Editing 1425: Tasks
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==Explanation== | ==Explanation== | ||
− | [[Cueball]] appears to be asking [[Ponytail]] to write an app that determines if a given picture is | + | {{incomplete|Explain why it is so difficult to identify a particular object in an image. Better background needed on title text.}} |
+ | [[Cueball]] appears to be asking [[Ponytail]] to write an app that determines if a given picture is of a bird in a national park. These two requirements, while seemingly about equally hard to a human, vary greatly in difficulty in computer science (CS). | ||
− | In order to determine whether the user is in a national park, Ponytail plans to determine the user's location using the mobile | + | In order to determine whether the user is in a national park, Ponytail plans to determine the user's location, presumably using Exif data from the uploaded image or some method of {{w|Mobile phone tracking|mobile phone tracking}}. This location will then be cross checked with a {{w|geographic information system}} (GIS) which will be able to determine whether the co-ordinates lie within a national park boundary. |
− | Determining whether an image is of a | + | Determining whether an image is of a particular object is far more difficult. This task falls into the area of {{w|Computer vision|computer vision}}. One of the goals in computer vision is to detect and classify objects within an image. This is a very challenging task since there is a large variability in the object's appearance and background composition. Furthermore, the object might only be partially visible or occluded. In the case of a living bird, additional complications arise from the variation between individual birds of the same species, and differences in pose (flying, perching in a tree, etc.). Differentiating between visually similar objects can result in false positives, for example is a photo of a bird in flight or a plane (or superman!). The task is especially difficult when the photo quality may be low. |
− | + | Today's state-of-the-art algorithms for solving this kind of task mostly use local features (e.g. {{w|Scale-invariant feature transform|SIFT}} or {{w|SURF}} in combination with a {{w|support vector machine}} or {{w|convolutional neural network}}). | |
− | + | The title text mentions [http://dspace.mit.edu/bitstream/handle/1721.1/6125/AIM-100.pdf The Summer Vision Project] from {{w|Marvin Minsky}} of MIT. In the summer of 1966, he asked his undergraduate student {{w|Gerald Jay Sussman}} to "spend the summer linking a camera to a computer and getting the computer to describe what it saw" ([http://szeliski.org/Book/]). The project schedule allocated one summer for the completion of this task. The required time was obviously significantly underestimated, since dozens of research groups around the world are still working on this topic today. | |
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− | The title text mentions [http://dspace.mit.edu/bitstream/handle/1721.1/6125/AIM-100.pdf The Summer Vision Project] | ||
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==Transcript== | ==Transcript== | ||
− | :[Ponytail sitting at a computer with Cueball standing behind her | + | :[Ponytail sitting at a computer with Cueball standing behind her] |
:Cueball: When a user takes a photo, the app should check whether they're in a national park... | :Cueball: When a user takes a photo, the app should check whether they're in a national park... | ||
:Ponytail: Sure, easy GIS lookup. Gimme a few hours. | :Ponytail: Sure, easy GIS lookup. Gimme a few hours. | ||
:Cueball: ...and check whether the photo is of a bird. | :Cueball: ...and check whether the photo is of a bird. | ||
:Ponytail: I'll need a research team and five years. | :Ponytail: I'll need a research team and five years. | ||
− | + | :In CS, it can be hard to explain the difference between the easy and the virtually impossible | |
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− | :In CS, it can be hard to explain the difference between the easy and the virtually impossible | ||
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{{comic discussion}} | {{comic discussion}} | ||
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