Downloadable content (DLC) is additional content created for an already released video game, distributed through the Internet by the game's publisher. It can be added for no extra cost or as a form of video game monetization, enabling the publisher to gain additional revenue from a title after it has been purchased, often using a microtransaction system. DLC can range from cosmetic content, such as skins, to new in-game content, like characters, levels, modes, and larger expansions that may contain a mix of such content as a continuation of the base game. In some games, multiple DLCs (including future DLC not yet released) may be bundled as part of a "season pass"—typically at a discount rather than purchasing each DLC individually. While the Dreamcast was the first home console to support DLC (albeit in a limited form due to hardware and internet connection limitations), Microsoft's Xbox helped popularize the concept. Since the seventh generation of video game consoles, DLC has been a prevalent feature of major video game platforms with internet connectivity. == Etymology == Since the popularization of microtransactions in online distribution platforms such as Steam, the term DLC has become a synonymous for any form of paid content in video games, regardless of whether they constitute the download of new content. Furthermore, this led to the creation of the oxymoronic term "on-disc DLC" for content included on the game's original files but locked behind a paywall. == History == === Precursors to DLC === The earliest form of downloadable content were offerings of full games, such as on the Atari 2600's GameLine service, which allowed users to download games using a telephone line. A similar service, Sega Channel, allowed for the downloading of games to the Sega Genesis over a cable line. While the GameLine and Sega Channel services allowed for the distribution of entire titles, they did not provide downloadable content for existing titles. Expansion packs were sold at retail for some PC games, which featured content such as additional levels, characters, or maps for a base game. They often required an installation of the original game in order to function, but some games (such as Half-Life) had "standalone" expansions, which were essentially spin-off games that reused engine code and assets from the original game. === On consoles === The Dreamcast was the first console to feature online support as a standard; DLC was available, though limited in size due to the narrowband connection and the 200 block limit of the Visual Memory Unit memory card. These online features were still considered a breakthrough in video games. With the release of the Xbox, Microsoft was the second company to implement downloadable content. Many Xbox titles, including Splinter Cell, Halo 2, and Ninja Gaiden, offered varying amounts of extra content, available for download through the Xbox Live service. Most of this content was available free. With the advent of the GameCube, Nintendo was the third company to implement downloadable content. Many GameCube titles offered varying amounts of extra content from Game Boy Advance titles with the GameCube – Game Boy Advance link cable. All of this content was available free. The Xbox 360 (2005) included more robust support for digital distribution, including DLC downloads and purchases, via its Xbox Live Marketplace service. Microsoft believed that publishers would benefit by offering small pieces of content at a small cost ($1 to $5), rather than full expansion packs (~$20), as this would allow players to pick and chose what content they desired, providing revenue to the publishers. Microsoft also utilized a digital currency known as "Microsoft Points" for transactions, which could also be purchased through physical gift cards to avoid the banking fees associated with the small price points. The PlayStation 3 (2006) adopted the same approach with their downloadable hub, the PlayStation Store. Sony planned on having the bulk of its content be purchased separately via many separate online microtransactions for PlayStation Network titles, including Gran Turismo HD Concept and Gran Turismo 5 Prologue. The Wii (2006) featured a sparser amount of downloadable content on their Wii Shop Channel, the bulk of which is accounted for by digital distribution of emulated Nintendo titles from previous generations. Music video games, such as titles from the Guitar Hero and Rock Band franchises, took significant advantage of downloadable content as a means of offering new songs to be played in-game. Harmonix claimed that Guitar Hero II would feature "more online content than anyone has ever seen in a game to this date." Rock Band features the largest number of downloadable items of any console video game, with a steady number of new songs that were added weekly between 2007 and 2013. Acquiring all the downloadable content for Rock Band would, as of July 12, 2012, cost $5,880.10. === On personal computers === As the popularity and speed of internet connections rose, so did the popularity of using the internet for digital distribution of media. User-created game mods and maps were distributed exclusively online, as they were mainly created by people without the infrastructure capable of distributing the content through physical media. In 1997, Cavedog offered a new unit every month as free downloadable content for their real-time strategy computer game Total Annihilation. Later PC digital distribution platforms, such as Games for Windows Marketplace and Steam, would add support for DLC in a similar manner to consoles. === On handhelds === Nokia phones of the late 1990s and early 2000s shipped with side-scrolling shooter Space Impact, available on various models. With the introduction of WAP in 2000, additional downloadable content for the game, with extra levels, became available. The Nintendo Wi-Fi Connection service on the Nintendo DS could be used to obtain a form of DLC for certain games, such as Picross DS—where players could download puzzle "packs" of classic puzzles from previous Picross series games (such as Mario's Picross). as well as downloadable user generated content. Due to the Nintendo DS's use of cartridges and lack of dedicated storage, most "DLC" for DS games was limited in scope, or in some cases (such as Professor Layton and the Curious Village and Moero! Nekketsu Rhythm Damashii Osu! Tatakae! Ouendan 2), was already part of the game's data on the cartridge, and merely unlocked. Its successor, the Nintendo 3DS, natively supported the purchase of DLC for supported titles via Nintendo eShop. Starting with iPhone OS 3, downloadable content became available for the platform via applications bought from the App Store. While this ability was initially only available to developers for paid applications, Apple eventually allowed for developers to offer this in free applications as well in October 2009. == On-disc DLC == In some cases, a purchased DLC may not actually download new content to the device, but merely consists of data used to enable associated content that is already present within the game's data. DLC of this nature revealed via data mining is typically referred to as "on-disc DLC" or PULC (premium unlockable content). This practice has sometimes been considered controversial, with publishers being accused of using what is effectively a microtransaction to lock access to content that was already contained within the game as sold at retail. Data relating to future DLC may be included on-disc or downloaded during updates for technical reasons as well, either to ensure online multiplayer compatibility for existing content between players who have not yet purchased the new DLC, or as dormant support code for planned content that is still in development at the time of the release. == Monetization == Downloadable content is often offered for a price. Since Facebook games popularized the business model of microtransactions, some have criticized downloadable content as being overpriced and an incentive for developers to leave items out of the initial release, with The Elder Scrolls IV: Oblivion's horse armor DLC having faced a mixed reception upon its release for that reason. However, by 2009, the Horse Armor DLC was one of the top ten content packs that Bethesda had sold, which justified the DLC model for future games. Where a normal software disc may allow its license sold or traded, DLC is generally locked to a specific user's account and does not come with the ability to transfer that license to another user. In addition to individual content downloads, video game publishers sometimes offer a "season pass", which allows users to pre-order a selection of upcoming content over a specific time period, and ensuring the customer's ability to immediately obtain the content upon release. As users do not have the ability to fully preview the content before their purchase, there is a chance that the content of a season
Discovery system (artificial intelligence)
A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to search systematically through the space of all possible hypotheses and yield the hypothesis - or set of equally likely hypotheses - that best describes the complex patterns in data. During the era known as the second AI summer (approximately 1978–1987), various systems akin to the era's dominant expert systems were developed to tackle the problem of extracting scientific hypotheses from data, with or without interacting with a human scientist. These systems included Autoclass, Automated Mathematician, Eurisko, which aimed at general-purpose hypothesis discovery, and more specific systems such as Dalton, which uncovers molecular properties from data. The dream of building systems that discover scientific hypotheses was pushed to the background with the second AI winter and the subsequent resurgence of subsymbolic methods such as neural networks. Subsymbolic methods emphasize prediction over explanation, and yield models which works well but are difficult or impossible to explain which has earned them the name black box AI. A black-box model cannot be considered a scientific hypothesis, and this development has even led some researchers to suggest that the traditional aim of science - to uncover hypotheses and theories about the structure of reality - is obsolete. Other researchers disagree and argue that subsymbolic methods are useful in many cases, just not for generating scientific theories. == Discovery systems from the 1970s and 1980s == Autoclass was a Bayesian Classification System written in 1986 Automated Mathematician was one of the earliest successful discovery systems. It was written in 1977 and worked by generating a modifying small Lisp programs Eurisko was a Sequel to Automated Mathematician written in 1984 Dalton is a still maintained program capable of calculating various molecular properties initially launched in 1983 and available in open source since 2017 Glauber is a scientific discovery method written in the context of computational philosophy of science launched in 1983 == Modern discovery systems (2009–present) == After a couple of decades with little interest in discovery systems, the interest in using AI to uncover natural laws and scientific explanations was renewed by the work of Michael Schmidt, then a PhD student in Computational Biology at Cornell University. Schmidt and his advisor, Hod Lipson, invented Eureqa, which they described as a symbolic regression approach to "distilling free-form natural laws from experimental data". This work effectively demonstrated that symbolic regression was a promising way forward for AI-driven scientific discovery. Since 2009, symbolic regression has matured further, and today, various commercial and open source systems are actively used in scientific research. Notable examples include Eureqa, now a part of DataRobot AI Cloud Platform, AI Feynman, and QLattice.
ACL Data Collection Initiative
The ACL Data Collection Initiative (ACL/DCI) was a project established in 1989 by the Association for Computational Linguistics (ACL) to create and distribute large text and speech corpora for computational linguistics research. The initiative aimed to address the growing need for substantial text databases that could support research in areas such as natural language processing, speech recognition, and computational linguistics. By 1993, the initiative’s activities had effectively ceased, with its functions and datasets absorbed by the Linguistic Data Consortium (LDC), which was founded in 1992. == Objectives == The ACL/DCI had several key objectives: To acquire a large and diverse text corpus from various sources To transform the collected texts into a common format based on the Standard Generalized Markup Language (SGML) To make the corpus available for scientific research at low cost with minimal restrictions To provide a common database that would allow researchers to replicate or extend published results To reduce duplication of effort among researchers in obtaining and preparing text data These objectives were designed to address the growing demand for very large amounts of text arising from applications in recognition and analysis of text and speech. Its core objective was to "oversee the acquisition and preparation of a large text corpus to be made available for scientific research at cost and without royalties". == History == By the late 1980s, researchers in computational linguistics and speech recognition faced a significant problem: the lack of large-scale, accessible text corpora for developing statistical models and testing algorithms. Existing generally available text databases were too small to meet the needs of developing applications in text and speech recognition. The initiative was formed to meet this need by collecting, standardizing, and distributing large quantities of text data with minimal restrictions for scientific research. As stated by Liberman (1990), "research workers have been severely hampered by the lack of appropriate materials, and specially by the lack of a large enough body of text on which published results can be replicated or extended by others." The ACL/DCI committee was established in February 1989. The committee included members from academic and industrial research laboratories in the United States and Europe. The initiative was chaired by Mark Liberman from the University of Pennsylvania (formerly of AT&T Bell Laboratories). Other committee members included representatives from organizations such as Bellcore, IBM T.J. Watson Research Center, Cambridge University, Virginia Polytechnic Institute & State University, Northeastern University, University of Pennsylvania, SRI International, MCC, Xerox PARC, ISSCO, and University of Pisa. The project operated initially without dedicated funding, relying on volunteer efforts from committee members and their affiliated institutions. Key supporters included AT&T Bell Labs, Bellcore, IBM, Xerox, and the University of Pennsylvania, which allowed the use of their computing facilities for ACL/DCI-related work. Previously running on volunteer effort pro bono, in 1991, it obtained funding from General Electric and the National Science Foundation (IRI-9113530). == Data == As of 1990, the ACL/DCI had collected hundreds of millions of words of diverse text. The collection included: Wall Street Journal articles (25 to 50 million words); Canadian Hansard (parliamentary records) in parallel English and French versions: cleaned-up English Hansard donated by the IBM alignment models group (100 million words), and original Bilingual Hansard (from a different time period) obtained directly (200 million words). Collins English Dictionary (1979 edition), both as fulltext (3 million words) and as various "database" versions, constructed using "typographers' tape" donated by Collins, which were computer tapes containing the structured digital data used to typeset and print the 1979 edition of the dictionary; Emails from ARPANET newsletters for the ACM Special Interest Group on Information Retrieval Forum (IRLIST) and AIList Digest issues distributed over the ARPANET (AILIST) (5 million words), both collected by Edward A. Fox at VIPSU; Articles on networking (2 million words); U.S. Department of Agriculture Extension Service Fact Sheets (>1 million words); 200,000 scientific abstracts of about 1,500 words each from the Department of Energy (25 million words); Archives of the Challenger Investigation Commission, including transcripts of depositions and hearings (2.5 million words); Books from the Library of America, including works by Mark Twain, Eugene O'Neill, Ralph Waldo Emerson, Herman Melville, W.E.B. DuBois, Willa Cather, and Benjamin Franklin (130 books, 20 million words); Public domain books like the King James Bible, Tristram Shandy, The Federalist Papers; Several million words of transcribed radiologists' reports, donated by Francis Ganong at Kurzweil Applied Intelligence Inc (about 5 million words); The Child Language Data Exchange corpus of child language acquisition transcripts; U.S. Department of Justice Justice Retrieval and Inquiry System (JURIS) materials; The Swiss Civil Code in parallel German, French and Italian; Economic reports from the Union Bank of Switzerland, in parallel English, German, French and Italian; About 12K words of administrative policy manuals and 14K words of administrative memos, contributed by Geoff Pullum of U.C.S.C.; Material from various ACM journals and the ACL journal Computational Linguistics; The CSLI publications series: 50-100 reports (8K words each) and 5-10 books (80K words each). The initiative started with North American English text but expanded to include Canadian French and planned to include Japanese, Chinese, and other Asian languages. At least 5 million words from the collection were tagged under the Penn Treebank project, and those tags were distributed by DCI as well. After DCI was absorbed by the LDC, the datasets were curated under LDC. == Format == The ACL/DCI corpus was coded in a standard form based on SGML (Standard Generalized Markup Language, ISO 8879), consistent with the recommendations of the Text Encoding Initiative (TEI), of which the DCI was an affiliated project. The TEI was a joint project of the ACL, the Association for Computers and the Humanities, and the Association for Literary and Linguistic Computing, aiming to provide a common interchange format for literary and linguistic data. The initiative planned to add annotations reflecting consensually approved linguistic features like part of speech and various aspects of syntactic and semantic structure over time. == Examples == As an example of the use of ACL/DCI, consider the Wall Street Journal (WSJ) corpus for speech recognition research. The WSJ corpus was used as the basis for the DARPA Spoken Language System (SLS) community's Continuous Speech Recognition (CSR) Corpus. The WSJ corpus became a standard benchmark for evaluating speech recognition systems and has been used in numerous research papers. The WSJ CSR Corpus provided DARPA with its first general-purpose English, large vocabulary, natural language, high perplexity corpus containing speech (400 hours) and text (47 million words) during 1987–89. The text corpus was 313 MB in size. The text was preprocessed to remove ambiguity in the word sequence that a reader might choose, ensuring that the unread text used to train language models was representative of the spoken test material. The preprocessing included converting numbers into orthographics, expanding abbreviations, resolving apostrophes and quotation marks, and marking punctuation. As another example, the Yarowsky algorithm used bitext data from DCI to train a simple word-sense disambiguation model that was competitive with advanced models trained on smaller datasets. == Distribution == Materials from the ACL/DCI collection were distributed to research groups on a non-commercial basis. By 1990, about 25 research groups and individual researchers had received tapes containing various portions of the collected material. To obtain the data, researchers had to sign an agreement not to redistribute the data or make direct commercial use of it. However, commercial application of "analytical materials" derived from the text, such as statistical tables or grammar rules, was explicitly permitted. The initiative first distributed data via 12-inch reels of 9-track tape, then via CD-ROMs. Each such tape could contain 30 million words compressed via the Lempel-Ziv algorithms. The first CD-ROM distribution was in 1991, funded by Dragon Systems Inc. It contained Collins English Dictionary, WSJ, scientific abstracts provided by the U.S. Department of Energy, and the Penn Treebank.
Multi-scale approaches
The scale space representation of a signal obtained by Gaussian smoothing satisfies a number of special properties, scale-space axioms, which make it into a special form of multi-scale representation. There are, however, also other types of "multi-scale approaches" in the areas of computer vision, image processing and signal processing, in particular the notion of wavelets. The purpose of this article is to describe a few of these approaches: == Scale-space theory for one-dimensional signals == For one-dimensional signals, there exists quite a well-developed theory for continuous and discrete kernels that guarantee that new local extrema or zero-crossings cannot be created by a convolution operation. For continuous signals, it holds that all scale-space kernels can be decomposed into the following sets of primitive smoothing kernels: the Gaussian kernel : g ( x , t ) = 1 2 π t exp ( − x 2 / 2 t ) {\displaystyle g(x,t)={\frac {1}{\sqrt {2\pi t}}}\exp({-x^{2}/2t})} where t > 0 {\displaystyle t>0} , truncated exponential kernels (filters with one real pole in the s-plane): h ( x ) = exp ( − a x ) {\displaystyle h(x)=\exp({-ax})} if x ≥ 0 {\displaystyle x\geq 0} and 0 otherwise where a > 0 {\displaystyle a>0} h ( x ) = exp ( b x ) {\displaystyle h(x)=\exp({bx})} if x ≤ 0 {\displaystyle x\leq 0} and 0 otherwise where b > 0 {\displaystyle b>0} , translations, rescalings. For discrete signals, we can, up to trivial translations and rescalings, decompose any discrete scale-space kernel into the following primitive operations: the discrete Gaussian kernel T ( n , t ) = I n ( α t ) {\displaystyle T(n,t)=I_{n}(\alpha t)} where α , t > 0 {\displaystyle \alpha ,t>0} where I n {\displaystyle I_{n}} are the modified Bessel functions of integer order, generalized binomial kernels corresponding to linear smoothing of the form f o u t ( x ) = p f i n ( x ) + q f i n ( x − 1 ) {\displaystyle f_{out}(x)=pf_{in}(x)+qf_{in}(x-1)} where p , q > 0 {\displaystyle p,q>0} f o u t ( x ) = p f i n ( x ) + q f i n ( x + 1 ) {\displaystyle f_{out}(x)=pf_{in}(x)+qf_{in}(x+1)} where p , q > 0 {\displaystyle p,q>0} , first-order recursive filters corresponding to linear smoothing of the form f o u t ( x ) = f i n ( x ) + α f o u t ( x − 1 ) {\displaystyle f_{out}(x)=f_{in}(x)+\alpha f_{out}(x-1)} where α > 0 {\displaystyle \alpha >0} f o u t ( x ) = f i n ( x ) + β f o u t ( x + 1 ) {\displaystyle f_{out}(x)=f_{in}(x)+\beta f_{out}(x+1)} where β > 0 {\displaystyle \beta >0} , the one-sided Poisson kernel p ( n , t ) = e − t t n n ! {\displaystyle p(n,t)=e^{-t}{\frac {t^{n}}{n!}}} for n ≥ 0 {\displaystyle n\geq 0} where t ≥ 0 {\displaystyle t\geq 0} p ( n , t ) = e − t t − n ( − n ) ! {\displaystyle p(n,t)=e^{-t}{\frac {t^{-n}}{(-n)!}}} for n ≤ 0 {\displaystyle n\leq 0} where t ≥ 0 {\displaystyle t\geq 0} . From this classification, it is apparent that we require a continuous semi-group structure, there are only three classes of scale-space kernels with a continuous scale parameter; the Gaussian kernel which forms the scale-space of continuous signals, the discrete Gaussian kernel which forms the scale-space of discrete signals and the time-causal Poisson kernel that forms a temporal scale-space over discrete time. If we on the other hand sacrifice the continuous semi-group structure, there are more options: For discrete signals, the use of generalized binomial kernels provides a formal basis for defining the smoothing operation in a pyramid. For temporal data, the one-sided truncated exponential kernels and the first-order recursive filters provide a way to define time-causal scale-spaces that allow for efficient numerical implementation and respect causality over time without access to the future. The first-order recursive filters also provide a framework for defining recursive approximations to the Gaussian kernel that in a weaker sense preserve some of the scale-space properties.
Robot Monk Xian'er
Robot Monk Xian'er (Chinese: 贤二机器僧) is a humanoid robot based on the cartoon character Xian'er. It was developed by a team of monks, volunteers and AI experts from Beijing Longquan Monastery in Beijing, China. He can follow human instructions to make body movements, read scriptures and play Buddhist music. He can chat and respond to people's emotional and spiritual questions with Buddhist wisdom. As a chatbot, Robot Monk Xian'er is available on certain public platforms including WeChat and Facebook. Over the years, master Xuecheng, the abbot of Beijing Longquan Monastery, replied to thousands of questions on Sina Weibo. These questions and their answers become the data source of the chatbot.
Aphelion (software)
The Aphelion Imaging Software Suite is a software suite that includes three base products - Aphelion Lab, Aphelion Dev, and Aphelion SDK for addressing image processing and image analysis applications. The suite also includes a set of extension programs to implement specific vertical applications that benefit from imaging techniques. The Aphelion software products can be used to prototype and deploy applications, or can be integrated, in whole or in part, into a user's system as processing and visualization libraries whose components are available as both DLLs or .Net components. == History and evolution == The development of Aphelion started in 1995 as a joint project of a French company, ADCIS S.A., and an American company, Amerinex Applied Imaging, Inc. (AAI) Aphelion's image processing and analysis functions were made from operators available from the KBVision software developed and sold by Amerinex's predecessor, Amerinex Artificial Intelligence Inc. In the 1990s, the XLim software library was developed at the Center of Mathematical Morphology of Mines ParisTech, and both companies carried out its development tasks. The first version of Aphelion was completed and released in April 1996. Successive versions were released before the first official stable release in December 1996 at the Photonics East conference in Boston and the Solutions Vision show in Paris in January 1997, where at the latter it competed with Stemmer Imaging's CVB imaging toolbox. In 1998, version 2.3 of Aphelion for Windows 98 was released, and its user base was growing in both France and the United States. Version 3.0, totally rewritten to take advantage of Microsoft's then-recent ActiveX technology, was officially released in 2000. It also became available as a « Developer » version, for rapid prototyping of applications using its intuitive GUI and the macro recording capability, and a « Core » version, including the full library as a set of ActiveX components to be used by software developers, integrators and original equipment manufacturers (OEM). As AAI turned its focus to security, in 2001, ADCIS took the lead on developing Aphelion. AAI focused on millimeter wave scanners for concealed weapon detection at airports, and eventually merged with Millimetrics to become Millivision. In 2004, ADCIS specified version 4.0 of Aphelion. The set of image processing/analysis functions was rewritten one more time to be compatible with the .NET technology and the emergence of 64 bit architecture PCs. In addition, the GUI was redesigned to address two usage types: a semi-automatic use where the user is guided through the different steps of functions, and a fully automatic use where the expert user can quickly invoke imaging functions. Its first release was presented at the IPOT exhibition in Birmingham, UK the same year. During the Vision Show in Paris in October 2008, the new Aphelion Lab product was launched for users that are not specialists in image processing. It is easier to use, and only includes fewer image processing functions. It was then included in the Aphelion Image Processing Suite, consisting of Aphelion Dev (replacing Aphelion Developer), Aphelion Lab, Aphelion SDK (replacing Aphelion Core), and a set of extensions. Nowadays, ADCIS is still working on the suite, and updated versions with new extensions and functionalities continually become available from the websites of both companies. In 2015, support was added for very large images and scan microscope images (virtual slides compound into a very large JPEG 2000 image) for high throughput imaging, and new specific extensions were also added. In late 2015, ADCIS announced Aphelion's port for tablets and smartphones, for vertical applications. The name "Aphelion" comes from the astronomical term of the same name, meaning the point on a planet rotating around the Sun where it lies farthest from it, applying the term in a metaphorical sense. Unix was the operating system used on scientific workstations in the 1990s, such as on the workstations manufactured by market leader Sun Microsystems, which Windows suite Aphelion was quite removed from. == Description == Aphelion is a software suite to be used for image processing and image analysis. It supports 2D and 3D, monochrome, color, and multi-band images. It is developed by ADCIS, a French software house located in Saint-Contest, Calvados, Normandy. Aphelion is widely used in the scientific/industry community to solve basic and complex imaging applications. First, the imaging application is quickly developed from the Graphical User Interface, involving a set of functions that can be automatically recorded into a macro command. The macro languages available in Aphelion (i.e. BasicScript, Python, and C#) help to process batch of images, and prompt the user if needed for specific parameters that are applied to the imaging functions. All Aphelion image processing functions are written in C++, and the Aphelion user interface is written in C#. C++ functions can be called from the C# language thanks the use of dedicated wrappers. The main principle of image processing is to automatically process pixels of a digital image, then extract one or more objects of interest (i.e. cells in the field of biology, inclusions in the field of material science) and compute one or more measurements on those objects to quantify the image and generate a verdict (good image, image with defects, cancerous cells). In other words, starting from an image, pixels are processed by a set of successive functions or operators until only measurements are computed and used as the input of a 3rd party system or a classification software that will classify objects of interest that have been extracted during the imaging process. An acquisition system such as a digital camera, a video camera, an optical or electron microscope, a medical scanner, or a smartphone can be used to capture images. The set of values or pixels can be processed as a 1D image (1D signal), a 2D image (array of pixel values corresponding to a monochrome or color image), or a 3D image displayed using volume rendering (array of voxels in the 3D space) or displaying surfaces by using 3D rendering. A 2D color image is made of 3 value pixels (typically Red, Green, and Blue information or another color space), and a 3D image is made of monochrome, color (indexed color are often used), multispectral, or hyperspectral data. When dealing with videos, an additional band is added corresponding to temporal information. The Aphelion Software Suite includes three base products, and a set of optional extensions for specific applications: Aphelion Lab: Entry-level package for non-experts in image processing. It helps to quickly segment an image in a semi-automatic or manual ways, and compute a set of measurements computed on objects of interest that have been extracted during the segmentation process. A set of wizards guides the user from image acquisition to report generation. Aphelion Dev: Full imaging environment including over 450 functions to develop and deploy an application that involves image processing and analysis. It also includes a set of macro-command languages to automate any application to be invoked from the user interface. It also helps to run the imaging algorithm on more than one image that are stored on disk, available on the network, or captured by an acquisition device. Aphelion libraries for image processing and visualization are provided in Aphelion Dev as DLLs and .Net components. Aphelion SDK: A set of libraries to develop a stand-alone application with a custom interface based on the Aphelion libraries. This software development kit including display, processing and analysis functions that can be used by software developers and OEMs. It is provided as DLLs and .Net components. The stand-alone application is typically developed in C# on one computer, and then deployed on multiple PCs and systems. A set of optional extensions can be added to the « Aphelion Dev » product, depending on the application. An evaluation version of Aphelion can be run on a PC for 30 days. A permanent version of Aphelion is available based on a perpetual license. Upgrades are available through a maintenance agreement based on a yearly fee. Technical support is provided by the engineers who are developing the product. The goal of image processing is usually to extract object(s) of interest in an image, and then to classify them based on some characteristics such as shape, density, position, etc. Using Aphelion, this goal is achieved by performing the following tasks: Load an image from disk or acquire an image using an acquisition device. Enhance the image removing noise or modifying its contrast. Segment the image extracting objects of interest to be measured and analyzed. Typically, for simple applications, a threshold is performed to generate a binary image. Then, morphological operators are applied to clean the image and only keep obj
PatchMatch
PatchMatch is an algorithm used to quickly find correspondences (or matches) between small square regions (or patches) of an image. It has various applications in image editing, such as reshuffling or removing objects from images or altering their aspect ratios without cropping or noticeably stretching them. PatchMatch was first presented in a 2011 paper by researchers at Princeton University. == Algorithm == The goal of the algorithm is to find the patch correspondence by defining a nearest-neighbor field (NNF) as a function f : R 2 → R 2 {\displaystyle f:\mathbb {R} ^{2}\to \mathbb {R} ^{2}} of offsets, which is over all possible matches of patch (location of patch centers) in image A, for some distance function of two patches D {\displaystyle D} . So, for a given patch coordinate a {\displaystyle a} in image A {\displaystyle A} and its corresponding nearest neighbor b {\displaystyle b} in image B {\displaystyle B} , f ( a ) {\displaystyle f(a)} is simply b − a {\displaystyle b-a} . However, if we search for every point in image B {\displaystyle B} , the work will be too hard to complete. So the following algorithm is done in a randomized approach in order to accelerate the calculation speed. The algorithm has three main components. Initially, the nearest-neighbor field is filled with either random offsets or some prior information. Next, an iterative update process is applied to the NNF, in which good patch offsets are propagated to adjacent pixels, followed by random search in the neighborhood of the best offset found so far. Independent of these three components, the algorithm also uses a coarse-to-fine approach by building an image pyramid to obtain the better result. === Initialization === When initializing with random offsets, we use independent uniform samples across the full range of image B {\displaystyle B} . This algorithm avoids using an initial guess from the previous level of the pyramid because in this way the algorithm can avoid being trapped in local minima. === Iteration === After initialization, the algorithm attempted to perform iterative process of improving the N N F {\displaystyle NNF} . The iterations examine the offsets in scan order (from left to right, top to bottom), and each undergoes propagation followed by random search. === Propagation === We attempt to improve f ( x , y ) {\displaystyle f(x,y)} using the known offsets of f ( x − 1 , y ) {\displaystyle f(x-1,y)} and f ( x , y − 1 ) {\displaystyle f(x,y-1)} , assuming that the patch offsets are likely to be the same. That is, the algorithm will take new value for f ( x , y ) {\displaystyle f(x,y)} to be arg min ( x , y ) D ( f ( x , y ) ) , D ( f ( x − 1 , y ) ) , D ( f ( x , y − 1 ) ) {\displaystyle \arg \min \limits _{(x,y)}{D(f(x,y)),D(f(x-1,y)),D(f(x,y-1))}} . So if f ( x , y ) {\displaystyle f(x,y)} has a correct mapping and is in a coherent region R {\displaystyle R} , then all of R {\displaystyle R} below and to the right of f ( x , y ) {\displaystyle f(x,y)} will be filled with the correct mapping. Alternatively, on even iterations, the algorithm search for different direction, fill the new value to be arg min ( x , y ) { D ( f ( x , y ) ) , D ( f ( x + 1 , y ) ) , D ( f ( x , y + 1 ) ) } {\displaystyle \arg \min \limits _{(x,y)}\{D(f(x,y)),D(f(x+1,y)),D(f(x,y+1))\}} . === Random search === Let v 0 = f ( x , y ) {\displaystyle v_{0}=f(x,y)} , we attempt to improve f ( x , y ) {\displaystyle f(x,y)} by testing a sequence of candidate offsets at an exponentially decreasing distance from v 0 {\displaystyle v_{0}} u i = v 0 + w α i R i {\displaystyle u_{i}=v_{0}+w\alpha ^{i}R_{i}} where R i {\displaystyle R_{i}} is a uniform random in [ − 1 , 1 ] × [ − 1 , 1 ] {\displaystyle [-1,1]\times [-1,1]} , w {\displaystyle w} is a large window search radius which will be set to maximum picture size, and α {\displaystyle \alpha } is a fixed ratio often assigned as 1/2. This part of the algorithm allows the f ( x , y ) {\displaystyle f(x,y)} to jump out of local minimum through random process. === Halting criterion === The often used halting criterion is set the iteration times to be about 4~5. Even with low iteration, the algorithm works well.