"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
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The visual performance of Humans is much better than that of computers, probably because of superior high-level image understanding, contextual knowledge, and massively parallel processing. But human capabilities deteriorate drastically after an extended period of surveillance, also certain working environments are either inaccessible or too hazardous for human beings. So for these reasons, automatic recognition systems are developed for various applications. Driven by advances in computing capability and image processing technology, computer mimicry of human vision has recently gained ground in a number of practical applications. Image recognition refers to technologies that identify places, logos, people, objects, buildings, and several other variables in digital images.
We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.
Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. Some of its applications include systems for factory automation, face recognition, booth monitoring, and security surveillance. Image recognition is embedded in technologies that enable students with learning disabilities to receive the education they need -- in a form they can perceive. Apps powered by computer vision offer text-to-speech options, which allow students with impaired vision or dyslexia to'read' the content. By employing image recognition, Jetpac caught visual cues in the photos and analyzed them to offer live data to its users.
The creators of the Python language are mulling a new proposal, PEP 622, that would finally bring a pattern matching statement syntax to Python. The new pattern matching statements would give Python programmers more expressive ways of handling structured data, without having to resort to workarounds. Pattern matching is a common feature of many programming languages, such as switch/case in C. It allows one of a number of possible actions to be taken based on the value of a given variable or expression. While Python has lacked a native syntax for pattern matching, it has been possible to emulate it with if/elif/else chains or a dictionary lookup. Supported pattern match types include literals, names, constant values, sequences, a mapping (basically, the presence of a key-value pair in the expression), a class, a mixture of the above, or any of those plus conditional expressions.
Ever since Android first came into existence in 2008, it has become the world's biggest mobile platform in terms of popularity and number of users. Over the years, Android developers have built advances in machine learning, features like on-device speech recognition, real-time video interactiveness, and real-time enhancements when taking a photo/selfie. In addition, image recognition with machine learning can enable users to point their smartphone camera at text and have it live-translated into 88 different languages with the help of Google Translate. Android users can even point your camera at a beautiful flower, use Google Lens to identify what type of flower that is, and then set a reminder to order a bouquet for someone. Google Lens is able to use computer vision models to expand and speed up web search and mobile experience.
AntWorks is a global, artificial intelligence (AI) and intelligent automation (IA) company that creates new possibilities with data through digitization, automation, and enterprise intelligence. As the world's only integrated intelligent automation platform (IAP), powered by fractal science principles and pattern recognition, ANTstein digitises every type of data for forward-thinking companies looking to achieve straight-through processing. Asheesh Mehra, Co-founder and Group CEO, AntWorks, tells us more. Asheesh Mehra: Today, automation of business processes has moved from being a nice-to-have to a necessity. Analyst firm, Forrester, predicted that business process automation can cut operating costs by up to 90%.
Robert Williams spent over a day in custody in January after face recognition software matched his driver's license photo to surveillance video of someone shoplifting, the American Civil Liberties Union of Michigan (ACLU) said in the complaint. In a video shared by ACLU, Williams says officers released him after acknowledging "the computer" must have been wrong.
Google has said that it will begin fact-checking images that appear from its search results. Starting today, a'Fact Check' label will start appearing under thumbnails. Clicking on the thumbnail will show a quick summary of the fact check, including the claim and a rating from a fact-checker such as Politifact. This tool is organised using ClaimReview, which is a method used by publishers to indicate fact-checked content to search engines, which are already used by Google Search and Google News. Fact-checkers have to meet Google's criteria before they can be used as the source.
The news: An open letter from a growing coalition of AI researchers is calling out scientific publisher Springer Nature for a conference paper it reportedly planned to include in its forthcoming book Transactions on Computational Science & Computational Intelligence. The paper, titled "A Deep Neural Network Model to Predict Criminality Using Image Processing," presents a face recognition system purportedly capable of predicting whether someone is a criminal, according to the original press release. It was developed by researchers at Harrisburg University and was due to be presented at a forthcoming conference. The demands: Citing the work of leading Black AI scholars, the letter debunks the scientific basis of the paper and asserts that crime-prediction technologies are racist. It also lists three demands: 1) for Springer Nature to rescind its offer to publish the study; 2) for it to issue a statement condemning the use of statistical techniques such as machine learning to predict criminality and acknowledging its role in incentivizing such research; and 3) for all scientific publishers to commit to not publishing similar papers in the future.