computer science

Outline of computer vision

Outline of computer vision is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Outline of computer vision rather than just read about it. In short: The following outline is provided as an overview of and topical guide to computer vision: Computer vision – interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.

Key takeaways

Outline of computer vision belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Outline of computer vision to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Outline of computer vision from memory before moving on to harder problems.

Reference excerpt

The following outline is provided as an overview of and topical guide to computer vision: Computer vision – interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring digital images (through image sensors), image processing, and image analysis, to reach an understanding of digital images. In general, it deals with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information that the computer can interpret. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images.

Branches of computer vision Computer stereo vision Underwater computer vision

History of computer vision History of computer vision

Computer vision subsystems

Image enhancement Image denoising Image histogram Inpainting Super-resolution imaging Histogram equalization Tone mapping Retinex Gamma correction Anisotropic diffusion (Perona–Malik equation)

Transformations Affine transform Homography (computer vision) Hough transform Radon transform Walsh–Hadamard transform

Filtering, Fourier and wavelet transforms and image compression Image compression Filter bank Gabor filter JPEG 2000 Adaptive filtering

Color vision Visual perception Human visual system model Color matching function Color space Color appearance model Color management system Color mapping Color model Color profile

Feature extraction Active contour Blob detection Canny edge detector Contour detection Edge detection Edge linking Harris Corner Detector Histogram of oriented gradients (HOG) Random sample consensus (RANSAC) Scale-invariant feature transform (SIFT)

Pose estimation Bundle adjustment Articulated body pose estimation (BoPoE) Direct linear transformation (DLT) Epipolar geometry Fundamental matrix Pinhole camera model Projective geometry Trifocal tensor

Registration Active appearance model (AAM) Cross-correlation Geometric hashing Graph cut segmentation Least squares estimation Image pyramid Image segmentation Level-set method Markov random fields Medial axis Motion field Motion vector Multispectral imaging Normalized cut segmentation Optical flow Particle filtering Scale space

Visual recognition Object recognition Scale-invariant feature transform (SIFT) Gesture recognition Bag-of-words model in computer vision Kadir–Brady saliency detector Eigenface

Commercial computer vision systems 5DX Aphelion (software) Microsoft PixelSense Poseidon drowning detection system Roboflow Visage SDK

Applications 3D reconstruction from multiple images Audio-visual speech recognition Augmented reality Augmented reality-assisted surgery Automated optical inspection Automatic image annotation Automatic number plate recognition Automatic target recognition Check weigher Closed-circuit television Computer stereo vision Contextual image classification DARPA LAGR Program Digital video fingerprinting Document mosaicing Facial recognition systems GazoPa Geometric feature learning Gesture recognition Image collection exploration Image retrieval Content-based image retrieval Reverse image search Image-based modeling and rendering Integrated mail processing Iris recognition Machine vision Mobile mapping Navigation system components for: Autonomous cars Mobile robots Object detection Optical braille recognition Optical character recognition Intelligent character recognition Pedestrian detection People counter Physical computing Red light camera Remote sensing Smart camera Traffic enforcement camera Traffic sign recognition Vehicle infrastructure integration Velocity Moments Video content analysis View synthesis Visual sensor network Visual Word Water remote sensing

Computer vision companies 3DFLOW Automatix Clarifai Cognex Corporation Datagen Diffbot IBM InspecVision Isra Vision Kinesense Mobileye Roboflow Scantron Corporation Teledyne DALSA VIEW Engineering Warden Machinery Zivid

Computer vision publications Electronic Letters on Computer Vision and Image Analysis International Journal of Computer Vision

Computer vision organizations Conference on Computer Vision and Pattern Recognition European Conference on Computer Vision International Conference on Computer Vision International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision

Persons influential in computer vision

See also Outline of artificial intelligence Outline of deep learning Outline of robotics List of computer graphics and descriptive geometry topics Virtual Design and Construction

References

External links

USC Iris computer vision conference list Computer vision papers on the web A complete list of papers of the most relevant computer vision conferences. Computer Vision Online News, source code, datasets and job offers related to computer vision. Keith Price's Annotated Computer Vision Bibliography CVonline Bob Fisher's Compendium of Computer Vision. British Machine Vision Association Supporting computer vision research within the UK via the BMVC and MIUA conferences, Annals of the BMVA (open-source journal), BMVA Summer School and one-day meetings

Worked examples

Example 1 — a first encounter with Outline of computer vision

Start with the simplest possible case. Write down what Outline of computer vision claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Outline of computer vision before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Outline of computer vision ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Outline of computer vision

In research: Outline of computer vision appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Outline of computer vision in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Outline of computer vision is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer vision, Computing-related lists, Outlines, so understanding it makes those chapters shorter.
In everyday life: Look for Outline of computer vision outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Outline of computer vision in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Outline of computer vision means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Outline of computer vision out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Outline of computer vision in simple terms?

Why does Outline of computer vision matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Outline of computer vision?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Outline of computer vision.