AI Coding Tools

Explore the best AI Coding Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

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  IWork

  iWork is an office suite of applications created by Apple for its macOS, iPadOS, and iOS operating systems, and also available cross-platform through the iCloud website. iWork includes the presentation application Keynote, the word-processing and desktop-publishing application Pages, and the spreadsheet application Numbers. Apple's design goals in creating iWork have been to allow Mac users to easily create attractive documents and spreadsheets, making use of macOS's extensive font library, integrated spelling checker, sophisticated graphics APIs and its AppleScript automation framework. The equivalent Microsoft Office applications to Pages, Numbers, and Keynote are Word, Excel, and PowerPoint, respectively. Although Microsoft Office applications cannot open iWork documents, iWork applications can open Office documents for editing, and export documents from iWork's native formats (.pages, .numbers, .key) to Microsoft Office formats (.docx, .xlsx, .pptx, etc.) as well as to PDF files. The oldest application in iWork is Keynote, first released as a standalone application in 2003 for use by Steve Jobs in his presentations. Steve Jobs announced Keynote saying "It's for when your presentation really matters". Pages was released with the first iWork bundle in 2004; Numbers was added in 2007 with the release of iWork '08. The next release, iWork '09, also included beta access to iWork.com, an online service that allowed users to upload and share documents on the web, now integrated into Apple's iCloud service. A version of iWork for iOS was released in 2010 with the first iPad, and the apps have been regularly updated since, including the addition of iPhone support. In 2013, Apple launched iWork web apps in iCloud; even years later, however, their functionality is somewhat limited compared to equivalents on the desktop. iWork was initially sold as a suite for $79, then later at $19.99 per app on OS X and $9.99 per app on iOS. Apple announced in October 2013 that all iOS and OS X devices purchased onwards, whether new or refurbished, would be eligible for a free download of all three iWork apps: after device setup, the user can "claim" the apps on the App Store, after which they are permanently linked to the user’s Apple ID. iWork for iCloud, which also incorporates a document hosting service, is free to all iCloud users. iWork was released for free on macOS and iOS (including older or resold devices) in April 2017. In September 2016, Apple announced that the real-time collaboration feature would be available for all iWork apps. == History == The first version of iWork, iWork '05, was announced on January 11, 2005 at the Macworld Conference & Expo and made available on January 22 in the United States and on January 29 worldwide. iWork '05 comprised two applications: Keynote 2, a presentation creation program, and Pages, a word processor. iWork '05 was sold for US$79. A 30-day trial was also made available for download on Apple's website. Originally IGG Software held the rights to the name iWork. While iWork was billed by Apple as "a successor to AppleWorks", it does not replicate AppleWorks's database and drawing tools. However, iWork integrates with existing applications from Apple's iLife suite through the Media Browser, which allows users to drag and drop music from iTunes, movies from iMovie, and photos from iPhoto and Aperture directly into iWork documents. iWork '06 was released on January 10, 2006 and contained updated versions of both Keynote and Pages. Both programs were released as universal binaries for the first time, allowing them to run natively on both PowerPC processors and the Intel processors used in the new iMac desktop computers and MacBook Pro notebooks which had been announced on the same day as the new iWork suite. The next version of the suite, iWork '08, was announced and released on August 7, 2007 at a special media event at Apple's campus in Cupertino, California. iWork '08, like previous updates, contained updated versions of Keynote and Pages. A new spreadsheet application, Numbers, was also introduced. Numbers differed from other spreadsheet applications, including Microsoft Excel, in that it allowed users to create documents containing multiple spreadsheets on a flexible canvas using a number of built-in templates. iWork '09, was announced on January 6, 2009 and released the same day. It contains updated versions of all three applications in the suite. iWork '09 also included access to a beta version of the iWork.com service, which allowed users to share documents online until that service was decommissioned at the end of July 2012. Users of iWork '09 could upload a document directly from Pages, Keynote, or Numbers and invite others to view it online. Viewers could write notes and comments in the document, and download a copy in iWork, Microsoft Office, or PDF formats. iWork '09 was also released with the Mac App Store on January 6, 2011 at $19.99 per application, and received regular updates after this point, including links to iCloud and a high-DPI version designed to match Apple's MacBook Pro with Retina Display. On January 27, 2010, Apple announced iWork for iPad, to be available as three separate $9.99 applications from the App Store. This version has also received regular updates including a version for pocket iPhone and iPod Touch devices, and an update to take advantage of Retina Display devices and the larger screens of recent iPhones. On October 22, 2013, Apple announced an overhaul of the iWork software for both the Mac and iOS. Both suites were made available via the respective App Stores. The update is free for current iWork owners and was also made available free of charge for anyone purchasing an OS X or iOS device after October 1, 2013. Any user activating the newly free iWork apps on a qualifying device can download the same apps on another iOS or OS X device logged into the same App Store account. The new OS X versions have been criticized for losing features such as multiple selection, linked text boxes, bookmarks, 2-up page views, mail merge, searchable comments, ability to read/export RTF files, default zoom and page count, integration with AppleScript. Apple has provided a road-map for feature re-introduction, stating that it hopes to reintroduce some missing features within the next six months. As of April 1, 2014 a few features—e.g., the ability to set the default zoom—had been reintroduced, though scores had not. Due to using a completely new file format that can work across macOS, Windows, and in most web browsers by using the online iCloud web apps, versions of iWork beginning with iWork 13 and later do not open or allow editing of documents created in versions prior to iWork '09, with users who attempt to open older iWork files being given a pop-up in the new iWork 13 app versions telling them to use the previous iWork '09 (which users may or may not have on their machine) in order to open and edit such files. Accordingly, the current version for OS X (which was initially only compatible with OS X Mavericks 10.9 onwards) moves any previously installed iWork '09 apps to an iWork '09 folder on the users machine (in /Applications/iWork '09/), as a work-around to allow users continued use of the earlier suite in order to open and edit older iWork documents locally on their machine. In October 2015, Apple released an update to mitigate this issue, allowing users to open documents saved in iWork '06 and iWork '08 formats in the latest version of Pages. In 2016, Apple announced that the real-time collaboration feature would be available for all iWork apps, instead of being constrained to using iWork for iCloud. The feature is comparable to Google Docs. == Versions == === Major releases === === Updates === iWork '09 received several updates: iWork 9.0.3 DVD (for Mac OS X 10.5.6 "Leopard" or newer; released August 26, 2010) iWork 9.0.4 (for Mac OS X 10.5.6 "Leopard" or newer; released August 26, 2010) iWork 9.1 (for Mac OS X 10.6.6 "Snow Leopard" or newer; released July 20, 2011) iWork 9.3 (for Mac OS X 10.7.4 "Lion" or newer; released December 4, 2012) The Mac App Store version of iWork was updated on October 15, 2015 for 10.10 "Yosemite" or newer. It is the final release to support 10.10 "Yosemite" and 10.11 "El Capitan". Keynote 6.6, Pages 5.6 and Numbers 3.6 are included. iWork received a major update again on March 28, 2019 with Keynote 9.0, Pages 8.0 and Numbers 6.0. == Components == === Common components === Products in the iWork suite share a number of components, largely as a result of sharing underlying code from the Cocoa and similar shared application programming interfaces (APIs). Among these are the well known universal multilingual spell checker, which can also be found in products like Safari and Mail. Grammar checking, find and replace, style and color pickers are similar examples of design features found throughout the Apple application space. Moreover, the applications

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  Adobe PhotoDeluxe

  PhotoDeluxe was a consumer-oriented image editing software line published by Adobe Systems from 1996 until July 8, 2002. At that time it was replaced by Adobe's newly launched consumer-oriented image editing software Photoshop Elements. Adobe no longer provides technical support for the PhotoDeluxe software line. PhotoDeluxe had a range of image processing capabilities for the home photographer and image handler. These included removing red-eye, cropping, and adjusting brightness, contrast, and sharpness. It also included software to extract pictures from an image scanner. Among the functionality included was the ability to dynamically resize photos and export them in a wide range of formats. It also had a range of printing options including printing multiple copies of an image on the same page. It was often bundled free with Epson scanners or as free software with new computers. == Features == Despite the critical concerns regarding the quality of the setup, Photo Deluxe supports layering, blurs, sharpening, cloning, gradient fills, color and background switches, color variations, resizing options, and many other features. Another drawback of PhotoDeluxe was that it was designed for Mac computers, so working on Windows PC was a problem for those who were unable to customize their preferences. == Versions == === Adobe PhotoDeluxe 1.0 === The first version was released in 1996 for Windows and Macintosh computers. In one year, it sold over one million copies. === Adobe PhotoDeluxe 2.0 === The new version was released in 1997 and had added features such as a Clone Tool, red-eye removal, and sample templates for making posters, cards, and calendars. It also had new special effect features. === Adobe PhotoDeluxe 3.0 === The 3rd version was released in 1998. The new features included customizable clipart settings, the ability to import photos on the web, enhanced repair activities following Guided Activities, and Adobe Connectables to add new activities. === Adobe PhotoDeluxe Home Edition (4.0) === Version 4.0 was created by the makers of Photoshop. It had advanced abilities such as tools to add animation, voice, and music to a picture. It also had features to restore photos to their original position. == History == Adobe PhotoDeluxe 1.0 was released in 1996 for Macintosh computers, initially retailing for an MSRP of $49. The software did quite well, reportedly selling over a million copies by February of the next year, primarily due to bundles with companies like Apple and Hewlett-Packard. PhotoDeluxe was primarily advertised to consumers as a way to do basic photo manipulation, such as cropping and rotating images, or creating simple cards and calendars. PhotoDeluxe 2.0 was released in 1997, and was the last version of PhotoDeluxe that Adobe made that worked on Macs. PhotoDeluxe 2.0 became the "number one selling consumer photo-editing software product in the world." PhotoDeluxe 3.0 was released in 1998, where it was rebranded as "3.0 Home Edition", as Adobe released PhotoDeluxe Business Edition later that year for a higher price. PhotoDeluxe Home Edition, unofficially called PhotoDeluxe 4.0, was released in 1999 and was the last version of PhotoDeluxe to be released. Adobe officially cancelled PhotoDeluxe on July 8, 2002, citing the presence of Photoshop and Photoshop Elements, with support being officially cancelled in mid-2003. No version of PhotoDeluxe is compatible with Windows 10, rendering the program obsolete. == Pricing == All home versions of PhotoDeluxe retailed for an MSRP of $49. PhotoDeluxe 2.0 and onwards allowed users to upgrade from a previous version of PhotoDeluxe or a competing piece of graphics software for $39. Additionally PhotoDeluxe Business Edition allowed a similar deal, allowing users to upgrade from other versions of PhotoDeluxe or a competing software for $59, instead of its normal price of $99. Adobe also offered a bundle allowing users of 1.0 or 2.0 to get 3.0 and Business Edition for $79.

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  Line detection

  In image processing, line detection is an algorithm that takes a collection of n edge points and finds all the lines on which these edge points lie. The most popular line detectors are the Hough transform and convolution-based techniques. == Hough transform == The Hough transform can be used to detect lines and the output is a parametric description of the lines in an image, for example ρ = r cos(θ) + c sin(θ). If there is a line in a row and column based image space, it can be defined ρ, the distance from the origin to the line along a perpendicular to the line, and θ, the angle of the perpendicular projection from the origin to the line measured in degrees clockwise from the positive row axis. Therefore, a line in the image corresponds to a point in the Hough space. The Hough space for lines has therefore these two dimensions θ and ρ, and a line is represented by a single point corresponding to a unique set of these parameters. The Hough transform can then be implemented by choosing a set of values of ρ and θ to use. For each pixel (r, c) in the image, compute r cos(θ) + c sin(θ) for each values of θ, and place the result in the appropriate position in the (ρ, θ) array. At the end, the values of (ρ, θ) with the highest values in the array will correspond to strongest lines in the image == Convolution-based technique == In a convolution-based technique, the line detector operator consists of a convolution masks tuned to detect the presence of lines of a particular width n and a θ orientation. Here are the four convolution masks to detect horizontal, vertical, oblique (+45 degrees), and oblique (−45 degrees) lines in an image. a) Horizontal mask(R1) (b) Vertical (R3) (C) Oblique (+45 degrees)(R2) (d) Oblique (−45 degrees)(R4) In practice, masks are run over the image and the responses are combined given by the following equation: R(x, y) = max(|R1 (x, y)|, |R2 (x, y)|, |R3 (x, y)|, |R4 (x, y)|) If R(x, y) > T, then discontinuity As can be seen below, if mask is overlay on the image (horizontal line), multiply the coincident values, and sum all these results, the output will be the (convolved image). For example, (−1)(0)+(−1)(0)+(−1)(0) + (2)(1) +(2)(1)+(2)(1) + (−1)(0)+(−1)(0)+(−1)(0) = 6 pixels on the second row, second column in the (convolved image) starting from the upper left corner of the horizontal lines. page 82 == Example == These masks above are tuned for light lines against a dark background, and would give a big negative response to dark lines against a light background. == Code example == The code was used to detect only the vertical lines in an image using Matlab and the result is below. The original image is the one on the top and the result is below it. As can be seen on the picture on the right, only the vertical lines were detected

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  Chasys Photo

  Chasys Photo (previously called Chasys Draw Artist, then Chasys Draw IES) is a suite of applications including a layer-based raster graphics editor with adjustment layers, linked layers, timeline and frame-based animation, icon editing, image stacking and comprehensive plug-in support (Chasys Draw IES Artist), a fast multi-threaded image file converter (Chasys Draw IES Converter) and a fast image viewer (Chasys Draw IES Viewer), with RAW image support in all components. It supports the native file formats of several competitors including Adobe Photoshop, Affinity Photo, Corel Photo-Paint, GIMP, Krita, Paint.NET and PaintShop Pro, and the whole suite is designed to make effective use of multi-core processors, touch-screens and pen-input devices. The software is developed by John Paul Chacha in Nairobi, Kenya. Chasys Draw IES is currently released as freeware, and is available for computers running Microsoft Windows operating systems. It is available in three distributions: the standard distro, a portable version and a Microsoft Store version. The suite is coded in a blend of C, C++ and assembly language. It runs on x86 processors and supports the MMX, SSE, SSE2, S-SSE3, and SSE4.1 instruction sets. == History == Chasys Draw is a project that was started in November 2001 by John Paul Chacha, mostly as a hobby than anything else. The original Chasys Draw was a rather simple bitmap editor done in Visual Basic, a lot like MS Paint save for its ability to do gradients. This application underwent many changes, eventually leading up to Chasys Draw 5. This was the first version to have its own native format, referred to simply as CD5. Major updates to the graphics code in May 2002 resulted in Chasys Draw DTFx (Direct Tool eFfects). The new graphics code being referred to here was actually a miniature bitmap abstraction engine that allowed for fast per-pixel operations and direct image buffer access (much as the DIB engine does for GDI). The engine was named JpDRAW. This version was also done in VB, but was much faster than all the previous versions. The new graphics code allowed for more tools to be implemented than was ever possible before. Later on in 2002, the developer decided to completely abandon VB as a programming platform and moved all the code to C/C++. The move to C/C++ allowed the development of a full-fledged graphics engine which was named JpDRAW2. Chasys was renamed to Chasys Draw Artist, and the CD5 image format was also updated to reflect the new features. By coincidence, the module that implemented the file format was the fifth module to be added, so the format was called Chasys Draw module 5, retaining the .cd5 file extension. First public release In April 2004, Chasys Draw Artist was released to the public via the internet for the first time (version 1.27). The release was done via betanews). In 2005, Chasys Draw underwent major user interface changes as well as internal changes. By December of that year, the project had reached version 1.63. This was the first version to introduce advanced features such as anti-aliasing. It was also the first version with full support for alpha channels. The CD5 image format was also upgraded to version 2, adding advanced compression, full alpha channels, encryption and metadata. Version 1.63 was the first version to win an IEEE (Kenya chapter) award in ICT. The "chazy-glass" interface, from which the all later versions' user interfaces borrowed, was introduced in version 1.80. Chasys Draw Artist adopted photo editing features in version 2.01. Comprehensive tutorials were added and many features were re-designed to make them easier to use. Multi-threading was introduced to accelerate some tasks, such as the improved auto-save engine. Utilities such as a converter and browser were added. Version 2.43 of Chasys Draw Artist was quietly released to the public in late 2007 without any announcements. It featured many fixes to the formal version 2.42, as well as many new features. The quiet release was due to a decision to re-build Chasys Draw Artist from scratch, while still continuing support for the old architecture. An experimental version 2.45 was released only to beta-testers for the purpose of testing new technologies that would be included in the new architecture and was officially withdrawn in May 2008. During the time when the versions 2.43~2.45 were being released, work was underway to create a new layer-based Chasys Draw, which was released as Chasys Draw IES (Image Editing Suite), with the initial version number 2.50. A new multi-layer tag-based image format was created to support layering and blending modes; this was named CD5 v3. The next version introduced animation and multi-resolution support as editing modes, and the next one brought in an unlimited undo engine, new plug-ins and several internal fixes. Further development led to the introduction of super-resolution and image stacking, support for video and video capture, Anti-aliasing, metadata save and restore, a "Pen and Path" tool, physical measurement specification, and a video sequence composer engine. The user interface was enhanced with adaptive scrolling and the auto-save engine was optimized. Some memory management was added for machines with low RAM. By version 2.60, Chasys Draw IES was capable of loading Photoshop's PSD files, as well as load and save JPEG 2000. This version also had shell integration with thumbnails and application-level support for multi-monitor display setups. Metadata was extended to support save, restore and scaling for text formatting and path data. There was also a new palette with exchangeable swatches, loadable from all kinds of palette files. A slicing tool for web and user interface design was also included. A C++ code module output for inline image generation was added, as was a constrained recolor brush. The concept of a "fully anti-aliased work-flow" was introduced in version 2.62, in which all drawing and selection tools were anti-aliased by default. Support for Photoshop plug-ins using Adobe's 8bf format was added in version 2.66, allowing users to utilize thousands of free plug-ins available online. Equivalents for the Pantone palettes (PMS 100 to 814-2x) were added, and the "Just-in-Time" memory compressor significantly reduced the editor's memory requirements. First freeware release Chasys Draw IES went freeware on 6 June 2009. With the coming of the freeware IES, two blending modes (Hue and Chroma) were added. Textures were improved to allow multiple layer-based textures. The TextArt G3 engine was enhanced with LINK metadata, and alpha shift was improved. IES 2.72 added the Luma Wand tool, fixed PNG and TIFF transparency issues, and fixed Smart-Paste transparency. IES 2.74 introduced alpha protection, and 2.75 followed with a new adjustments engine that faced out many effects implemented by the effects engine. The adjustments engine was designed to appeal to experienced image editors. IES 2.76 introduced a new transform engine and the Resizer for IES plug-in supporting multi-core and 18 scaling methods, including customizable windowed Sinc interpolation. IES 2.77 added Greyscale with Tint adjustment, separated the Lock and Click-Thru layer properties, extended the Cloning Brush with three options (this, below and composite) and also extended the Color Picker with multiple point sampling. IES 3.01 brought a new look and many breakthrough tools to the suite. It was geared toward touch and was fully compatible with Windows 7. The toolbox was reorganized, with some tools being grouped and new ones added. Some message boxes were replaced with a new popup system, and the working of the workspace was changed to use a back-blitter, which enabled the addition of new blending modes, Screen and Mask. The printing interface was modified and given accurate proofing. Alpha Function Adjustment was added and a new Anti-Quantization Engine included for all adjustments to remove the need for 16 bits per channel editing. An internal clipboard was created to cater for copying images that are too large for the Windows clipboard, and translucency full-page gradients added. Some new tutorials were added and keyboard shortcuts made configurable. IES 3.05 brought the power of custom full-page gradients to the suite, supporting .ggr, .grd and .gra gradients. New gradient styles were included, as was support for Adobe color tables (.act), palette previewing, point color editing and a highly improved TextArt engine. Digital lightroom IES 3.11 was introduced on 14 December 2009. It was done on a new development base and added a new application, raw-Input. This was a RAW image format processor based on dcraw. This application allowed the use of Chasys Draw IES in processing digital negatives, which are popular with professional photographers. Chasys Draw IES 3.24 was released with a re-designed user interface, powered by a higher performance graphics core and better memory management. A history palette w

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  Human–robot interaction

  Human–robot interaction (HRI) is the study of interactions between humans and robots. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language processing, design, psychology and philosophy. A subfield known as physical human–robot interaction (pHRI) has tended to focus on device design to enable people to safely interact with robotic systems. == Origins == Human–robot interaction has been a topic of both science fiction and academic speculation even before any robots existed. Because much of active HRI development depends on natural language processing, many aspects of HRI are continuations of human communications, a field of research which is much older than robotics. The origin of HRI as a discrete problem was stated by 20th-century author Isaac Asimov in 1941, in his novel I, Robot. Asimov coined Three Laws of Robotics, namely: A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. These three laws provide an overview of the goals engineers and researchers hold for safety in the HRI field, although the fields of robot ethics and machine ethics are more complex than these three principles. However, generally human–robot interaction prioritizes the safety of humans that interact with potentially dangerous robotics equipment. Solutions to this problem range from the philosophical approach of treating robots as ethical agents (individuals with moral agency), to the practical approach of creating safety zones. These safety zones use technologies such as lidar to detect human presence or physical barriers to protect humans by preventing any contact between machine and operator. Although initially robots in the human–robot interaction field required some human intervention to function, research has expanded this to the extent that fully autonomous systems are now far more common than in the early 2000s. Autonomous systems include from simultaneous localization and mapping systems which provide intelligent robot movement to natural-language processing and natural-language generation systems which allow for natural, human-esque interaction which meet well-defined psychological benchmarks. Anthropomorphic robots (machines which imitate human body structure) are better described by the biomimetics field, but overlap with HRI in many research applications. Examples of robots which demonstrate this trend include Willow Garage's PR2 robot, the NASA Robonaut, and Honda ASIMO. However, robots in the human–robot interaction field are not limited to human-like robots: Paro and Kismet are both robots designed to elicit emotional response from humans, and so fall into the category of human–robot interaction. Goals in HRI range from industrial manufacturing through Cobots, medical technology through rehabilitation, autism intervention, and elder care devices, entertainment, human augmentation, and human convenience. Future research therefore covers a wide range of fields, much of which focuses on assistive robotics, robot-assisted search-and-rescue, and space exploration. == The goal of friendly human–robot interactions == Robots are artificial agents with capacities of perception and action in the physical world often referred by researchers as workspace. Their use has been generalized in factories but nowadays they tend to be found in the most technologically advanced societies in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, entertainment and hospital care. These new domains of applications imply a closer interaction with the user, sharing the workspace but also goals in terms of task achievement. The subfield of physical human–robot interaction (pHRI) has largely focused on device design to enable people to safely interact with robotic systems but is increasingly developing algorithmic approaches in an attempt to support fluent and expressive interactions between humans and robotic systems. With the advance in AI, the research is focusing on one part towards the safest physical interaction but also on a socially correct interaction, dependent on cultural criteria. The goal is to build an intuitive, and easy communication with the robot through speech, gestures, and facial expressions. Kerstin Dautenhahn refers to friendly Human–robot interaction as "Robotiquette" defining it as the "social rules for robot behaviour (a 'robotiquette') that is comfortable and acceptable to humans" The robot has to adapt itself to our way of expressing desires and orders and not the contrary. But every day environments such as homes have much more complex social rules than those implied by factories or even military environments. Thus, the robot needs perceiving and understanding capacities to build dynamic models of its surroundings. It needs to categorize objects, recognize and locate humans and further recognize their emotions. The need for dynamic capacities pushes forward every sub-field of robotics. Furthermore, by understanding and perceiving social cues, robots can enable collaborative scenarios with humans. For example, with the rapid rise of personal fabrication machines such as desktop 3D printers, laser cutters, etc., entering our homes, scenarios may arise where robots can collaboratively share control, co-ordinate and achieve tasks together. Industrial robots have already been integrated into industrial assembly lines and are collaboratively working with humans. The social impact of such robots have been studied and has indicated that workers still treat robots and social entities, rely on social cues to understand and work together. On the other end of HRI research the cognitive modelling of the "relationship" between human and the robots benefits the psychologists and robotic researchers the user study are often of interests on both sides. This research endeavours part of human society. For effective human – humanoid robot interaction numerous communication skills and related features should be implemented in the design of such artificial agents/systems. == General HRI research == HRI research spans a wide range of fields, some general to the nature of HRI. === Methods for perceiving humans === Methods for perceiving humans in the environment are based on sensor information. Research on sensing components and software led by Microsoft provide useful results for extracting the human kinematics (see Kinect). An example of older technique is to use colour information for example the fact that for light skinned people the hands are lighter than the clothes worn. In any case a human modelled a priori can then be fitted to the sensor data. The robot builds or has (depending on the level of autonomy the robot has) a 3D mapping of its surroundings to which is assigned the humans locations. Most methods intend to build a 3D model through vision of the environment. The proprioception sensors permit the robot to have information over its own state. This information is relative to a reference. Theories of proxemics may be used to perceive and plan around a person's personal space. A speech recognition system is used to interpret human desires or commands. By combining the information inferred by proprioception, sensor and speech the human position and state (standing, seated). In this matter, natural-language processing is concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural-language data. For instance, neural-network architectures and learning algorithms that can be applied to various natural-language processing tasks including part-of-speech tagging, chunking, named-entity recognition, and semantic role labeling. === Methods for motion planning === Motion planning in dynamic environments is a challenge that can at the moment only be achieved for robots with 3 to 10 degrees of freedom. Humanoid robots or even 2 armed robots, which can have up to 40 degrees of freedom, are unsuited for dynamic environments with today's technology. However lower-dimensional robots can use the potential field method to compute trajectories which avoid collisions with humans. === Cognitive models and theory of mind === Humans exhibit negative social and emotional responses as well as decreased trust toward some robots that closely, but imperfectly, resemble humans; this phenomenon has been termed the "Uncanny Valley". However recent research in telepresence robots has established that mimicking human body postures and expressive gestures has made the robots likeable and engaging in a remote setting. Further, the presence o

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  Optical granulometry

  Optical granulometry is the process of measuring the different grain sizes in a granular material, based on a photograph. Technology has been created to analyze a photograph and create statistics based on what the picture portrays. This information is vital in maintaining machinery in various trades worldwide. Mining companies can use optical granulometry to analyze inactive or moving rock to quantify the size of these fragments. Forestry companies can zero in on wood chip sizes without stopping the production process, and minimize sizing errors. With more photoanalysis technologies being produced, mining companies have shown an increased interest in these types of systems because of their ability to maintain efficiency throughout the mining process. Companies are saving millions of dollars annually because of this new technology, and are cutting back on maintenance costs on equipment. In order for optical granulometry to be completely successful, an accurate photo must be taken – under sufficient lighting, and using proper technology – to obtain quantified results. If these requirements are met, an image analysis system can be implemented. == The process == Software uses four basic steps in determining the average size of material: See the Wikipedia article on Photoanalysis to see how mining, forestry and agricultural companies are using this technology to improve quality control techniques. == Smartphone-based, segmentation-free estimation of grain size distribution == Recently, a methodology has emerged by which soil grain size distribution can be inferred from optical images acquired with commodity smartphones by training convolutional neural networks to predict parameters of the distribution curve directly from the image, without explicit image segmentation . In this approach, a standardized image of a soil surface is captured under controlled conditions, preprocessed to reduce device-specific variability, and passed to a regression model that outputs the parameters of a cumulative distribution function e.g., a two-parameter Weibull curve. The resulting distribution can be used to derive geotechnical descriptors and class boundaries.

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  Kuwahara filter

  The Kuwahara filter is a non-linear smoothing filter used in image processing for adaptive noise reduction. Most filters that are used for image smoothing are linear low-pass filters that effectively reduce noise but also blur out the edges. However the Kuwahara filter is able to apply smoothing on the image while preserving the edges. It is named after Michiyoshi Kuwahara, Ph.D., who worked at Kyoto and Osaka Sangyo Universities in Japan, developing early medical imaging of dynamic heart muscle in the 1970s and 80s. == The Kuwahara operator == Suppose that I ( x , y ) {\displaystyle I(x,y)} is a grey scale image and that we take a square window of size 2 a + 1 {\displaystyle 2a+1} centered around a point ( x , y ) {\displaystyle (x,y)} in the image. This square can be divided into four smaller square regions Q i = 1 ⋯ 4 {\displaystyle Q_{i=1\cdots 4}} each of which will be Q i ( x , y ) = { [ x , x + a ] × [ y , y + a ] if i = 1 [ x − a , x ] × [ y , y + a ] if i = 2 [ x − a , x ] × [ y − a , y ] if i = 3 [ x , x + a ] × [ y − a , y ] if i = 4 {\displaystyle Q_{i}(x,y)={\begin{cases}\left[x,x+a\right]\times \left[y,y+a\right]&{\mbox{ if }}i=1\\\left[x-a,x\right]\times \left[y,y+a\right]&{\mbox{ if }}i=2\\\left[x-a,x\right]\times \left[y-a,y\right]&{\mbox{ if }}i=3\\\left[x,x+a\right]\times \left[y-a,y\right]&{\mbox{ if }}i=4\\\end{cases}}} where × {\displaystyle \times } is the cartesian product. Pixels located on the borders between two regions belong to both regions so there is a slight overlap between subregions. The arithmetic mean m i ( x , y ) {\displaystyle m_{i}(x,y)} and standard deviation σ i ( x , y ) {\displaystyle \sigma _{i}(x,y)} of the four regions centered around a pixel (x,y) are calculated and used to determine the value of the central pixel. The output of the Kuwahara filter Φ ( x , y ) {\displaystyle \Phi (x,y)} for any point ( x , y ) {\displaystyle (x,y)} is then given by Φ ( x , y ) = m i ( x , y ) {\textstyle \Phi (x,y)=m_{i}(x,y)} where i = a r g min j ⁡ σ j ( x , y ) {\displaystyle i=\operatorname {arg\min } _{j}\sigma _{j}(x,y)} . This means that the central pixel will take the mean value of the area that is most homogenous. The location of the pixel in relation to an edge plays a great role in determining which region will have the greater standard deviation. If for example the pixel is located on a dark side of an edge it will most probably take the mean value of the dark region. On the other hand, should the pixel be on the lighter side of an edge it will most probably take a light value. On the event that the pixel is located on the edge it will take the value of the more smooth, least textured region. The fact that the filter takes into account the homogeneity of the regions ensures that it will preserve the edges while using the mean creates the blurring effect. Similarly to the median filter, the Kuwahara filter uses a sliding window approach to access every pixel in the image. The size of the window is chosen in advance and may vary depending on the desired level of blur in the final image. Bigger windows typically result in the creation of more abstract images whereas small windows produce images that retain their detail. Typically windows are chosen to be square with sides that have an odd number of pixels for symmetry. However, there are variations of the Kuwahara filter that use rectangular windows. Additionally, the subregions do not need to overlap or have the same size as long as they cover all of the window. == Color images == For color images, the filter should not be performed by applying the filter to each RGB channel separately, and then recombining the three filtered color channels to form the filtered RGB image. The main problem with that is that the quadrants will have different standard deviations for each of the channels. For example, the upper left quadrant may have the lowest standard deviation in the red channel, but the lower right quadrant may have the lowest standard deviation in the green channel. This situation would result in the color of the central pixel to be determined by different regions, which might result in color artifacts or blurrier edges. To overcome this problem, for color images a slightly modified Kuwahara filter must be used. The image is first converted into another color space, the HSV color space. The modified filter then operates on only the "brightness" channel, the Value coordinate in the HSV model. The variance of the "brightness" of each quadrant is calculated to determine the quadrant from which the final filtered color should be taken from. The filter will produce an output for each channel which will correspond to the mean of that channel from the quadrant that had the lowest standard deviation in "brightness". This ensures that only one region will determine the RGB values of the central pixel. ImageMagick uses a similar approach, but using the Rec. 709 Luma as the brightness metric. === Julia Implementation === == Applications == Originally the Kuwahara filter was proposed for use in processing RI-angiocardiographic images of the cardiovascular system. The fact that any edges are preserved when smoothing makes it especially useful for feature extraction and segmentation and explains why it is used in medical imaging. The Kuwahara filter however also finds many applications in artistic imaging and fine-art photography due to its ability to remove textures and sharpen the edges of photographs. The level of abstraction helps create a desirable painting-like effect in artistic photographs especially in the case of the colored image version of the filter. These applications have known great success and have encouraged similar research in the field of image processing for the arts. Although the vast majority of applications have been in the field of image processing there have been cases that use modifications of the Kuwahara filter for machine learning tasks such as clustering. The Kuwahara filter has been implemented in CVIPtools. The Kuwahara filter is present as a shader node in Blender. == Drawbacks and restrictions == The Kuwahara filter despite its capabilities in edge preservation has certain drawbacks. At a first glance it is noticeable that the Kuwahara filter does not take into account the case where two regions have equal standard deviations. This is not often the case in real images since it is rather hard to find two regions with exactly the same standard deviation due to the noise that is always present. In cases where two regions have similar standard deviations the value of the center pixel could be decided at random by the noise in these regions. Again this would not be a problem if the regions had the same mean. However, it is not unusual for regions of very different means to have the same standard deviation. This makes the Kuwahara filter susceptible to noise. Different ways have been proposed for dealing with this issue, one of which is to set the value of the center pixel to ( m 1 + m 2 ) / 2 {\textstyle (m_{1}+m_{2})/2} in cases where the standard deviation of two regions do not differ more than a certain value D {\displaystyle D} . The Kuwahara filter is also known to create block artifacts in the images especially in regions of the image that are highly textured. These blocks disrupt the smoothness of the image and are considered to have a negative effect in the aesthetics of the image. This phenomenon occurs due to the division of the window into square regions. A way to overcome this effect is to take windows that are not rectangular(i.e. circular windows) and separate them into more non-rectangular regions. There have also been approaches where the filter adapts its window depending on the input image. == Extensions of the Kuwahara filter == The success of the Kuwahara filter has spurred an increase the development of edge-enhancing smoothing filters. Several variations have been proposed for similar use most of which attempt to deal with the drawbacks of the original Kuwahara filter. The "Generalized Kuwahara filter" proposed by P. Bakker considers several windows that contain a fixed pixel. Each window is then assigned an estimate and a confidence value. The value of the fixed pixel then takes the value of the estimate of the window with the highest confidence. This filter is not characterized by the same ambiguity in the presence of noise and manages to eliminate the block artifacts. The "Mean of Least Variance"(MLV) filter, proposed by M.A. Schulze also produces edge-enhancing smoothing results in images. Similarly to the Kuwahara filter it assumes a window of size 2 d − 1 × 2 d − 1 {\displaystyle 2d-1\times 2d-1} but instead of searching amongst four subregions of size d × d {\displaystyle d\times d} for the one with minimum variance it searches amongst all possible d × d {\displaystyle d\times d} subregions. This means the central pixel of the window will be assigned the mean of the one subregion out of a poss

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  Fyre (software)

  Fyre, formerly de Jong Explorer, is a cross-platform tool for producing artwork based on histograms of iterated chaotic functions. It implements the Peter de Jong map in a fixed function pipeline through either a GTK GUI frontend, or a command line facility for easier rendering of high-resolution, high quality images. The program was renamed from de Jong Explorer to Fyre simply because 'It wasn't taken yet' and so that in the future, it could support more functions than just the standard Peter de Jong map. Fyre features a sidebar on the left to which the user can input the required variables and on the right is displayed the result of the equation. == Extra features == Additional image manipulation tools such as Gaussian blurs and Gamma controls are included in the program. The advantage to using them directly within Fyre is that the image accuracy and quality do not decline. Fyre features animation capabilities so that a user can link together several maps and create uncompressed AVIs from them. However, the uncompressed animation files are very large and so should be compressed with a separate tool, such as mencoder. == Peter de Jong Map == For most values of a,b,c and d the point (x,y) moves chaotically. The resulting image is a map of the probability that the point lies within the area represented by each pixel. Therefore, the longer that the user lets Fyre render for, the larger the probability map becomes and the more accurate the resulting image.

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  CineAsset

  CineAsset was a complete mastering software suite by Doremi Labs that could create and playback encrypted (Pro version) and unencrypted DCI compliant packages from virtually any source. CineAsset included a separate "Editor" application for generating Digital Cinema Packages (DCPs). CineAsset Pro added the ability to generate encrypted DCPs and Key Delivery Messages (KDMs) for any encrypted content in the database. It has since been discontinued, along with CineAsset Player. == Features == == Supported formats == === Input === Source: ==== Containers ==== AVI MOV MXF MPG TS WMV M2TS MTS MP4 MKV ==== Video Codecs ==== JPEG2000 ProRes 422 DNxHD® YUV Uncompressed 8-10 bits DIVX® XVID® MPEG4 AVC / H-264 VC-1 MPEG2 ==== Image Sequences ==== BMP TIFF TGA DPX JPG J2C ==== Audio Files ==== WAV MP3 WMA MP2 === Output === Source: ==== JPEG2000 ==== 2D and 3D at up to 4K resolution Bit Rate: 50–250 Mbit/s (500 Mbit/s for frame rates above 30 fps) Speed: Faster than real-time processing when using optional render nodes ==== MPEG2 ==== I-Only or Long GOP 1080p up to 80 Mbit/s ==== H264 ==== 1080p up to 50 Mbit/s ==== VC1 ==== DCP wrapping only (no transcode)

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  Voice search

  Voice search, also called voice-enabled search, allows the user to use a voice to search the Internet, a website, or an app. In a broader definition, voice search includes open-domain keyword query on any information on the Internet, for example in Google Voice Search, Cortana, Siri and Amazon Echo. Voice search is often interactive, involving several rounds of interaction that allows a system to ask for clarification. Voice search is a type of dialog system. Voice search is not a replacement for typed search. Rather the search terms, experience and use cases can differ heavily depending on the input type. == Supported language == Language is the most essential factor for a system to understand, and provide the most accurate results of what the user searches. This covers across languages, dialects, and accents, as users want a voice assistant that both understands them and speaks to them understandably. While spoken and written languages differ, voice search should support natural spoken language instead of only transforming voice into text and doing a regular text search with the help speech recognition. For example, in typed search an eCommerce user can easily copy and paste an alphanumeric product code to search field, but when speaking the search terms can be very different, such as "show me the new Bluetooth headphones by Samsung". == How it works == The difference between text and voice search is not only the input type. The mechanism must include an automatic speech recognition (ASR) for input, but it can also include natural language understanding for natural spoken search queries such as "What's the population for the United States" It can include text-to-speech (TTS) or a regular display for output modalities. Users might sometimes be required to activate the search by using a wake word. Then, the search system will detect the language spoken by the user. It will then detect the keywords and context of the sentence. Lastly, the device will return results depending on its output. A device with a screen might display the results, while a device without a screen will speak them back to the searcher.

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  Clef (app)

  Clef was a San Francisco-based technology company, known for developing a mobile app that created a two-factor authentication for websites. It allowed users to access sites with a single login password management service which stores encrypted passwords in private accounts. It had a standard verification method that requires access to data on the mobile phone to confirm the user's identity. The application required a Wi-Fi or mobile network, and the user could log in by scanning the computer screen with their phone. == History == Clef was founded in 2013 by Mark Hudnall, B. Byrne and Jesse Pollak. It raised $1.6 million in seed funding in November 2014. Clef integrated with many websites and applications, including WordPress. On March 17, 2017, Clef announced they would no longer support the plugin after June 6, 2017; Clef was acquired by Authy, another 2FA service, which later got acquired by Twilio.

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  List of computer graphics journals

  List of computer graphics journals includes notable peer-reviewed scientific and academic journals that focus on computer graphics, visualization, and related areas such as rendering, animation, image processing, and geometric modeling. == Journals == ACM Transactions on Graphics Computers & Graphics IEEE Computer Graphics and Applications IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Graphical Models Journal of Computer Graphics Techniques Presence: Teleoperators and Virtual Environments Virtual Reality Simulation & Gaming

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  Physicalization

  Physicalization of computer hardware (the opposite of virtualization), is a way to place multiple physical machines in a rack unit. It can be a way to reduce hardware costs, since in some cases, server processors cost more per core than energy efficient laptop processors, which may make up for added cost of board level integration. While Moore's law makes increasing integration less expensive, some jobs require much I/O bandwidth, which may be less expensive to provide using many less-integrated processors. Applications and services that are I/O bound are likely to benefit from such physicalized environments. This ensures that each operating system instance is running on a processor that has its own network interface card, host bus and I/O sub-system unlike in the case of a multi-core servers where a single I/O sub-system is shared between all the cores / VMs.

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  Single particle analysis

  Single particle analysis is a group of related computerized image processing techniques used to analyze images from transmission electron microscopy (TEM). These methods were developed to improve and extend the information obtainable from TEM images of particulate samples, typically proteins or other large biological entities such as viruses. Individual images of stained or unstained particles are very noisy, making interpretation difficult. Combining several digitized images of similar particles together gives an image with stronger and more easily interpretable features. An extension of this technique uses single particle methods to build up a three-dimensional reconstruction of the particle. Using cryo-electron microscopy it has become possible to generate reconstructions with sub-nanometer, near-atomic resolution resolution first in the case of highly symmetric viruses, and now in smaller, asymmetric proteins as well. == Techniques == Single particle analysis can be done on both negatively stained and vitreous ice-embedded transmission electron cryomicroscopy (CryoTEM) samples. Single particle analysis methods are, in general, reliant on the sample being homogeneous, although techniques for dealing with conformational heterogeneity are being developed. Images (micrographs) are taken with an electron microscope using charged-coupled device (CCD) detectors coupled to a phosphorescent layer (in the past, they were instead collected on film and digitized using high-quality scanners). The image processing is carried out using specialized software programs, often run on multi-processor computer clusters. Depending on the sample or the desired results, various steps of two- or three-dimensional processing can be done. === Alignment and classification === Biological samples, and especially samples embedded in thin vitreous ice, are highly radiation sensitive, thus only low electron doses can be used to image the sample. This low dose, as well as variations in the metal stain used (if used) means images have high noise relative to the signal given by the particle being observed. By aligning several similar images to each other so they are in register and then averaging them, an image with higher signal-to-noise ratio can be obtained. As the noise is mostly randomly distributed and the underlying image features constant, by averaging the intensity of each pixel over several images only the constant features are reinforced. Typically, the optimal alignment (a translation and an in-plane rotation) to map one image onto another is calculated by cross-correlation. However, a micrograph often contains particles in multiple different orientations and/or conformations, and so to get more representative image averages, a method is required to group similar particle images together into multiple sets. This is normally carried out using one of several data analysis and image classification algorithms, such as multi-variate statistical analysis and hierarchical ascendant classification, or k-means clustering. Often data sets of tens of thousands of particle images are used, and to reach an optimal solution an iterative procedure of alignment and classification is used, whereby strong image averages produced by classification are used as reference images for a subsequent alignment of the whole data set. === Image filtering === Image filtering (band-pass filtering) is often used to reduce the influence of high and/or low spatial frequency information in the images, which can affect the results of the alignment and classification procedures. This is particularly useful in negative stain images. The algorithms make use of fast Fourier transforms (FFT), often employing Gaussian shaped soft-edged masks in reciprocal space to suppress certain frequency ranges. High-pass filters remove low spatial frequencies (such as ramp or gradient effects), leaving the higher frequencies intact. Low-pass filters remove high spatial frequency features and have a blurring effect on fine details. === Contrast transfer function === Due to the nature of image formation in the electron microscope, bright-field TEM images are obtained using significant underfocus. This, along with features inherent in the microscope's lens system, creates blurring of the collected images visible as a point spread function. The combined effects of the imaging conditions are known as the contrast transfer function (CTF), and can be approximated mathematically as a function in reciprocal space. Specialized image processing techniques such as phase flipping and amplitude correction / Wiener filtering can (at least partially) correct for the CTF, and allow high resolution reconstructions. === Three-dimensional reconstruction === Transmission electron microscopy images are projections of the object showing the distribution of density through the object, similar to medical X-rays. By making use of the projection-slice theorem a three-dimensional reconstruction of the object can be generated by combining many images (2D projections) of the object taken from a range of viewing angles. Proteins in vitreous ice ideally adopt a random distribution of orientations (or viewing angles), allowing a fairly isotropic reconstruction if a large number of particle images are used. This contrasts with electron tomography, where the viewing angles are limited due to the geometry of the sample/imaging set up, giving an anisotropic reconstruction. Filtered back projection is a commonly used method of generating 3D reconstructions in single particle analysis, although many alternative algorithms exist. Before a reconstruction can be made, the orientation of the object in each image needs to be estimated. Several methods have been developed to work out the relative Euler angles of each image. Some are based on common lines (common 1D projections and sinograms), others use iterative projection matching algorithms. The latter works by beginning with a simple, low resolution 3D starting model and compares the experimental images to projections of the model and creates a new 3D to bootstrap towards a solution. Methods are also available for making 3D reconstructions of helical samples (such as tobacco mosaic virus), taking advantage of the inherent helical symmetry. Both real space methods (treating sections of the helix as single particles) and reciprocal space methods (using diffraction patterns) can be used for these samples. === Tilt methods === The specimen stage of the microscope can be tilted (typically along a single axis), allowing the single particle technique known as random conical tilt. An area of the specimen is imaged at both zero and at high angle (~60-70 degrees) tilts, or in the case of the related method of orthogonal tilt reconstruction, +45 and −45 degrees. Pairs of particles corresponding to the same object at two different tilts (tilt pairs) are selected, and by following the parameters used in subsequent alignment and classification steps a three-dimensional reconstruction can be generated relatively easily. This is because the viewing angle (defined as three Euler angles) of each particle is known from the tilt geometry. 3D reconstructions from random conical tilt suffer from missing information resulting from a restricted range of orientations. Known as the missing cone (due to the shape in reciprocal space), this causes distortions in the 3D maps. However, the missing cone problem can often be overcome by combining several tilt reconstructions. Tilt methods are best suited to negatively stained samples, and can be used for particles that adsorb to the carbon support film in preferred orientations. The phenomenon known as charging or beam-induced movement makes collecting high-tilt images of samples in vitreous ice challenging. === Map visualization and fitting === Various software programs are available that allow viewing the 3D maps. These often enable the user to manually dock in protein coordinates (structures from X-ray crystallography, NMR, or a computational model such as one found in the AlphaFold Protein Structure Database) of subunits into the electron density. Several programs can also fit subunits computationally; as of the 2020s using these programs tend to produce better accuracy than manual docking because they can perform labor-intensive tasks such as: The scale of SPA-derived maps depends on knowing the pixel size (angstorms per pixel), which is not always accurate. Programs can automatically correct for this difference by using coordinate data or by using knowledge of chemical bonds. Many proteins are made up of several roughly rigid protein domains linked by flexible parts. Pre-existing coordinate data, whether experimental or computational, may not exactly match the inter-domain positioning of the cyro-EM map. Modern programs can automatically "chop" pre-existing coordinate data into individual domains and fit them in individually. For higher-resolution structures, it is pos

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  DexNet

  Dex-net is a robotic. It uses a Grasp Quality Convolutional Neural Network to learn how to grasp unusually shaped objects. == History == Dex-net was developed by University of California, Berkeley professor Ken Goldberg and graduate student Jeff Mahler. == Design == Dex-net includes a high-resolution 3-D sensor and two arms, each controlled by a different neural network. One arm is equipped with a conventional robot gripper and another with a suction system. The robot’s software scans an object and then asks both neural networks to decide, on the fly, whether to grab or suck a particular object. It runs on an off-the-shelf industrial machine made by Swiss robotics company ABB. The software learns by attempting to pick up objects in a virtual environment. Dex-Net can generalize from an object it has seen before to a new one. The robot can "nudge" such virtual objects to examine if it is unsure how to grasp them. The trial data set was 6.7 million point clouds, grasps and analytic grasp metrics generated from thousands of 3D models. Grasps are defined as a gripper's planar position, angle and depth relative to an RGB-D sensor. == Mean picks per hour == A metric called mean picks per hour (MPPH) is calculated by multiplying the average time per pick and the average probability of success for a specific set of objects. The new metric allows labs working on picking robots to compare their results. Humans are capable of between 400 and 600 MPPH. In a contest organized by Amazon recently, the best robots were capable of between 70 and 95. Dex-net has achieved 200 to 300.

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