GRATF 2.0.1.0 Crack+ License Code & Keygen [32|64bit] 2022
Goal
GRATF is to provide a library which does localization, recognition and pose estimation of optical glyphs in still images and video files.
The library can be used in robotics applications for example, where glyphs may serve as commands or directions to robots. However, most popular application of optical glyph recognition is augmented reality.
The project includes:
· Glyph recognition and pose estimation library, which is an extension to AForge.NET framework. The library can be reused in other applications which require glyph recognition functionality.
· Glyph recognition prototype, which is implemented as a plug-in to IPPrototyper tool from AForge.NET framework. Can be used for demonstrating glyph recognition algorithm's idea step by step or for its further improvement, debugging and testing. Simply open a folder containing images with glyphs (sample images are provided) and see steps of glyph recognition algorithm.
· Glyph Recognition Studio - an application which does recognition of glyphs in video streams (video files or local capture devices, like web cameras). Also the application does 2D and 3D augmented reality - puts images into video feed replacing recognized glyphs or puts 3D models on top of recognized glyphs using same pose as real glyphs have - virtual objects in real world. Just define some glyphs in the Glyph Recognition Studio, print them from the application and see how it works with your web camera.
State
The project is under active development.
Current development status is:
· Glyph recognition and pose estimation library is ready for usage.
· Glyph recognition prototype (located in IPPrototyper tool's repository) has done a lot of demonstration and tests.
· Web camera testing is done and results are good but needs further improvement.
Research and Testing Methodology
A significant part of research and testing has been done using open source IPPrototyper tool.
Results
The following experiments have been performed using IPPrototyper tool.
· 2D Localization: localize glyphs in certain area of image with fixed distance to camera (using circle or square around the glyph). Tried to make a small 2D localizer (which would be used for mini version of Web Camera project).
· 2D Recognition: recognize glyphs in image and compare the recognized glyph with reference glyph. Results have been good enough, many glyphs can be recognized.
· 3D Recognition: recognize glyphs in image and in real world, mark the detected
GRATF 2.0.1.0 Activation Code With Keygen Download For Windows
The project is building a library which does localization, recognition and pose estimation of optical glyphs in still images and video files.
Note: The project does not contain any extra 3D models or application software, which is independent from libraries. It does only glyphy recognition.
Vision is one of the most important human senses. In Augmented Reality (AR) we use our eyes to provide an additional input into our environment. We look at a scene, the camera of a phone for example, and point a digital camera at that scene. The phone's camera then identifies glyphs in that scene and feeds them to a graphic engine which uses the knowledge about the glyphs to render a VR experience for us.
GRATF uses an open architecture approach to glyph recognition. The camera does the thinking, the computer does the heavy lifting.
The glyphs are modeled as image manipulations which can be freely combined and recombined. At the same time, the system is designed to be widely scalable and can be used to recognize glyphs in sequences of images or video streams.
As for the look, we're aiming for a natural, abstract style. Some glyphs can be very simple (curly edges, for example), and some can be very complex (placing a virtual 3D object for example).
The process we have used to create the glyphs is to simply take an image and create a new image where the original image has been replaced by an image that looks like a glyph.
The key to the project is that the camera is smart enough to know what glyphs it sees, and where. So with just a single image (the thing the camera is pointing at), and the glyph knowledge, the computer is able to draw and place all the glyphs.
In practice we do not see a 3D model of the glyphs as we experience them. We simply see an image of the glyph and the computer uses that knowledge to render a 2D model from the 3D model. In this way, it is possible to create virtual 3D objects on top of real world objects.
Currently the library does:
· Recognize text in photo files in PDF and JPG formats (if image contains text).
· Recognize hand-written text in any image.
· Recognize text in Web page and static images.
· Recognize text in video files, including streaming video.
· Recognize 3D objects, including text in 3D!
· Recognize text in arbitrary C#
f0d5c1b395

Related

Items