vision for recognition includes tasks such as image classification or identification, object localization and object detection.

The best algorithms for recognition tasks are based on convolutional neural networks. The algorithms still struggle with objects that are small or thin, such as an ant on a stem of a flower or a person holding a narrow pen. And they have trouble with images which have been distorted with filters.

In object detection, a computer vision system will process image data for a specific condition. This includes the detection of possible abnormal cells or tissues in medical images or the detection of a vehicle in an automatic road toll system. Detection based on 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.

Part of computer vision, image restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. The corruption may occur due to the image-capture process (from signal noise or lens blur), post-processing (from file compression), or photography in non-ideal conditions (such that there is haze or motion blur). The computer vision systems will restore the images by analyzing the image data in terms of the local image structures, such as lines or edges, and control the filtering based on these structures for better image noise removal compared to simpler approaches.