Posts Tagged ‘AR’

Augmented Reality in education

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            Imagine how those boring history lessons at school would be if the children would watch an augmented view of the lesson, like a 3D Solar System Model or Galileu talking directly to them, and they were able to interact with this augmented information. Children learn better the lesson if they see it like a game.

Image 1. An example of learning without AR and with AR.

               Although using Augmented Reality can drive to fun learning, its use is not restricted to children, but can also be used in more advances levels, where the presence of an AR System can provide a very useful tool for the student.

             For instance, in mathematics and geometry the main advantage of using an AR system is that students actually see three dimensional objects which previously they had to calculate and construct with traditional methods, like pen and paper. Instead of working with such old methods, it is better working working directly in 3D space. As a result, complex spatial problems may be comprehended better and faster, as well as spatial relations. A research on it was carried out by Kaufmann and Schmalstieg who published their results in the study “Mathematics and Geometry Education with Collaborative Augmented Reality”. In this research, Kaufmann and Schmalstieg investigated how an AR system improved spatial abilities and transfer of learning of math and geometric objects to the students.

           We can find another example of an AR system developed for educational purpose in “Augmented Reality for teaching spatial relations”,  study carried out by P.Maier, M. Tönnis and G. Klinker in 2009. In this paper, the authors describe how the Augmented Reality can explain in an easy way the spatial relations of the molecules and their spatial interactions and chemical reactions. Furthermore, the use of Augmented Reality can help to scientists to speed up the process of designing new molecules.

Figure 2. AR in medicine, where an “error” could drive to a disaster.   


Computer Vision for Augmented Reality

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Augmented Reality (AR) is a exponentially growing area in the so-called Virtual Environment (VE) field. While VE, or Virtual Reality (VR), provides a complete immersive experience into a fully synthetic scenario, where the user cannot see and interact with the real surroundings, AR allows to see the real world, placing virtual objects or superimposing virtual information. In other words, AR does not substitute reality, but integrates and supplements it. It takes the real objects as a foundation to add contextual information helping the user to deep his understanding about the subject. The potential application domains of this technology are vast, including medical, education and training, manufacturing and repair, annotation and visualization, path planning, gaming and entertainment, military. The related market is exploding: according recent studies, the installed base of AR-capable mobile device has grown from 8 million in 2009 to more than 100 million in 2010, producing a global revenue that is estimated to reach $1.5 billion by 2015.

Figure 1. Different AR scenarios

But, besides the final AR tangible (or visible) results, and market predictions, one can ask: what is the enabling technology that effectively allows augmenting our reality? In other words, beyond the virtual information rendered, how can the device, or the application, be aware of the world and select the appropriate content to present to the user?

From a generic point of view, this task is far from being trivial: in fact, while for a human being the surrounding understanding is somehow unconsciously and easily reached in almost all the scenarios in fractions of second, for a computer-based machine the things are way more complicated. What is done in the practice is to constrain the scenario and sense the world status through multi-modal sensors (i.e., images, videos, sounds, inertial sensors): the discrete information flow is then fused, merged, and processed by so-called Artificial Intelligence (AI) algorithms that try to give a plausible explanation to the provided data. 

Very close to AI, often intersecting and relying on it, and strongly related to AR application development is the Computer Vision. In fact, since the main cue for the actual AR systems is the artificial vision, this field has gained increasing importance in the AR context. As AI aims at the surroundings understanding relying on generic low-level sensor data, Computer Vision is concerned with duplicating, or emulating the capabilities of the Human Vision System (HVS) by reconstructing, and interpreting, a 3D scene only from its 2D projections, or images. Although it may seem simple, any task related to Computer Vision can become arbitrarily complex: this is due to the intrinsic nature of the problem, so-called ill-posed inverse. In other words, a 3D scene understanding has to be reached from its 2D projections, in fact losing one spatial dimension. Furthermore, given the AR interactivity constraints, the tasks have to be performed in real-time, or near real-time.

Figure 2. Typical Computer VIsion processing pipeline


Markerless Image Tracking: recursive tracking techniques

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          As described in previous entries using markers to perform a tracking presents more disadvantages than using the object itself as a target to be tracked. Some of those disadvantages are the need to print the marker or that the tracking can fail due to occlusions. Also, these markers are invasive to the environment, using marketing expression, “do not keep the packaging clean”.

      Due to these reasons, many researchers and companies have focused on developing markerless tracking systems instead of marker-based tracking systems. The former will be the subject of this entry.

    Figure 1. Online Monocular techniques scheme.

      Techniques developed for online monocular markerless augmented reality systems can be classified into two sub-branches: model based and Structure from Motion (SfM) based. The difference is that while in the former a previous knowledge about the real world before the tracking is performed is required, in the later this knowledge is acquired during the tracking. Inside these two sub-branches two different approaches can be taken into consideration according the nature of the tracking. The first of them, known as recursive tracking, uses the previous known pose to estimate the current one. The second option, which is called tracking by detection, allows to calculate the pose estimation without any previous knowledge or estimation, which can be better for recovering from failures.

      Furthermore, the model based approaches which use a recursive tracking can be classified in three branches or categories: edge based, optical flow based and textured based. In the other hand, the approaches covered by tracking by detection techniques are: edge based techniques and texture-based techniques. Although techniques based on tracking by detection seem to be a better option, several things have to be taken into consideration before to choose any of them in order to select the option which fits our requirements, like frame rate, accuracy or even object tracked.


Markerless Augmented Reality

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          Image or object recognition and 3D Object Tracking in Augmented Reality is not a new concept and is, or has been, already enabled mainly by visual markers.Visual markers have been widely used in existing AR applications in last years. In most of these applications, the performance of an AR system depends highly on the tracking method for visual marker detection,  pose estimation, and so depending on the particular application. The visual marker’s design can differ from one to another. But the use of these visual markers limit the interactivity and are constrained to a range of photos or objects encapsulated within a border to create the marker. Therefore, in order to use this approach, these visual marks have to be printed previously and also be kept for future uses. Unlike in the marker-based Augmented reality systems, in markerless augmented reality systems any part of the real environment may be used as a target that can be tracked in order to place virtual objects.

      An example of AR using visual markers.

          With the new advances in mobile technologies, both in hardware and software, new markerless approaches like the ones based on natural features, broke into the Augmented Reality world, not only allowing to use real objects as a target instead of these old and ugly markers, but also overcome some of their limitations.

         In order to perform the object tracking, markerless augmented reality systems rely in natural features instead of fiducial marks. Therefore, there are no ambient intrusive markers which are not really part of the environment. Furthermore, markerless augmented reality counts on specialized and robust trackers already available. Another advantage of the markerless systems is the possibility of extracting from the environment characteristics and information that may later be used by them. However, among the disadvantages we can consider for markerless augmented reality systems is that tracking and registration techniques become more complex.

An example of Markerless AR (MAR).

Augmented Reality: Future, Present or any of them?

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       Augmented Reality is becoming popular amongst CV researchers, marketers, final users and even investors. However, one question remains unsolved. Is this technology enough developed to be considered as today’s technology? or does it still need deeper developments to be ready for normal use? One thing is clear, the present or the very close future of Augmented Reality is far away from the “Minority Report” screens seen in movies.

       Despite the fact some people think Augmented Reality is a technology targeted to the future, the current mobile processors already allow AR to be used, breaking the idea of AR being a closed technology which can only be applied successfully in a very controlled environment crowded of constraints. Due to these powerful processors Computer Vision algorithms which previously could be only run in Personal Computers now, can run on smartphones and Tablets, and they pass Augmented Reality to reality in current devices.

       It is true that there is still a huge world to be discovered, but the Computer Vision’s branches ported to Augmented Reality such as Image Recognition, Image Tracking or Object Tracking, or the latest developments in Augmented Reality Browsers are allowing AR to break into current applications with a good response from the final user. However, there are still some people that think Augmented reality is not still useful because users are limited to have a smartphone or a tablet and they must maintain specific constraints meanwhile the application is running, as could be seen in the last Augmented Reality Event. I personally agree up to a point with previous assertion. Playing a whole game or seeing a whole movie can be uncomfortable for the user to keep almost quite, or focusing to one point for a long time, but this can be thought as next steps to be improved in the field.