Depth image compression
First Claim
1. A computer-implemented method of compressing a depth image comprising probability information, the method comprising:
- receiving an image comprising a plurality of image elements having depth values and probability distributions over a plurality of classes representing the likelihood that the image elements are members of the classes;
for the image elements, calculating probability masses for the classes, the probability masses calculated based on probability values of the image elements being related to the classes multiplied by squares of the depth values of the image elements;
calculating, a plurality of output elements at lower resolution than the received image at least through aggregating the image elements on the basis of the probability masses; and
clustering output elements to find positions of centers of the classes.
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Accused Products
Abstract
Depth image compression is described for example, to enable body-part centers of players of a game to be detected in real time from depth images or for other applications such as augmented reality, and human-computer interaction. In an embodiment, depth images which have associated body-part probabilities, are compressed using probability mass which is related to the depth of an image element and a probability of a body part for the image element. In various examples, compression of the depth images using probability mass enables body part center detection, by clustering output elements, to be speeded up. In some examples, the scale of the compression is selected according to a depth of a foreground region and in some cases different scales are used for different image regions. In some examples, certainties of the body-part centers are calculated using probability masses of clustered image elements.
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Citations
20 Claims
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1. A computer-implemented method of compressing a depth image comprising probability information, the method comprising:
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receiving an image comprising a plurality of image elements having depth values and probability distributions over a plurality of classes representing the likelihood that the image elements are members of the classes; for the image elements, calculating probability masses for the classes, the probability masses calculated based on probability values of the image elements being related to the classes multiplied by squares of the depth values of the image elements; calculating, a plurality of output elements at lower resolution than the received image at least through aggregating the image elements on the basis of the probability masses; and clustering output elements to find positions of centers of the classes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus for compressing a depth image comprising:
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an input arranged to receive an image comprising a plurality of image elements having depth values and probability distributions over a plurality of classes representing the likelihood that the image elements are members of the classes; and a processor programmed to; calculate probability masses for the classes, the probability masses being probability values multiplied by squares of the depth values of the image elements, the probability values being related to probabilities of the classes for the image elements; calculate a plurality of output elements at lower resolution than the received image by aggregating the image elements on the basis of the probability masses; and cluster output elements to find positions of centers of the classes. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A computer-implemented method comprising:
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receiving an image comprising a plurality of image elements each having a depth value and a probability distribution over a plurality of body parts representing the likelihood that the image element is a member of each of the body parts; for each image element, calculating a probability mass for each body part, the probability mass being a probability value multiplied by the square of the depth value of the image element, the probability value being related to a probability of the body part for the image element; calculating, for each body part, a plurality of output elements at a lower resolution than the received image by aggregating the image elements on the basis of the probability mass; clustering output elements to find positions of centers of the body parts. - View Dependent Claims (18, 19, 20)
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Specification