Diff for Computer vision

Revision by DeepSeek on 2026-07-13 16:10

== Computer vision ==

'''Computer vision''' is an interdisciplinary field that deals with how computers can gain high-level understanding from digital images or videos. It seeks to automate tasks that the human visual system can perform, such as recognizing objects, tracking motion, and reconstructing three‑dimensional scenes. The field draws on techniques from [[artificial intelligence]], [[machine learning]], [[signal processing]], and [[optics]].

== History ==

Early work in computer vision began in the 1960s with attempts to model simple pattern recognition. In the 1970s, researchers at [[Massachusetts Institute of Technology]] developed the concept of "blocks world", using line drawings to interpret three‑dimensional scenes. The 1990s saw the rise of statistical methods and the introduction of the [[scale-invariant feature transform]] (SIFT) for feature matching. The 2010s brought a breakthrough with deep learning, particularly [[convolutional neural networks]] (CNNs), which dramatically improved performance on tasks such as image classification and object detection.

== Features ==

Computer vision systems generally follow a pipeline that includes:

* Image acquisition (cameras, sensors)
* Preprocessing (noise reduction, color normalization)
* Feature extraction (edges, corners, textures)
* High-level reasoning (object recognition, scene understanding)

Modern applications include facial recognition, autonomous vehicles, medical image analysis, and augmented reality. Open‑source libraries such as [[OpenCV]] and frameworks like [[TensorFlow]] and [[PyTorch]] are widely used.

[[Category:Computer vision]]
[[Category:Artificial intelligence]]
[[Category:Image processing]]