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Multocular image pickup apparatus and multocular image pickup method

A multocular image pickup apparatus includes: a distance calculation unit that calculates information regarding the distance to a captured object from an output video of a first image pickup unit, which is to be the reference unit of a plurality of image pickup units that pick up images, and from an output video of an image pickup unit that is different from the first image pickup unit; a multocular video synthesizing unit that generates synthesized video from the output video of the plurality of image pickup units based on the distance information for regions where the distance information could be calculated; and a monocular video synthesizing unit that generates synthesized video from the output video of the first image pickup unit for regions where the distance information could not be calculated. The distance calculation unit calculates a first distance information that is the distance to the captured object from the output video of the first image pickup unit and the output video of a second image pickup unit that is different from the first image pickup unit, and in case that there is a region where the first distance information could not be calculated, for the region where the first distance information could not be calculated, the distance calculation unit recalculates information regarding the distance to the captured object from the output video of an image pickup unit that was not used for calculating the distance among the plurality of image pickup units, and from the output video of the first image pickup unit.
Owner:SHARP KK

Image semantic division method based on depth full convolution network and condition random field

The invention provides an image semantic division method based on a depth full convolution network and a condition random field. The image semantic division method comprises the following steps: establishing a depth full convolution semantic division network model; carrying out structured prediction based on a pixel label of a full connection condition random field, and carrying out model training, parameter learning and image semantic division. According to the image semantic division method provided by the invention, expansion convolution and a spatial pyramid pooling module are introduced into the depth full convolution network, and a label predication pattern output by the depth full convolution network is further revised by utilizing the condition random field; the expansion convolution is used for enlarging a receptive field and ensures that the resolution ratio of a feature pattern is not changed; the spatial pyramid pooling module is used for extracting contextual features of different scale regions from a convolution local feature pattern, and a mutual relation between different objects and connection between the objects and features of regions with different scales are provided for the label predication; the full connection condition random field is used for further optimizing the pixel label according to feature similarity of pixel strength and positions, so that a semantic division pattern with a high resolution ratio, an accurate boundary and good space continuity is generated.
Owner:CHONGQING UNIV OF TECH
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