In image processing and photography, a color histogram is a representation of the distribution of colors in an image. For digital images, a color histogram represents the number of pixels that have colors in each of a fixed list of color ranges that span the image's color space (the set of all possible colors). A color histogram can be built for any kind of color space, although the term is more often used for three-dimensional spaces such as RGB or HSV. For monochromatic images, the term intensity histogram may be used instead. For multi-spectral images, where each pixel is represented by an arbitrary number of measurements (for example, beyond the three measurements in RGB), a color histogram is N-dimensional, with N being the number of measurements taken. Each measurement has its own wavelength range of the light spectrum, some of which may be outside the visible spectrum. If the set of possible color values is sufficiently small, each of those colors may be placed on a range by itself; then the histogram is merely the count of pixels that have each possible color. Most often, the space is divided into an appropriate number of ranges, often arranged as a regular grid, each containing many similar color values. A color histogram may also be represented and displayed as a smooth function defined over the color space that approximates the pixel counts. Like other kinds of histograms, a color histogram is a statistic that can be viewed as an approximation of an underlying continuous distribution of color values. == Overview == Color histograms are flexible constructs that can be built from images in various color spaces, whether RGB, rg chromaticity or any other color space of any dimension. A histogram of an image is produced first by discretization of the colors in the image into a number of bins, and counting the number of image pixels in each bin. For example, a red–blue chromaticity histogram can be formed by first normalizing color pixel values by dividing RGB values by R+G+B, then quantizing the normalized R and B coordinates into N bins each. A two-dimensional histogram of red–blue chromaticity divided into four bins (N=4) may yield a histogram similar to this table: A histogram can be N-dimensional. Although harder to display, a three-dimensional color histogram for the above example could be thought of as four separate red–blue histograms, where each of the four histograms contains the red–blue values for a bin of green (0–63, 64–127, 128–191, and 192–255). The histogram provides a compact summarization of the distribution of data in an image. A color histogram of an image is relatively invariant with translation and rotation about the viewing axis, and varies only slowly with the angle of view. By comparing histogram signatures of two images and matching the color content of one image with the other, a color histogram is particularly well suited for the problem of recognizing an object of unknown position and rotation within a scene. Importantly, translation of an RGB image into the illumination invariant rg-chromaticity space allows the histogram to operate well in varying light levels. 1. What is a histogram? A histogram is a graphical representation of the number of pixels in an image. In a more simple way to explain, a histogram is a bar graph, whose X-axis represents the tonal scale (black at the left and white at the right), and Y-axis represents the number of pixels in an image in a certain area of the tonal scale. For example, the graph of a luminance histogram shows the number of pixels for each brightness level (from black to white), and when there are more pixels, the peak at the certain luminance level is higher. 2. What is a color histogram? A color histogram of an image represents the distribution of the composition of colors in the image. It shows different types of colors appeared and the number of pixels in each type of the colors appeared. The relation between a color histogram and a luminance histogram is that a color histogram can be also expressed as “three luminance histograms”, each of which shows the brightness distribution of each individual red/green/blue color channel. == Characteristics of a color histogram == A color histogram focuses only on the proportion of the number of different types of colors, regardless of the spatial location of the colors. The values of a color histogram are from statistics. They show the statistical distribution of colors and the essential tone of an image. In general, as the color distributions of the foreground and background in an image are different, there might be a bimodal distribution in the histogram. For the luminance histogram alone, there is no perfect histogram and in general, the histogram can tell whether it is over-exposure or not, but there are times when you might think the image is over exposed by viewing the histogram; however, in reality it is not. == Principles of the formation of a color histogram == The formation of a color histogram is rather simple. From the definition above, we can simply count the number of pixels for each 256 scales in each of the 3 RGB channel, and plot them on 3 individual bar graphs. In general, a color histogram is based on a certain color space, such as RGB or HSV. When we compute the pixels of different colors in an image, if the color space is large, then we can first divide the color space into certain numbers of small intervals. Each of the intervals is called a bin. This process is called color quantization. Then, by counting the number of pixels in each of the bins, we get a color histogram of the image. The concrete steps of the principles can be viewed in Example 1. == Examples == === Example 1 === Given the following image of a cat (an original version and a version that has been reduced to 256 colors for easy histogram purposes), the following data represents a color histogram in the RGB color space, using four bins. Bin 0 corresponds to intensities 0–63 Bin 1 is 64–127 Bin 2 is 128–191 and Bin 3 is 192–255. === Example 2 === Application in camera: Nowadays, some cameras have the ability to show the 3 color histograms when we take photos. We can examine clips (spikes on either the black or white side of the scale) in each of the 3 RGB color histograms. If we find one or more clipping on a channel of the 3 RGB channels, then this would result in a loss of detail for that color. To illustrate this, consider this example: We know that each of the three R, G, B channels has a range of values from 0 to 255 (8 bit). So consider a photo that has a luminance range of 0–255. Assume the photo we take is made of 4 blocks that are adjacent to each other and we set the luminance scale for each of the 4 blocks of original photo to be 10, 100, 205, 245. Thus, the image looks like the topmost figure on the right. Then, we overexpose the photo a little, say, the luminance scale of each block is increased by 10. Thus, the luminance scale for each of the 4 blocks of new photo is 20, 110, 215, 255. Then, the image looks like the second figure on the right. There is not much difference between both figures, all we can see is that the whole image becomes brighter (the contrast for each of the blocks remain the same). Now, we overexpose the original photo again, this time the luminance scale of each block is increased by 50. Thus, the luminance scale for each of the 4 blocks of the new photo is 60, 150, 255, 255. The new image now looks like the third figure on the right. Note that the scale for the last block is 255 instead of 295, for 255 is the top scale and thus the last block has clipped. When this happens, we lose the contrast of the last 2 blocks, and thus we cannot recover the image no matter how we adjust it. To conclude, when taking photos with a camera that displays histograms, always keep the brightest tone in the image below the largest scale 255 on the histogram in order to avoid losing details. == Drawbacks and other approaches == The main drawback of histograms for classification is that the representation is dependent on the color of the object being studied, ignoring its shape and texture. Color histograms can potentially be identical for two images with different object content which happens to share color information. Conversely, without spatial or shape information, similar objects of different color may be indistinguishable based solely on color histogram comparisons. There is no way to distinguish a red and white cup from a red and white plate. Put it another way: histogram-based algorithms have no concept of a generic 'cup', and a model of a red and white cup is no use when given an otherwise identical blue and white cup. Another problem is that color histograms have high sensitivity to noisy interference such as lighting intensity changes and quantization errors. High dimensionality (bins) color histograms are also another issue. Some color histogram feature spaces often occupy more than one hundred di
Find It, Fix It
Find It, Fix It is a mobile app developed by the city of Seattle to report non-emergency issues. == History == The City of Seattle launched Find It, Fix It in 2013 for Android and iOS phones to let citizens report potholes, graffiti, and other problems they observe to the city. The app did not support Windows Phone, making it inaccessible to Microsoft employees in the city who used the company's then-supported mobile operating system. In 2015, Mayor Ed Murray led a Find It, Fix It walk with about 100 other people, including police officers, in the University District. Participants were encouraged to use the app to report problems they observed in the neighborhood. Later Find It, Fix It walks have taken place in neighborhoods including Crown Hill, First Hill, Belltown, Wallingford, and Highland Park. In 2020, Find It, Fix It added support for reporting issues with the dockless bicycle sharing systems in the city. Citing the success of Seattle’s app, the nearby city of Kent, Washington, announced that it would create a similar customer service app. == Usage == Users of Find It, Fix It can submit reports about graffiti, potholes, parking violations, broken street signs, and other issues. The app is designed to use a smartphone’s camera and GPS features to make it easier for users to file reports. The Atlantic reported in 2018 that Find It, Fix It was being used by neighborhood groups to report homeless encampments with the intention of having authorities remove them, citing examples of campaigns in Ravenna and Ballard. The executive director of Ballard Alliance, a local chamber of commerce for businesses in the neighborhood, used a private Facebook group to encourage business owners to use the app to report homeless encampments. In response to a poster campaign in the summer of 2019 with the slogan “See a tent? Report a tent”, a representative for the mayor’s office and two Seattle City Council members said that it was inappropriate to encourage use of Find It, Fix It to displace homeless people. As a backlash to these campaigns, people living far from Seattle filed hoax complaints using the app, such as by using photos of tents on display at REI stores. According to the Seattle Times, between January 1, 2020, and November 15, 2021, the city had received over 230,000 service requests, of which 77% were submitted via Find It, Fix It. The largest category of these, numbering over 55,000, concerned illegal dumping. Of complaints categorized as "parking", 3,000 had comments explicitly mentioning issues around homelessness. The ZIP code 98134, covering an industrial area south of Pioneer Square and north of Georgetown, had 5,559 service requests per 1,000 residents, by far the highest in the city.
Best AI Paraphrasing Tools in 2026
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Robert Wilensky
Robert Wilensky (26 March 1951 – 15 March 2013) was an American computer scientist and professor at the UC Berkeley School of Information, with his main focus of research in artificial intelligence. == Academic career == In 1971, Wilensky received his bachelor's degree in mathematics from Yale University, and in 1978, a Ph.D. in computer science from the same institution. After finishing his thesis, "Understanding Goal-Based Stories", Wilensky joined the faculty from the EECS Department of UC Berkeley. In 1986, he worked as the doctoral advisor of Peter Norvig, who then later published the standard textbook of the field: Artificial Intelligence: A Modern Approach. From 1993 to 1997, Wilensky was the Berkeley Computer Science Division Chair. During this time, he also served as director of the Berkeley Cognitive Science Program, director of the Berkeley Artificial Intelligence Research Project, and board member of the International Computer Science Institute. In 1997, he became a fellow of the Association for Computing Machinery "for research contributions to the areas of natural language processing and digital libraries as well as outstanding leadership in Computer Science." Furthermore, he also was a Fellow of the Association for the Advancement of Artificial Intelligence. He retired from faculty in 2007 and died on Friday, March 15, 2013, of a bacterial infection at the Alta Bates Summit Medical Center. Wilensky was married to Ann Danforth and he is survived by her and their two children, Avi and Eli Wilensky == Research == Throughout his career, Wilensky authored and co-authored over 60 scholarly articles and technical reports on AI, natural language processing, and information dissemination. In addition to his numerous technical publications, Wilensky also published two books on the programming language LISP, LISPcraft and Common LISPcraft, and had almost completed another book manuscript when he suffered a cardiac arrest and stopped writing. Among his publications are: R. Wilensky, (1986-09-17). Common LISPcraft. W. W. Norton & Company. ISBN 9780393955446. T. A. Phelps and R. Wilensky, "Toward active, extensible, networked documents: Multivalent architecture and applications," in Proc. 1st ACM Intl. Conf. on Digital Libraries, E. A. Fox and G. Marchionini, Eds., New York, NY: ACM Press, 1996, pp. 100–108. J. Traupman and R. Wilensky, "Experiments in Improving Unsupervised Word Sense Disambiguation," University of California, Berkeley, Department of EECS, Computer Science Division, Tech. Rep. 03–1227, Feb. 2003. R. Wilensky, Planning and Understanding: A Computational Approach to Human Reasoning, Advanced Book Program, Reading, MA: Addison-Wesley Publishing Co., 1983. R. Wilensky, "Understanding Goal-Based Stories," Yale University, Sep. 1978. B. Kahn and R. Wilensky, "A Framework for Distributed Digital Object Services", May 1995.
Simon Godsill
Simon John Godsill (born 2 December 1965) is professor of statistical signal processing at the University of Cambridge, and a professorial fellow at Corpus Christi College. He is also a member of the Centre for Science and Policy. His main area of research is Bayesian statistics and stochastic sampling methodologies, particularly particle filtering. == Education == Godsill obtained both undergraduate and Ph.D. degrees from the Department of Engineering at Cambridge University, whilst a member of Selwyn College. He obtained a first class degree in the Electrical and Information Sciences Tripos. The title of his 1993 Ph.D. thesis was "The Restoration of Degraded Audio Signals" and his Ph.D. supervisor was Peter Rayner, whom he shared with Michael Richard Lynch. == Career == Godsill has published over 250 articles in peer reviewed journals, along with the books Digital audio restoration: a statistical model based approach and Compressed sensing & sparse filtering. == Business interests == Godsill is currently a director of CEDAR Audio Ltd, a Cambridge-based company that applies Bayesian mathematics for purposes of noise reduction in audio data. In February 2005, the company received a Sci-Tech Academy Award (a 'Technical Oscar') for its services to the movie industry, and a stream of innovations appeared over the following years with corresponding recognition including induction into the Audio Technology Hall of Fame (2008), a Cinema Audio Society Award (2009). Godsill is also a director at Input Dynamics Ltd, a Cambridge-based company that applies Bayesian techniques to touch screen technology. Godsill is involved with the research effort at BMLL Technologies, a Cambridge spin-off working in the field of machine learning application in the financial sector.
Computer appliance
A computer appliance is a computer system with a combination of hardware, software, or firmware that is specifically designed to provide a particular computing resource. Such devices became known as appliances because of the similarity in role or management to a home appliance, which are generally closed and sealed, and are not serviceable by the user or owner. The hardware and software are delivered as an integrated product and may even be pre-configured before delivery to a customer, to provide a turn-key solution for a particular application. Unlike general purpose computers, appliances are generally not designed to allow the customers to change the software and the underlying operating system, or to flexibly reconfigure the hardware. Another form of appliance is the virtual appliance, which has similar functionality to a dedicated hardware appliance, but is distributed as a software virtual machine image for a hypervisor-equipped device. == Overview == Traditionally, software applications run on top of a general-purpose operating system, which uses the hardware resources of the computer (primarily memory, disk storage, processing power, and networking bandwidth) to meet the computing needs of the user. The main issue with the traditional model is related to complexity. It is complex to integrate the operating system and applications with a hardware platform, and complex to support it afterwards. By tightly constraining the variations of the hardware and software, the appliance becomes easily deployable, and can be used without nearly as wide (or deep) IT knowledge. Additionally, when problems and errors appear, the supporting staff very rarely needs to explore them deeply to understand the matter thoroughly. The staff needs merely training on the appliance management software to be able to resolve most of problems. In all forms of the computer appliance model, customers benefit from easy operations. The appliance has exactly one combination of hardware and operating system and application software, which has been pre-installed at the factory. This prevents customers from needing to perform complex integration work, and dramatically simplifies troubleshooting. In fact, this "turnkey operation" characteristic is the driving benefit that customers seek when purchasing appliances. To be considered an appliance, the (hardware) device needs to be integrated with software, and both are supplied as a package. This distinguishes appliances from "home grown" solutions, or solutions requiring complex implementations by integrators or value-added resellers (VARs). The appliance approach helps to decouple the various systems and applications, for example in the data center. Once a resource is decoupled, in theory it can be also centralized to become shared among many systems, centrally managed and optimized, all without requiring changes to any other system. == Tradeoffs of the computer appliance approach == The major disadvantage of deploying a computer appliance is that since they are designed to supply a specific resource, they most often include a customized operating system running over specialized hardware, neither of which are likely to be compatible with the other systems previously deployed. Customers lose flexibility. One may believe that a proprietary embedded operating system, or operating system within an application, can make the appliance much more secure from common cyber attacks. However, the opposite is true. Security by obscurity is a poor security decision, and appliances are often plagued by security issues as evidenced by the proliferation of IoT devices. == Types of appliances == The variety of computer appliances reflects the wide range of computing resources they provide to applications. Some examples: Storage appliances provide large amounts of storage, often available to many machines on the network. See Network-attached storage and Storage area network. Network appliances are general purpose routers which may also provide firewall protection, Transport Layer Security (TLS), messaging, access to specialized networking protocols (like the ebXML Message Service) and bandwidth multiplexing for the multiple systems they front-end. Backup and disaster recovery appliances computer appliances that are integrated backup software and backup targets, sometimes with hypervisors to support local DR of protected servers. They are often a gateway to a full DRaaS solution. Firewall and Security appliances Dedicated network appliances that are designed to protect computer networks from unwanted traffic. IIoT and MES Gateway appliances Computer appliances that are designed to translate data bidirectionally between control systems and enterprise systems. Proprietary, embedded, firmware applications running on the appliance use point-to-point connections to translate data between field devices in their native automation protocols and MES systems through their APIs, ODBC, or RESTful interfaces. Anti-spam appliances for e-mail spam Software appliances A single application server appliance, with just enough operating system (JeOS) for it to run. Virtual machine appliances consist of a "hypervisor style" embedded operating system running on appliance hardware. The hypervisor layer is matched to the hardware of the appliance, and cannot be varied by the customer, but the customer may load other operating systems and applications onto the appliance in the form of virtual machines. == Consumer appliances == Aside from its deployment within data centers, many computer appliances are directly used by the general public. These include: Digital video recorder Residential gateway Network-attached storage (NAS) Video game console Consumer uses stress the need for an appliance to have easy installation, configuration, and operation, with little or no technical knowledge being necessary. == Appliances in industrial automation == The world of industrial automation has been rich in appliances. These appliances have been hardened to withstand temperature and vibration extremes. These appliances are also highly configurable, enabling customization to meet a wide variety of applications. The key benefits of an appliance in automation are: Reduced downtime - a failed appliance is typically replaced with a COTS replacement and its task is quickly and easily reloaded from a backup. Highly scalable - appliances are typically targeted solutions for an area of a plant or process. As the requirements change, scalability is achieved through the installation of another appliance. Automation concepts are easily replicated throughout the enterprise by standardizing on appliances to perform the needed tasks, as opposed to the development of custom automation schemes for each task. Low TCO (total cost of ownership) - appliances are developed, tested and supported by automation product vendors and undergo a much broader level of quality testing than custom designed automation solutions. The use of appliances in automation reduce the level of testing needed in each individual application. Reduced design time - appliances perform specific functions and although they are highly configurable, they are typically self documenting. This enables appliance based solutions to be transferred from engineer to engineer with minimal need for training and documentation. Types of automation appliances: PLC (programmable logic controller) - Programmable logic controllers are appliances that are typically used for discrete control and offer a wide range of Input and Output options. They are configured through standardized programming languages such as IEC-1131. PID (proportional–integral–derivative controller) - PID controllers are appliances that monitor a process variable and, based on an error term, effect change on a control output (manipulated variable) to drive the process variable to a setpoint. PAC (programmable automation controller) - Programmable automation controllers are appliances that embody properties of both PLCs and PID controllers enabling the integration of both analog and discrete control. Universal gateway - A universal gateway appliance has the ability to communicate with a variety of devices through their respective communication protocols, and will affect data transactions between them. This in increasingly important as manufacturing strives to improve agility, quality, production rates, production costs and reduce downtime through enhanced M2M (machine to machine) communications. EATMs (Enterprise Appliance Transaction Modules) - Enterprise appliance transaction modules are appliances that affect data transactions from plant floor automation systems to enterprise business systems. They communicate to plant floor equipment through various vendor automation protocols, and communicate to business systems through database communication protocols such as JMS (Java Message Service) and SQL (Structured Query Language). == Internal structure == There are several
Apertium
Apertium is a free/open-source rule-based machine translation platform. It is free software and released under the terms of the GNU General Public License. == Overview == Apertium is a transfer-based machine translation system, which uses finite state transducers for all of its lexical transformations, and Constraint Grammar taggers as well as hidden Markov models or Perceptrons for part-of-speech tagging / word category disambiguation. A structural transfer component is responsible for word movement and agreement; most Apertium language pairs up until now have used "chunking" or shallow transfer rules, though newer pairs use (possibly recursive) rules defined in a Context-free grammar. Many existing machine translation systems available at present are commercial or use proprietary technologies, which makes them very hard to adapt to new usages. Apertium code and data is free software and uses a language-independent specification, to allow for the ease of contributing to Apertium, more efficient development, and enhancing the project's overall growth. At present (December 2020), Apertium has released 51 stable language pairs, delivering fast translation with reasonably intelligible results (errors are easily corrected). Being an open-source project, Apertium provides tools for potential developers to build their own language pair and contribute to the project. == History == Apertium originated as one of the machine translation engines in the project OpenTrad, which was funded by the Spanish government, and developed by the Transducens research group at the Universitat d'Alacant. It was originally designed to translate between closely related languages, although it has recently been expanded to treat more divergent language pairs. To create a new machine translation system, one just has to develop linguistic data (dictionaries, rules) in well-specified XML formats. Language data developed for it (in collaboration with the Universidade de Vigo, the Universitat Politècnica de Catalunya and the Universitat Pompeu Fabra) currently support (in stable version) the Arabic, Aragonese, Asturian, Basque, Belarusian, Breton, Bulgarian, Catalan, Crimean Tatar, Danish, English, Esperanto, French, Galician, Hindi, Icelandic, Indonesian, Italian, Kazakh, Macedonian, Malaysian, Maltese, Northern Sami, Norwegian (Bokmål and Nynorsk), Occitan, Polish, Portuguese, Romanian, Russian, Sardinian, Serbo-Croatian, Silesian, Slovene, Spanish, Swedish, Tatar, Ukrainian, Urdu, and Welsh languages. A full list is available below. Several companies are also involved in the development of Apertium, including Prompsit Language Engineering, Imaxin Software and Eleka Ingeniaritza Linguistikoa. The project has taken part in the 2009, 2010, 2011, 2012, 2013 and 2014 editions of Google Summer of Code and the 2010, 2011, 2012, 2013, 2014, 2015, 2016 and 2017 editions of Google Code-In. == Translation methodology == This is an overall, step-by-step view how Apertium works. The diagram displays the steps that Apertium takes to translate a source-language text (the text we want to translate) into a target-language text (the translated text). Source language text is passed into Apertium for translation. The deformatter removes formatting markup (HTML, RTF, etc.) that should be kept in place but not translated. The morphological analyser segments the text (expanding elisions, marking set phrases, etc.), and looks up segments in the language dictionaries, returning dictionary forms and tags for all matches. In pairs that involve agglutinative morphology, including a number of Turkic languages, a Helsinki Finite State Transducer (HFST) is used. Otherwise, an Apertium-specific finite state transducer system called lttoolbox, is used. The morphological disambiguator (the morphological analyser and the morphological disambiguator together form the part of speech tagger) resolves ambiguous segments (i.e., when there is more than one match) by choosing one match. Apertium uses Constraint Grammar rules (with the vislcg3 parser) for most of its language pairs. Retokenisation uses a finite state transducer to match sequences of lexical units and may reorder or translate tags (often used for translating idiomatic expressions into something that more approaches the target language grammar) Lexical transfer looks up disambiguated source-language basewords to find their target-language equivalents (i.e., mapping source language to target language). For lexical transfer, Apertium uses an XML-based dictionary format called bidix. Lexical selection chooses between alternative translations when the source text word has alternative meanings. Apertium uses a specific XML-based technology, apertium-lex-tools, to perform lexical selection. Structural transfer (i.e., it is an XML format that allows writing complex structural transfer rules) can consist of one-step chunking transfer, three-step chunking transfer or a CFG-based transfer module. The chunking modules flag grammatical differences between the source language and target language (e.g. gender or number agreement) by creating a sequence of chunks containing markers for this. They then reorder or modify chunks in order to produce a grammatical translation in the target-language. The newer CFG-based module matches input sequences into possible parse trees, selecting the best-ranking one and applying transformation rules on the tree. The morphological generator uses the tags to deliver the correct target language surface form. The morphological generator is a morphological transducer, just like the morphological analyser. A morphological transducer both analyses and generates forms. The post-generator makes any necessary orthographic changes due to the contact of words (e.g. elisions). The reformatter replaces formatting markup (HTML, RTF, etc.) that was removed by the deformatter in the first step. Apertium delivers the target-language translation. == Supported languages == As of June 2026, the following 108 pairs and 51 languages and languages varieties are supported by Apertium.