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Determining whether an image is of a given kind of natural object is far more difficult. This task falls into the area of {{w|computer vision}}. One of the goals in computer vision is to detect and classify objects within an image. This is a very challenging task for a number of reasons.
 
Determining whether an image is of a given kind of natural object is far more difficult. This task falls into the area of {{w|computer vision}}. One of the goals in computer vision is to detect and classify objects within an image. This is a very challenging task for a number of reasons.
  
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Firstly, humans use size, edge-assignment, movement, and stereoscopic vision when looking at a scene (not a picture of a thing, but the thing itself) to discern individual objects and then {{w|Figure-ground (perception)|categorize them as foreground or background}}. A photograph, however, is a static, monoscopic image that can only provide size and edge-assignment clues. Humans are only able to discern objects from background in photographs by comparing the photo against all of the things they've seen and everything they've learned about those things over the course of their life and {{w|Visual perception|identifying matching patterns}}.
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Firstly, humans use size, edge-assignment, movement, and stereoscopic vision when looking at a scene (not a picture of a thing, but the thing itself) to discern individual objects and then categorize them as foreground or background.<ref>{{w|Figure-ground_(perception)}}</ref> A photograph, however, is a static, monoscopic image that can only provide size and edge-assignment clues. Humans are only able to discern objects from background in photographs by comparing the photo against all of the things they've seen and everything they've learned about those things over the course of their life and identifying matching patterns.<ref>{{w|Visual_perception}}</ref>
  
 
Secondly, the quality of the photograph will have an impact on a computer's ability to match patterns. For example, the object in the photograph might be partially visible or occluded. In the case of a living bird, additional complications arise from the variations among 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 it a photo of a bird in flight or a plane <s>(or superman!)</s>? Ponytail's estimate of 5 years may be overly optimistic (see [[678: Researcher Translation]]).
 
Secondly, the quality of the photograph will have an impact on a computer's ability to match patterns. For example, the object in the photograph might be partially visible or occluded. In the case of a living bird, additional complications arise from the variations among 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 it a photo of a bird in flight or a plane <s>(or superman!)</s>? Ponytail's estimate of 5 years may be overly optimistic (see [[678: Researcher Translation]]).

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