Abstract:
A data learning device in a deep learning network characterized by a high image resolution and a thin channel at an input stage and an output stage and a low image resolution and a thick channel in an intermediate deep layer includes a feature information extraction unit configured to extract global feature information considering an association between all elements of data when generating an initial estimate in the deep layer; a direct channel-to-image conversion unit configured to generate expanded data having the same resolution as a final output from the generated initial estimate of the global feature information or intermediate outputs sequentially generated in subsequent layers; and a comparison and learning unit configured to calculate a difference between the expanded data generated by the direct channel-to-image conversion unit and a prepared ground truth value and update network parameters such that the difference is decreased.
Abstract:
Disclosed is a method of performing geometric correction on images obtained by multiple cameras and an image obtainment apparatus therefor, the method including: receiving the images obtained by the multiple cameras; extracting feature points of the received images and checking an interrelation between the extracted feature points; estimating locations and directions of the cameras, which respectively obtain the images, by using the interrelation between the feature points; calculating a relative geometric relationship between the multiple cameras from the estimated locations and directions of the cameras when the received images are determined as the images obtained by the multiple cameras with a fixed geometric interrelationship; and performing the geometric correction on each of the images by using the calculated geometric relationship. The method of efficiently performing geometric correction according to the embodiment of the present invention enables images to be obtained easily using the unmanned device with a short photographing time.
Abstract:
Provided is a system and method for providing a stereoscopic image by adjusting a depth value, the system including a depth value estimator to estimate a depth value of an object included in a first stereoscopic image from the stereoscopic image, a depth value adjusting unit to adjust the depth value in consideration of a display device, a stereoscopic image processing unit to process the first stereoscopic image to be a second stereoscopic image based on the adjusted depth value, and a stereoscopic image provider to provide the second stereoscopic image to the display device.
Abstract:
Disclosed are a system for producing stereoscopic images with a hole filling algorithm, and a method thereof. A system for producing stereoscopic images according to an exemplary embodiment of the present invention includes: a movement area detecting unit examining a movement area for an object moved in an image, detecting/tracking changes of the movement area by dividing a front view and a background of the movement area, and providing result information; a filling error processing unit filling a hole detected by the movement area detecting unit and correcting a filled filling region; and a stereoscopic image visualizing unit visualizing a stereoscopic image corrected by the filling error processing unit.