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Techniques for determining settings for a content capture device

專利號
US11968456B2
公開日期
2024-04-23
申請人
Magic Leap, Inc.（US FL Plantation）
發(fā)明人
Brian Keith Smith; Ilya Tsunaev
IPC分類
H04N23/72; H04N5/222; H04N5/265; H04N23/63; H04N23/71; H04N23/73; H04N23/741; H04N23/743; H04N23/76
技術(shù)領(lǐng)域
luma,pixel,image,may,in,be,aec,examples,object,weight
地域： FL FL Plantation

摘要

A method for computing a total weight array includes receiving an image frame captured by a content capture device and identifying a plurality of objects in the image frame. Each object of the plurality of objects corresponds to one of a plurality of pixel groups. The method also includes providing a plurality of neural networks and calculating, for each object of the plurality of objects, an object weight using a corresponding neural network of the plurality of neural networks. The method further includes computing the total weight array by summing the object weight for each of the plurality of objects.

說明書

The eye gaze weight may indicate where a user is looking relative to the object. A value for the eye gaze may be one of an enumerated set (e.g., “at the vector,” “near the vector,” or “out of the vector”). In some examples, the values for the enumerated set may be predefined (e.g., if the eye gaze is a second distance (greater than the first distance) from an object, the eye gaze is “out of the vector” and receives a weight of 0.5; if the eye gaze is a first distance away from an object, the eye gaze is “near the vector” and receives a weight of 0.75; and if the eye gaze is toward an object, the eye gaze is “at the vector” and receives a weight of 1).

Referring to FIG. 9, a first neural network for a first object (910) may be used. For the first neural network, each weight described above for the first object may be an input of inputs 920. For example, the object priority weight may be an exposure weight parameter 1 (922), the object size weight may be an exposure weight parameter 2 (924), and the eye gaze weight may be an exposure weight parameter n (926). It should be recognized that any number of weights may be used as the inputs 920.

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