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Glossary of computer graphics
This is a glossary of terms relating to computer graphics. For more general computer hardware terms, see glossary of computer hardware terms. == 0–9 == 2D convolution Operation that applies linear filtering to image with a given two-dimensional kernel, able to achieve e.g. edge detection, blurring, etc. 2D image 2D texture map A texture map with two dimensions, typically indexed by UV coordinates. 2D vector A two-dimensional vector, a common data type in rasterization algorithms, 2D computer graphics, graphical user interface libraries. 2.5D Also pseudo 3D. Rendering whose result looks 3D while actually not being 3D or having great limitations, e.g. in camera degrees of freedom. 3D graphics pipeline A graphics pipeline taking 3D models and producing a 2D bitmap image result. 3D paint tool A 3D graphics application for digital painting of multiple texture map image channels directly onto a rotated 3D model, such as zbrush or mudbox, also sometimes able to modify vertex attributes. 3D scene A collection of 3D models and lightsources in world space, into which a camera may be placed, describing a scene for 3D rendering. 3D unit vector A unit vector in 3D space. 4D vector A common datatype in graphics code, holding homogeneous coordinates or RGBA data, or simply a 3D vector with unused W to benefit from alignment, naturally handled by machines with 4-element SIMD registers. 4×4 matrix A matrix commonly used as a transformation of homogeneous coordinates in 3D graphics pipelines. 7e3 format A packed pixel format supported by some graphics processing units (GPUs) where a single 32-bit word encodes three 10-bit floating-point color channels, each with seven bits of mantissa and three bits of exponent. == A == AABB Axis-aligned bounding box (sometimes called "axis oriented"), a bounding box stored in world coordinates; one of the simplest bounding volumes. Additive blending A compositing operation where d s t = d s t + s r c , {\displaystyle dst=dst+src,} without the use of an alpha channel, used for various effects. Also known as linear dodge in some applications. Affine texture mapping Linear interpolation of texture coordinates in screen space without taking perspective into account, causing texture distortion. Aliasing Unwanted effect arising when sampling high-frequency signals, in computer graphics appearing e.g. when downscaling images. Antialiasing methods can prevent it. Alpha channel An additional image channel (e.g. extending an RGB image) or standalone channel controlling alpha blending. Ambient lighting An approximation to the light entering a region from a wide range of directions, used to avoid needing an exact solution to the rendering equation. Ambient occlusion (AO) Effect approximating, in an inexpensive way, one aspect of global illumination by taking into account how much ambient light is blocked by nearby geometry, adding visual clues about the shape. Analytic model A mathematical model for a phenomenon to be simulated, e.g. some approximation to surface shading. Contrasts with Empirical models based purely on recorded data. Anisotropic filtering Advanced texture filtering improving on mipmapping, preventing aliasing while reducing blur in textured polygons at oblique angles to the camera. Anti-aliasing Methods for filtering and sampling to avoid visual artifacts associated with the uniform pixel grid in 3D rendering. Array texture A form of texture map containing an array of 2D texture slices selectable by a 3rd 'W' texture coordinate; used to reduce state changes in 3D rendering. Augmented reality Computer-rendered content inserted into the user's view of the real world. AZDO Approaching zero driver overhead, a set of techniques aimed at reducing the CPU overhead in preparing and submitting rendering commands in the OpenGL pipeline. A compromise between the traditional GL API and other high-performance low-level rendering APIs. == B == Back-face culling Culling (discarding) of polygons that are facing backwards from the camera. Baking Performing an expensive calculation offline, and caching the results in a texture map or vertex attributes. Typically used for generating lightmaps, normal maps, or low level of detail models. Barycentric coordinates Three-element coordinates of a point inside a triangle. Beam tracing Modification of ray tracing which instead of lines uses pyramid-shaped beams to address some of the shortcomings of traditional ray tracing, such as aliasing. Bicubic interpolation Extension of cubic interpolation to 2D, commonly used when scaling textures. Bilinear interpolation Linear interpolation extended to 2D, commonly used when scaling textures. Binding Selecting a resource (texture, buffer, etc.) to be referenced by future commands. Billboard A textured rectangle that keeps itself oriented towards the camera, typically used e.g. for vegetation or particle effects. Binary space partitioning (BSP) A data structure that can be used to accelerate visibility determination, used e.g. in Doom engine. Bit depth The number of bits per pixel, sample, or texel in a bitmap image (holding one or more image channels, typical values being 4, 8, 16, 24, 32) Bitmap Image stored by pixels. Bit plane A format for bitmap images storing 1 bit per pixel in a contiguous 2D array; Several such parallel arrays combine to produce the a higher-bit-depth image. Opposite of packed-pixel format. Blend operation A render state controlling alpha blending, describing a formula for combining source and destination pixels. Bone Coordinate systems used to control surface deformation (via Weight maps) during skeletal animation. Typically stored in a hierarchy, controlled by key frames, and other procedural constraints. Bounding box One of the simplest type of bounding volume, consisting of axis-aligned or object-aligned extents. Bounding volume A mathematically simple volume, such as a sphere or a box, containing 3D objects, used to simplify and accelerate spatial tests (e.g. for visibility or collisions). BRDF Bidirectional reflectance distribution functions (BRDFs), empirical models defining 4D functions for surface shading indexed by a view vector and light vector relative to a surface. Bump mapping Technique similar to normal mapping that instead of normal maps uses so called bump maps (height maps). BVH Bounding volume hierarchy is a tree structure on a set of geometric objects. == C == Camera A virtual camera from which rendering is performed, also sometimes referred to as 'eye'. Camera space A space with the camera at the origin, aligned with the viewer's direction, after the application of the world transformation and view transformation. Cel shading Cartoon-like shading effect. Clipping Limiting specific operations to a specific region, usually the view frustum. Clipping plane A plane used to clip rendering primitives in a graphics pipeline. These may define the view frustum or be used for other effects. Clip space Coordinate space in which clipping is performed. Clip window A rectangular region in screen space, used during clipping. A clip window may be used to enclose a region around a portal in portal rendering. CLUT A table of RGB color values to be indexed by a lower-bit-depth image (typically 4–8 bits), a form of vector quantization. Color bleeding Unwanted effect in texture mapping. A color from a border of unmapped region of the texture may appear (bleed) in the mapped result due to interpolation. Color channels The set of channels in a bitmap image representing the visible color components, i.e. distinct from the alpha channel or other information. Color resolution Command buffer A region of memory holding a set of instructions for a graphics processing unit for rendering a scene or portion of a scene. These may be generated manually in bare metal programming, or managed by low level rendering APIs, or handled internally by high level rendering APIs. Command list A group of rendering commands ready for submission to a graphics processing unit, see also Command buffer. Compute API An API for efficiently processing large amounts of data. Compute shader A compute kernel managed by a rendering API, with easy access to rendering resources. Cone tracing Modification of ray tracing which instead of lines uses cones as rays in order to achieve e.g. antialiasing or soft shadows. Connectivity information Indices defining [rendering primitive]s between vertices, possibly held in index buffers. describes geometry as a graph or hypergraph. CSG Constructive solid geometry, a method for generating complex solid models from boolean operations combining simpler modelling primitives. Cube mapping A form of environment reflection mapping in which the environment is captured on a surface of a cube (cube map). Culling Before rendering begins, culling removes objects that don't significantly contribute to the rendered result (e.g. being obscured or outside camera view). == D == Decal A "sticker" picture applied onto a surface (e.g. a
OntoUML
OntoUML is a language for ontology-driven conceptual modeling. OntoUML is built as a UML extension based on the Unified Foundational Ontology. The foundations of UFO and OntoUML can be traced back to Giancarlo Guizzardi's Ph.D. thesis "Ontological foundations for structural conceptual models". In his work, he proposed a novel foundational ontology for conceptual modeling (UFO) and employed it to evaluate and re-design a fragment of the UML 2.0 metamodel for the purposes of conceptual modeling and domain ontology engineering. == Supporting tools == In 2006, Guizzardi co-founded the Ontology & Conceptual Modeling Research Group (NEMO) located at the Federal University of Espírito Santo (UFES) in Vitória city, state of Espírito Santo, Brazil. Since then, NEMO has been responsible for most of the developments in OntoUML. Several papers about ontologies and OntoUML have been authored by members of the NEMO group.
OpenL Tablets
OpenL Tablets is a business rule management system (BRMS) and a business rules engine (BRE) based on table representation of rules. Engine implements optimized sequential algorithm. OpenL includes such table types as decision table, decision tree, spreadsheet-like calculator. == History == The OpenL Tablets project was started as an in-house development project in 2003 and later in 2006 was uploaded to SourceForge. Initially it was an open-source business rule engine for Java. Starting from version 5 it became a BRMS. == Technology == OpenL Tablets engine is specially designed for business rules and uses table rules presentation. Table format enforces rules to be structured and format itself is close to tables found in various business documents. OpenL Tablets is based on OpenL framework for creating custom languages running on Java VM. The engine is designed to allow pluggable language implementations. Currently, it uses 2 languages: table structure for rules format and java-like for code snippets in rules. Java-like language is Java 5.0 implementation with Business User Extensions. OpenL Tablets rules are mixture of declarative programming for rules logic and imperative programming for workflow control. Table formats are flexible enough to match the semantics of the problem domain. Tests, traces, benchmarks are integral part of the engine. It also provides powerful type definition capabilities to handle rules domain model inside rules files. The project is written in Java, but can be used at any platform using Service-oriented architecture approach, e.g. via web service. === Patents === The OpenL Tablets engine has patent pending validation feature. There are usages of OpenL Tablets which may be patented. == BRMS == OpenL Tablets includes several productivity tools and applications addressing BRMS related capabilities. They include web application to edit rules called OpenL WebStudio, web application to deploy rules as web services, Rules Repository to store and manage rules, Eclipse plug-ins to work with rules projects. == Related systems == CLIPS: public domain software tool for building expert systems. ILOG rules: a business rule management system. JBoss Drools: a business rule management system (BRMS). JESS: a rule engine for the Java platform - it is a superset of CLIPS programming language. Prolog: a general purpose logic programming language. DTRules: a Decision Table-based, open-sourced rule engine for Java.
BabelNet
BabelNet is a multilingual lexical-semantic knowledge graph, ontology and encyclopedic dictionary developed at the NLP group of the Sapienza University of Rome under the supervision of Roberto Navigli. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. For each Babel synset, BabelNet provides short definitions (called glosses) in many languages harvested from both WordNet and Wikipedia. == Statistics of BabelNet == As of December 2023, BabelNet (version 5.3) covers 600 languages. It contains almost 23 million synsets and around 1.7 billion word senses (regardless of their language). Each Babel synset contains 2 synonyms per language, i.e., word senses, on average. The semantic network includes all the lexico-semantic relations from WordNet (hypernymy and hyponymy, meronymy and holonymy, antonymy and synonymy, etc., totaling around 364,000 relation edges) as well as an underspecified relatedness relation from Wikipedia (totaling around 1.9 billion edges). Version 5.3 also associates around 61 million images with Babel synsets and provides a Lemon RDF encoding of the resource, available via a SPARQL endpoint. 2.67 million synsets are assigned domain labels. == Applications == BabelNet has been shown to enable multilingual natural language processing applications. The lexicalized knowledge available in BabelNet has been shown to obtain state-of-the-art results in: Semantic relatedness, Multilingual word-sense disambiguation and entity linking, with the Babelfy system, Video games with a purpose. == Prizes and acknowledgments == BabelNet received the META prize 2015 for "groundbreaking work in overcoming language barriers through a multilingual lexicalised semantic network and ontology making use of heterogeneous data sources". The Artificial Intelligence Journal paper that describes BabelNet won the Prominent Paper Award in 2017. BabelNet featured prominently in a Time magazine article about the new age of innovative and up-to-date lexical knowledge resources available on the Web.
Viewport
A viewport is a polygon viewing region in computer graphics. In computer graphics theory, there are two region-like notions of relevance when rendering some objects to an image. In textbook terminology, the world coordinate window is the area of interest (meaning what the user wants to visualize) in some application-specific coordinates, e.g. miles, centimeters etc. The word window as used here should not be confused with the GUI window, i.e. the notion used in window managers. Rather it is an analogy with how a window limits what one can see outside a room. In contrast, the viewport is an area (typically rectangular) expressed in rendering-device-specific coordinates, e.g. pixels for screen coordinates, in which the objects of interest are going to be rendered. Clipping to the world-coordinates window is usually applied to the objects before they are passed through the window-to-viewport transformation. For a 2D object, the latter transformation is simply a combination of translation and scaling, the latter not necessarily uniform. An analogy of this transformation process based on traditional photography notions is to equate the world-clipping window with the camera settings and the variously sized prints that can be obtained from the resulting film image as possible viewports. Because the physical-device-based coordinates may not be portable from one device to another, a software abstraction layer known as normalized device coordinates is typically introduced for expressing viewports; it appears for example in the Graphical Kernel System (GKS) and later systems inspired from it. In 3D computer graphics, the viewport refers to the 2D rectangle used to project the 3D scene to the position of a virtual camera. A viewport is a region of the screen used to display a portion of the total image to be shown. In virtual desktops, the viewport is the visible portion of a 2D area which is larger than the visualization device. When viewing a document in a web browser, the viewport is the region of the browser window which contains the visible portion of the document. If the size of the viewport changes, for example as a result of the user resizing the browser window, then the browser may reflow the document (recalculate the locations and sizes of elements of the document). If the document is larger than the viewport, the user can control the portion of the document which is visible by scrolling in the viewport.
Darkforest
Darkforest is a computer go program developed by Meta Platforms, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them. With the update, the system is known as Darkfmcts3. Darkforest is of similar strength to programs like CrazyStone and Zen. It has been tested against a professional human player at the 2016 UEC cup. Google's AlphaGo program won against a professional player in October 2015 using a similar combination of techniques. Darkforest is named after Liu Cixin's science fiction novel The Dark Forest. == Background == Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on cutting-edge hardware, and Go's evaluation function could change drastically with one stone change. However, by using a Deep Convolutional Neural Network designed for long-term predictions, Darkforest has been able to substantially improve the win rate for bots over more traditional Monte Carlo Tree Search based approaches. === Matches === Against human players, Darkfores2 achieves a stable 3d ranking on KGS Go Server, which roughly corresponds to an advanced amateur human player. However, after adding Monte Carlo Tree Search to Darkfores2 to create a much stronger player named darkfmcts3, it can achieve a 5d ranking on the KGS Go Server. ==== Against other AI ==== darkfmcts3 is on par with state-of-the-art Go AIs such as Zen, DolBaram and Crazy Stone, but lags behind AlphaGo. It won 3rd place in January 2016 KGS Bot Tournament against other Go AIs. === News coverage === After Google's AlphaGo won against Fan Hui in 2015, Facebook made its AI's hardware designs public, alongside releasing the code behind DarkForest as open-source, in addition to heavy recruiting to strengthen its team of AI engineers. == Style of play == Darkforest uses a neural network to sort through the 10100 board positions, and find the most powerful next move. However, neural networks alone cannot match the level of good amateur players or the best search-based Go engines, and so Darkfores2 combines the neural network approach with a search-based machine. A database of 250,000 real Go games were used in the development of Darkforest, with 220,000 used as a training set and the rest used to test the neural network's ability to predict the next moves played in the real games. This allows Darkforest to accurately evaluate the global state of the board, but local tactics were still poor. Search-based engines have poor global evaluation, but are good at local tactics. Combining these two approaches is difficult because search-based engines work much faster than neural networks, a problem which was solved in Darkfores2 by running the processes in parallel with frequent communication between the two. === Conventional strategies === Go is generally played by analyzing the position of the stones on the board. Various advanced players have described it as playing in some part subconsciously. Unlike chess and checkers, where AI players can simply look further forward at moves than human players, but with each round of Go having on average 250 possible moves, that approach is ineffective. Instead, neural networks copy human play by training the AI systems on images of successful moves, the AI can effectively learn how to interpret how the board looks, as many grandmasters do. In November 2015, Facebook demonstrated the combination of MCTS with neural networks, which played with a style that "felt human". === Flaws === It has been noted that Darkforest still has flaws in its playstyle. The bot sometimes plays tenuki ("move elsewhere") pointlessly when local powerful moves are required. When the bot is losing, it shows the typical behavior of MCTS, it plays bad moves and loses more. The Facebook AI team has acknowledged these as areas of future improvement. == Program architecture == The family of Darkforest computer go programs is based on convolution neural networks. The most recent advances in Darkfmcts3 combined convolutional neural networks with more traditional Monte Carlo tree search. Darkfmcts3 is the most advanced version of Darkforest, which combines Facebook's most advanced convolutional neural network architecture from Darkfores2 with a Monte Carlo tree search. Darkfmcts3 relies on a convolution neural networks that predicts the next k moves based on the current state of play. It treats the board as a 19x19 image with multiple channels. Each channel represents a different aspect of board information based upon the specific style of play. For standard and extended play, there are 21 and 25 different channels, respectively. In standard play, each players liberties are represented as six binary channels or planes. The respective plane is true if the player one, two, or three or more liberties available. Ko (i.e. illegal moves) is represented as one binary plane. Stone placement for each opponent and empty board positions are represented as three binary planes, and the duration since a stone has been placed is represented as real numbers on two planes, one for each player. Lastly, the opponents rank is represented by nine binary planes, where if all are true, the player is a 9d level, if 8 are true, an 8d level, and so forth. Extended play additionally considers the border (binary plane that is true at the border), position mask (represented as distance from the board center, i.e. x ( − 0.5 ∗ d i s t a n c e 2 ) {\displaystyle x^{(-0.5distance^{2})}} , where x {\displaystyle x} is a real number at a position), and each player's territory (binary, based on which player a location is closer to). Darkfmct3 uses a 12-layer full convolutional network with a width of 384 nodes without weight sharing or pooling. Each convolutional layer is followed by a rectified linear unit, a popular activation function for deep neural networks. A key innovation of Darkfmct3 compared to previous approaches is that it uses only one softmax function to predict the next move, which enables the approach to reduce the overall number of parameters. Darkfmct3 was trained against 300 random selected games from an empirical dataset representing different game stages. The learning rate was determined by vanilla stochastic gradient descent. Darkfmct3 synchronously couples a convolutional neural network with a Monte Carlo tree search. Since the convolutional neural network is computationally taxing, the Monte Carlo tree search focuses computation on the more likely game play trajectories. By running the neural network synchronously with the Monte Carlo tree search, it is possible to guarantee that each node is expanded by the moves predicted by the neural network. == Comparison with other systems == Darkfores2 beats Darkforest, its neural network-only predecessor, around 90% of the time, and Pachi, one of the best search-based engines, around 95% of the time. On the Kyu rating system, Darkforest holds a 1-2d level. Darkfores2 achieves a stable 3d level on KGS Go Server as a ranked bot. With the added Monte Carlo tree search, Darkfmcts3 with 5,000 rollouts beats Pachi with 10k rollouts in all 250 games; with 75k rollouts it achieves a stable 5d level in KGS server, on par with state-of-the-art Go AIs (e.g., Zen, DolBaram, CrazyStone); with 110k rollouts, it won the 3rd place in January KGS Go Tournament.