Therefore, considering the given statistics, there will be a promising rise in the need for Computer Vision engineers. According to a report presented by ‘Research and Markets’, the value of AI in the Computer Vision market is estimated to increase by USD 35.4 billion in the next five years from the value of USD 15.9 billion in 2021. Some more interesting examples are listed below.Ĭomputer Vision is also one of the most demanding domains in AI. Consider saving someone’s life on time when a person has been diagnosed with a fatal disease or being able to get those perfect fitting clothes without actually trying them sounds interesting, right? Well, all of this is possible with the use of Computer Vision. So, presently, why is Computer Vision one of the most crucial domains of AI? The reason being, it has a tremendous number of applications relevant in day to day life across multiple diverse industries. With the advent of more excellent computational capabilities of machines and broader access to data, AI became prominent after the 1980s. Image classification of cats was one of the initial challenging problems which researchers tackled in AI. Shneiderman B, Plaisant C (2004) Designing the User Interface: Strategies for Effective Human-Computer Interaction (4th Edition).Computer Vision as a field, in its primitive form, arose right in the early 1960s after the term Artificial Intelligence (AI) was coined at the Dartmouth conference. Shand SJ (1916) A recording micrometer for rock analysis. Verhandlungen der Kaiserlich-Königslich geologischen Reichsanstalt 143–175 Rosiwal A (1898) Über geometrische gesteinsanalysen, ein einfacher weg zur ziffermässigen festellung des quantitätsverhältnisses der mineralbestandtheile egmengter gesteine. Mock K, Amato J, Bertmaring J (2012) GeoPixelCounter (Version 1.0). Medyckyj-Scott D, Hearnshaw HM (1993) Human Factors in Geographical Information Systems.
JMICROVISION TUTORIAL SOFTWARE
Lamprecht MR, Sabatini DM, Carpenter AE (2007) Cell Profiler™: free, versatile software for automated biological image analysis. Berkshire Encyclopedia of Human-Computer Interaction. Higgins M (2000) Measurement of crystal size distributions. A semiautomated image analysis technique. Gatlin CL, Schaberg ES, Jordan WH, Kuyatt BL, Smith WC (1993) Point counting on the Macintosh. Am Mineral 34:1–11ĭelesse MA (1848) Procédé méchanique pour determiner la composition des roches. Morgan Kaufmann Publishers IncĬhayes F (1949) A simple point counter for thin section analysis. We compare the time it takes to complete a session of point counting and the tools' usability.Ĭard SK, Mackinlay JD, Shneiderman B (1999) Readings in information visualization: using vision to think. A comparison between this application and similar tools through users' experiences is provided. It provides visual tools like distortion techniques, overview + detail, and statistics to assist the technique. In this contribution, we introduce an interactive visualization application called Rock.AR that reduces the amount of time required to apply this technique and simplify its work flow. Point counting in thin sections is normally conducted through mechanical or electromechanical devices attached to a microscope such devices are very expensive, offer limited functionality, and are very time consuming. Although point counting is very time consuming, it is of common use in several domains including geology, biology, medicine, and materials sciences, among others. Conventionally, this quantification has been determined by point counting performed on thin sections. An example of this is the petrographic study of rock samples, i.e., the quantification of its mineral components and the percentages of each phase present in it. In the field of geology, as in many other fields, a common problem is the analysis of 2D sections.
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