DialogOS

DialogOS is a graphical programming environment to design computer system which can converse through voice with the user. Dialogs are clicked together in a Flowchart. DialogOS includes bindings to control Lego Mindstorms robots by voice and has bindings to SQL databases, as well as a generic plugin architecture to integrate with other types of backends. DialogOS is used in computer science courses in schools and universities to teach programming and to introduce beginners in the basic principles of human/computer interaction and dialog design. It has also been used in research systems. DialogOS was initially developed commercially by CLT Sprachtechnologie GmbH until its liquidation in 2017. The rights were then acquired by Saarland University and the software was released as open-source. == Bindings to Lego Mindstorms NXT == DialogOS can control the LEGO Mindstorms NXT Series. It uses sensor-nodes to obtain values for the following sensors: noise sensor ultrasonic sensor touch sensor luminosity sensor

Luxafor

Luxafor () is a brand of office productivity tools designed to improve efficiency and communication in workplaces. The brands main product is LED status indicators for use in office settings. Luxafor is a product line under the company SIA Greynut, based in Riga, Latvia. == History == Luxafor was developed by the technology company SIA Greynut. The brand first gained attention through a Kickstarter campaign in 2015, which aimed to fund its initial product, the Luxafor Flag. Although the campaign was unsuccessful in reaching its funding goal, the product was still brought to market. In 2017, Luxafor launched another Kickstarter campaign for the Luxafor Bluetooth, a wireless version of its LED status indicator. This campaign also did not meet its funding goal, but like its predecessor, the product was still developed and released. Despite initial setbacks, Luxafor Bluetooth has become one of the brand's leading products. == Products == Luxafors main product range is LED status indicators, including: === Luxafor Flag === A USB-powered LED indicator that shows different colors to signal the user's availability. === Luxafor Bluetooth === A wireless LED indicator controlled via Bluetooth, integrating with productivity tools like Slack and Microsoft Teams. === Luxafor Switch === An advanced status indicator designed to manage room and workspace availability. === Other === Other Luxafor products include CO2 Dongle, Smart Button, Mute Button, Pomodoro Timer and others. == Features == Luxafor products are known for their customizable indicators, integration capabilities with IFTTT, Zapier, and remote control features. They are compatible with various operating systems, including Windows and macOS, and can be integrated with numerous communication and productivity platforms, like Microsoft Teams and Cisco Jabber.

Lexical Markup Framework

Language resource management – Lexical markup framework (LMF; ISO 24613), produced by ISO/TC 37, is the ISO standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in the contexts of multilingual communication. == Objectives == The goals of LMF are to provide a common model for the creation and use of lexical resources, to manage the exchange of data between and among these resources, and to enable the merging of large number of individual electronic resources to form extensive global electronic resources. Types of individual instantiations of LMF can include monolingual, bilingual or multilingual lexical resources. The same specifications are to be used for both small and large lexicons, for both simple and complex lexicons, for both written and spoken lexical representations. The descriptions range from morphology, syntax, computational semantics to computer-assisted translation. The covered languages are not restricted to European languages but cover all natural languages. The range of targeted NLP applications is not restricted. LMF is able to represent most lexicons, including WordNet, EDR and PAROLE lexicons. == History == In the past, lexicon standardization has been studied and developed by a series of projects like GENELEX, EDR, EAGLES, MULTEXT, PAROLE, SIMPLE and ISLE. Then, the ISO/TC 37 National delegations decided to address standards dedicated to NLP and lexicon representation. The work on LMF started in Summer 2003 by a new work item proposal issued by the US delegation. In Fall 2003, the French delegation issued a technical proposition for a data model dedicated to NLP lexicons. In early 2004, the ISO/TC 37 committee decided to form a common ISO project with Nicoletta Calzolari (CNR-ILC Italy) as convenor and Gil Francopoulo (Tagmatica France) and Monte George (ANSI, United States) as editors. The first step in developing LMF was to design an overall framework based on the general features of existing lexicons and to develop a consistent terminology to describe the components of those lexicons. The next step was the actual design of a comprehensive model that best represented all of the lexicons in detail. A large panel of 60 experts contributed a wide range of requirements for LMF that covered many types of NLP lexicons. The editors of LMF worked closely with the panel of experts to identify the best solutions and reach a consensus on the design of LMF. Special attention was paid to the morphology in order to provide powerful mechanisms for handling problems in several languages that were known as difficult to handle. 13 versions have been written, dispatched (to the National nominated experts), commented and discussed during various ISO technical meetings. After five years of work, including numerous face-to-face meetings and e-mail exchanges, the editors arrived at a coherent UML model. In conclusion, LMF should be considered a synthesis of the state of the art in NLP lexicon field. == Current stage == The ISO number is 24613. The LMF specification has been published officially as an International Standard on 17 November 2008. == As one of the members of the ISO/TC 37 family of standards == The ISO/TC 37 standards are currently elaborated as high level specifications and deal with word segmentation (ISO 24614), annotations (ISO 24611 a.k.a. MAF, ISO 24612 a.k.a. LAF, ISO 24615 a.k.a. SynAF, and ISO 24617-1 a.k.a. SemAF/Time), feature structures (ISO 24610), multimedia containers (ISO 24616 a.k.a. MLIF), and lexicons (ISO 24613). These standards are based on low level specifications dedicated to constants, namely data categories (revision of ISO 12620), language codes (ISO 639), scripts codes (ISO 15924), country codes (ISO 3166) and Unicode (ISO 10646). The two level organization forms a coherent family of standards with the following common and simple rules: the high level specification provides structural elements that are adorned by the standardized constants; the low level specifications provide standardized constants as metadata. == Key standards == The linguistics constants like /feminine/ or /transitive/ are not defined within LMF but are recorded in the Data Category Registry (DCR) that is maintained as a global resource by ISO/TC 37 in compliance with ISO/IEC 11179-3:2003. And these constants are used to adorn the high level structural elements. The LMF specification complies with the modeling principles of Unified Modeling Language (UML) as defined by Object Management Group (OMG). The structure is specified by means of UML class diagrams. The examples are presented by means of UML instance (or object) diagrams. An XML DTD is given in an annex of the LMF document. == Model structure == LMF is composed of the following components: The core package that is the structural skeleton which describes the basic hierarchy of information in a lexical entry. Extensions of the core package which are expressed in a framework that describes the reuse of the core components in conjunction with the additional components required for a specific lexical resource. The extensions are specifically dedicated to morphology, MRD, NLP syntax, NLP semantics, NLP multilingual notations, NLP morphological patterns, multiword expression patterns, and constraint expression patterns. == Example == In the following example, the lexical entry is associated with a lemma clergyman and two inflected forms clergyman and clergymen. The language coding is set for the whole lexical resource. The language value is set for the whole lexicon as shown in the following UML instance diagram. The elements Lexical Resource, Global Information, Lexicon, Lexical Entry, Lemma, and Word Form define the structure of the lexicon. They are specified within the LMF document. On the contrary, languageCoding, language, partOfSpeech, commonNoun, writtenForm, grammaticalNumber, singular, plural are data categories that are taken from the Data Category Registry. These marks adorn the structure. The values ISO 639-3, clergyman, clergymen are plain character strings. The value eng is taken from the list of languages as defined by ISO 639-3. With some additional information like dtdVersion and feat, the same data can be expressed by the following XML fragment: This example is rather simple, while LMF can represent much more complex linguistic descriptions the XML tagging is correspondingly complex. == Selected publications about LMF == The first publication about the LMF specification as it has been ratified by ISO (this paper became (in 2015) the 9th most cited paper within the Language Resources and Evaluation conferences from LREC papers): Language Resources and Evaluation LREC-2006/Genoa: Gil Francopoulo, Monte George, Nicoletta Calzolari, Monica Monachini, Nuria Bel, Mandy Pet, Claudia Soria: Lexical Markup Framework (LMF) About semantic representation: Gesellschaft für linguistische Datenverarbeitung GLDV-2007/Tübingen: Gil Francopoulo, Nuria Bel, Monte George Nicoletta Calzolari, Monica Monachini, Mandy Pet, Claudia Soria: Lexical Markup Framework ISO standard for semantic information in NLP lexicons About African languages: Traitement Automatique des langues naturelles, Marseille, 2014: Mouhamadou Khoule, Mouhamad Ndiankho Thiam, El Hadj Mamadou Nguer: Toward the establishment of a LMF-based Wolof language lexicon (Vers la mise en place d'un lexique basé sur LMF pour la langue wolof) [in French] About Asian languages: Lexicography, Journal of ASIALEX, Springer 2014: Lexical Markup Framework: Gil Francopoulo, Chu-Ren Huang: An ISO Standard for Electronic Lexicons and its Implications for Asian Languages DOI 10.1007/s40607-014-0006-z About European languages: COLING 2010: Verena Henrich, Erhard Hinrichs: Standardizing Wordnets in the ISO Standard LMF: Wordnet-LMF for GermaNet EACL 2012: Judith Eckle-Kohler, Iryna Gurevych: Subcat-LMF: Fleshing out a standardized format for subcategorization frame interoperability EACL 2012: Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M Meyer, Christian Wirth: UBY - A Large-Scale Unified Lexical-Semantic Resource Based on LMF. About Semitic languages: Journal of Natural Language Engineering, Cambridge University Press (to appear in Spring 2015): Aida Khemakhem, Bilel Gargouri, Abdelmajid Ben Hamadou, Gil Francopoulo: ISO Standard Modeling of a large Arabic Dictionary. Proceedings of the seventh Global Wordnet Conference 2014: Nadia B M Karmani, Hsan Soussou, Adel M Alimi: Building a standardized Wordnet in the ISO LMF for aeb language. Proceedings of the workshop: HLT & NLP within Arabic world, LREC 2008: Noureddine Loukil, Kais Haddar, Abdelmajid Ben Hamadou: Towards a syntactic lexicon of Arabic Verbs. Traitement Automatique des Langues Naturelles, Toulouse (in French) 2007: Khemakhem A, Gargouri B, Abdelwahed A, Francopoulo G: Modélisation des paradigmes de fl

Cross-language information retrieval

Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The term "cross-language information retrieval" has many synonyms, of which the following are perhaps the most frequent: cross-lingual information retrieval, translingual information retrieval, multilingual information retrieval. The term "multilingual information retrieval" refers more generally both to technology for retrieval of multilingual collections and to technology which has been moved to handle material in one language to another. The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects (text, and other media) of various languages, translated into the user's language. Cross-language information retrieval refers more specifically to the use case where users formulate their information need in one language and the system retrieves relevant documents in another. To do so, most CLIR systems use various translation techniques. CLIR techniques can be classified into different categories based on different translation resources: Dictionary-based CLIR techniques Parallel corpora based CLIR techniques Comparable corpora based CLIR techniques Machine translator based CLIR techniques CLIR systems have improved so much that the most accurate multi-lingual and cross-lingual adhoc information retrieval systems today are nearly as effective as monolingual systems. Other related information access tasks, such as media monitoring, information filtering and routing, sentiment analysis, and information extraction require more sophisticated models and typically more processing and analysis of the information items of interest. Much of that processing needs to be aware of the specifics of the target languages it is deployed in. Mostly, the various mechanisms of variation in human language pose coverage challenges for information retrieval systems: texts in a collection may treat a topic of interest but use terms or expressions which do not match the expression of information need given by the user. This can be true even in a mono-lingual case, but this is especially true in cross-lingual information retrieval, where users may know the target language only to some extent. The benefits of CLIR technology for users with poor to moderate competence in the target language has been found to be greater than for those who are fluent. Specific technologies in place for CLIR services include morphological analysis to handle inflection, decompounding or compound splitting to handle compound terms, and translations mechanisms to translate a query from one language to another. The first workshop on CLIR was held in Zürich during the SIGIR-96 conference. Workshops have been held yearly since 2000 at the meetings of the Cross Language Evaluation Forum (CLEF). Researchers also convene at the annual Text Retrieval Conference (TREC) to discuss their findings regarding different systems and methods of information retrieval, and the conference has served as a point of reference for the CLIR subfield. Early CLIR experiments were conducted at TREC-6, held at the National Institute of Standards and Technology (NIST) on November 19–21, 1997. Google Search had a cross-language search feature that was removed in 2013.

Production (computer science)

In computer science, a production or production rule is a rewrite rule that replaces some symbols with other symbols. A finite set of productions P {\displaystyle P} is the main component in the specification of a formal grammar (specifically a generative grammar). In such grammars, a set of productions is a special case of relation on the set of strings V ∗ {\displaystyle V^{}} (where ∗ {\displaystyle {}^{}} is the Kleene star operator) over a finite set of symbols V {\displaystyle V} called a vocabulary that defines which non-empty strings can be substituted with others. The set of productions is thus a special kind subset P ⊂ V ∗ × V ∗ {\displaystyle P\subset V^{}\times V^{}} and productions are then written in the form u → v {\displaystyle u\to v} to mean that ( u , v ) ∈ P {\displaystyle (u,v)\in P} (not to be confused with → {\displaystyle \to } being used as function notation, since there may be multiple rules for the same u {\displaystyle u} ). Given two subsets A , B ⊂ V ∗ {\displaystyle A,B\subset V^{}} , productions can be restricted to satisfy P ⊂ A × B {\displaystyle P\subset A\times B} , in which case productions are said "to be of the form A → B {\displaystyle A\to B} . Different choices and constructions of A , B {\displaystyle A,B} lead to different types of grammars. In general, any production of the form u → ϵ , {\displaystyle u\to \epsilon ,} where ϵ {\displaystyle \epsilon } is the empty string (sometimes also denoted λ {\displaystyle \lambda } ), is called an erasing rule, while productions that would produce strings out of nowhere, namely of the form ϵ → v , {\displaystyle \epsilon \to v,} are never allowed. In order to allow the production rules to create meaningful sentences, the vocabulary is partitioned into (disjoint) sets Σ {\displaystyle \Sigma } and N {\displaystyle N} providing two different roles: Σ {\displaystyle \Sigma } denotes the terminal symbols known as an alphabet containing the symbols allowed in a sentence; N {\displaystyle N} denotes nonterminal symbols, containing a distinguished start symbol S ∈ N {\displaystyle S\in N} , that are needed together with the production rules to define how to build the sentences. In the most general case of an unrestricted grammar, a production u → v {\displaystyle u\to v} , is allowed to map arbitrary strings u {\displaystyle u} and v {\displaystyle v} in V {\displaystyle V} (terminals and nonterminals), as long as u {\displaystyle u} is not empty. So unrestricted grammars have productions of the form V ∗ ∖ { ϵ } → V ∗ {\displaystyle V^{}\setminus \{\epsilon \}\to V^{}} or if we want to disallow changing finished sentences V ∗ N V ∗ = ( V ∗ ∖ Σ ∗ ) → V ∗ {\displaystyle V^{}NV^{}=(V^{}\setminus \Sigma ^{})\to V^{}} , where V ∗ N V ∗ {\displaystyle V^{}NV^{}} indicates concatenation and forces a non-terminal symbol to always be present on the left-hand side of the productions, and ∖ {\displaystyle \setminus } denotes set minus or set difference. If we do not allow the start symbol to occur in v {\displaystyle v} (the word on the right side), we have to replace V ∗ {\displaystyle V^{}} with ( V ∖ { S } ) ∗ {\displaystyle (V\setminus \{S\})^{}} on the right-hand side. The other types of formal grammar in the Chomsky hierarchy impose additional restrictions on what constitutes a production. Notably in a context-free grammar, the left-hand side of a production must be a single nonterminal symbol. So productions are of the form: N → V ∗ {\displaystyle N\to V^{}} == Grammar generation == To generate a string in the language, one begins with a string consisting of only a single start symbol, and then successively applies the rules (any number of times, in any order) to rewrite this string. This stops when a string containing only terminals is obtained. The language consists of all the strings that can be generated in this manner. Any particular sequence of legal choices taken during this rewriting process yields one particular string in the language. If there are multiple different ways of generating this single string, then the grammar is said to be ambiguous. For example, assume the alphabet consists of a {\displaystyle a} and b {\displaystyle b} , with the start symbol S {\displaystyle S} , and we have the following rules: 1. S → a S b {\displaystyle S\rightarrow aSb} 2. S → b a {\displaystyle S\rightarrow ba} then we start with S {\displaystyle S} , and can choose a rule to apply to it. If we choose rule 1, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a S b {\displaystyle aSb} . If we choose rule 1 again, we replace S {\displaystyle S} with a S b {\displaystyle aSb} and obtain the string a a S b b {\displaystyle aaSbb} . This process is repeated until we only have symbols from the alphabet (i.e., a {\displaystyle a} and b {\displaystyle b} ). If we now choose rule 2, we replace S {\displaystyle S} with b a {\displaystyle ba} and obtain the string a a b a b b {\displaystyle aababb} , and are done. We can write this series of choices more briefly, using symbols: S ⇒ a S b ⇒ a a S b b ⇒ a a b a b b {\displaystyle S\Rightarrow aSb\Rightarrow aaSbb\Rightarrow aababb} . The language of the grammar is the set of all the strings that can be generated using this process: { b a , a b a b , a a b a b b , a a a b a b b b , … } {\displaystyle \{ba,abab,aababb,aaababbb,\dotsc \}} .

Automated storage and retrieval system

An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie

Viola–Jones object detection framework

The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers. Each classifier is a single perceptron with several binary masks (Haar features). To detect faces in an image, a sliding window is computed over the image. For each image, the classifiers are applied. If at any point, a classifier outputs "no face detected", then the window is considered to contain no face. Otherwise, if all classifiers output "face detected", then the window is considered to contain a face. The algorithm is efficient for its time, able to detect faces in 384 by 288 pixel images at 15 frames per second on a conventional 700 MHz Intel Pentium III. It is also robust, achieving high precision and recall. While it has lower accuracy than more modern methods such as convolutional neural network, its efficiency and compact size (only around 50k parameters, compared to millions of parameters for typical CNN like DeepFace) means it is still used in cases with limited computational power. For example, in the original paper, they reported that this face detector could run on the Compaq iPAQ at 2 fps (this device has a low power StrongARM without floating point hardware). == Problem description == Face detection is a binary classification problem combined with a localization problem: given a picture, decide whether it contains faces, and construct bounding boxes for the faces. To make the task more manageable, the Viola–Jones algorithm only detects full view (no occlusion), frontal (no head-turning), upright (no rotation), well-lit, full-sized (occupying most of the frame) faces in fixed-resolution images. The restrictions are not as severe as they appear, as one can normalize the picture to bring it closer to the requirements for Viola-Jones. any image can be scaled to a fixed resolution for a general picture with a face of unknown size and orientation, one can perform blob detection to discover potential faces, then scale and rotate them into the upright, full-sized position. the brightness of the image can be corrected by white balancing. the bounding boxes can be found by sliding a window across the entire picture, and marking down every window that contains a face. This would generally detect the same face multiple times, for which duplication removal methods, such as non-maximal suppression, can be used. The "frontal" requirement is non-negotiable, as there is no simple transformation on the image that can turn a face from a side view to a frontal view. However, one can train multiple Viola-Jones classifiers, one for each angle: one for frontal view, one for 3/4 view, one for profile view, a few more for the angles in-between them. Then one can at run time execute all these classifiers in parallel to detect faces at different view angles. The "full-view" requirement is also non-negotiable, and cannot be simply dealt with by training more Viola-Jones classifiers, since there are too many possible ways to occlude a face. == Components of the framework == A full presentation of the algorithm is in. Consider an image I ( x , y ) {\displaystyle I(x,y)} of fixed resolution ( M , N ) {\displaystyle (M,N)} . Our task is to make a binary decision: whether it is a photo of a standardized face (frontal, well-lit, etc) or not. Viola–Jones is essentially a boosted feature learning algorithm, trained by running a modified AdaBoost algorithm on Haar feature classifiers to find a sequence of classifiers f 1 , f 2 , . . . , f k {\displaystyle f_{1},f_{2},...,f_{k}} . Haar feature classifiers are crude, but allows very fast computation, and the modified AdaBoost constructs a strong classifier out of many weak ones. At run time, a given image I {\displaystyle I} is tested on f 1 ( I ) , f 2 ( I ) , . . . f k ( I ) {\displaystyle f_{1}(I),f_{2}(I),...f_{k}(I)} sequentially. If at any point, f i ( I ) = 0 {\displaystyle f_{i}(I)=0} , the algorithm immediately returns "no face detected". If all classifiers return 1, then the algorithm returns "face detected". For this reason, the Viola-Jones classifier is also called "Haar cascade classifier". === Haar feature classifiers === Consider a perceptron f w , b {\displaystyle f_{w,b}} defined by two variables w ( x , y ) , b {\displaystyle w(x,y),b} . It takes in an image I ( x , y ) {\displaystyle I(x,y)} of fixed resolution, and returns f w , b ( I ) = { 1 , if ∑ x , y w ( x , y ) I ( x , y ) + b > 0 0 , else {\displaystyle f_{w,b}(I)={\begin{cases}1,\quad {\text{if }}\sum _{x,y}w(x,y)I(x,y)+b>0\\0,\quad {\text{else}}\end{cases}}} A Haar feature classifier is a perceptron f w , b {\displaystyle f_{w,b}} with a very special kind of w {\displaystyle w} that makes it extremely cheap to calculate. Namely, if we write out the matrix w ( x , y ) {\displaystyle w(x,y)} , we find that it takes only three possible values { + 1 , − 1 , 0 } {\displaystyle \{+1,-1,0\}} , and if we color the matrix with white on + 1 {\displaystyle +1} , black on − 1 {\displaystyle -1} , and transparent on 0 {\displaystyle 0} , the matrix is in one of the 5 possible patterns shown on the right. Each pattern must also be symmetric to x-reflection and y-reflection (ignoring the color change), so for example, for the horizontal white-black feature, the two rectangles must be of the same width. For the vertical white-black-white feature, the white rectangles must be of the same height, but there is no restriction on the black rectangle's height. ==== Rationale for Haar features ==== The Haar features used in the Viola-Jones algorithm are a subset of the more general Haar basis functions, which have been used previously in the realm of image-based object detection. While crude compared to alternatives such as steerable filters, Haar features are sufficiently complex to match features of typical human faces. For example: The eye region is darker than the upper-cheeks. The nose bridge region is brighter than the eyes. Composition of properties forming matchable facial features: Location and size: eyes, mouth, bridge of nose Value: oriented gradients of pixel intensities Further, the design of Haar features allows for efficient computation of f w , b ( I ) {\displaystyle f_{w,b}(I)} using only constant number of additions and subtractions, regardless of the size of the rectangular features, using the summed-area table. === Learning and using a Viola–Jones classifier === Choose a resolution ( M , N ) {\displaystyle (M,N)} for the images to be classified. In the original paper, they recommended ( M , N ) = ( 24 , 24 ) {\displaystyle (M,N)=(24,24)} . ==== Learning ==== Collect a training set, with some containing faces, and others not containing faces. Perform a certain modified AdaBoost training on the set of all Haar feature classifiers of dimension ( M , N ) {\displaystyle (M,N)} , until a desired level of precision and recall is reached. The modified AdaBoost algorithm would output a sequence of Haar feature classifiers f 1 , f 2 , . . . , f k {\displaystyle f_{1},f_{2},...,f_{k}} . The details of the modified AdaBoost algorithm is detailed below. ==== Using ==== To use a Viola-Jones classifier with f 1 , f 2 , . . . , f k {\displaystyle f_{1},f_{2},...,f_{k}} on an image I {\displaystyle I} , compute f 1 ( I ) , f 2 ( I ) , . . . f k ( I ) {\displaystyle f_{1}(I),f_{2}(I),...f_{k}(I)} sequentially. If at any point, f i ( I ) = 0 {\displaystyle f_{i}(I)=0} , the algorithm immediately returns "no face detected". If all classifiers return 1, then the algorithm returns "face detected". === Learning algorithm === The speed with which features may be evaluated does not adequately compensate for their number, however. For example, in a standard 24x24 pixel sub-window, there are a total of M = 162336 possible features, and it would be prohibitively expensive to evaluate them all when testing an image. Thus, the object detection framework employs a variant of the learning algorithm AdaBoost to both select the best features and to train classifiers that use them. This algorithm constructs a "strong" classifier as a linear combination of weighted simple “weak” classifiers. h ( x ) = sgn ⁡ ( ∑ j = 1 M α j h j ( x ) ) {\displaystyle h(\mathbf {x} )=\operatorname {sgn} \left(\sum _{j=1}^{M}\alpha _{j}h_{j}(\mathbf {x} )\right)} Each weak classifier is a threshold function based on the feature f j {\displaystyle f_{j}} . h j ( x ) = { − s j if f j < θ j s j otherwise {\displaystyle h_{j}(\mathbf {x} )={\begin{cases}-s_{j}&{\text{if }}f_{j}<\theta _{j}\\s_{j}&{\text{otherwise}}\end{cases}}} The threshold value θ j {\displaystyle \theta _{j}} and the polarity s j ∈ ± 1 {\displaystyle s_{j}\in \pm 1} are determined in the training, as well as the coefficients α j {\displaystyle \alpha _{j}} . Here a simplified version of the lea