Abstract:
A resource resettable deep neural network accelerator according to an embodiment of the present disclosure includes: a memory layer including a scratchpad memory layer configured to divide deep neural network parameter data (hereinafter, data) in an external memory layer into a plurality of tiles and to load the divided tiles, and a register file memory layer configured to load tiled data of the scratchpad memory layer; and a plurality of cores configured to process an inference operation for the data loaded in the register file memory layer, wherein the memory layer includes a virtual tiling layer added to a certain location for loading the tiled data from a previous memory layer so as to correspond to a specific tiling size.
Abstract:
Disclosed is a method of reconstructing a three-dimensional color mesh and an apparatus for the same. According to an embodiment of the present disclosure, the method includes: receiving mesh information of an object, multiple multi-view images obtained by photographing the object at different positions, and camera parameter information corresponding to the multiple multi-view images; constructing a texture map with respect to the object on the basis of the received information and setting a texture patch referring to a color value of the same multi-view image; correcting a color value of a vertex included for each texture patch; and performing rendering with respect to the object by applying the corrected color value of the vertex to the texture map.
Abstract:
Provided is a method and apparatus for registering metadata regarding a drone image. The apparatus for registering metadata regarding a drone image acquires captured data, and parses metadata from the captured data. The apparatus for registering metadata regarding a drone image generates new metadata using additional information and the parsed metadata, and registers the generated new metadata and the captured data to generate new captured data.
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:
A method and apparatus for generating agricultural semantic image information are provided to manage crop image information and agricultural environmental information as an object in an integrated manner. The apparatus generates agricultural semantic image information through these steps: receiving primary agricultural environmental information from each sensor of an agricultural environment semantic adaptation system; generating secondary agricultural environmental information based on the primary agricultural environmental information; receiving image information and still lo images from imaging means of the agricultural environment semantic adaptation system; extracting growth state information from the still images; and recording the primary agricultural environmental information, secondary agricultural environmental information, and growth state information as metadata for the image information.
Abstract:
An apparatus for generating a 3-dimensional face model includes a multi-view image capturer configured to sense a motion of the mobile device and automatically capture still images from two or more directions; and a 3D model generator configured to generate a 3D face mode using the two or more still images obtained by the multi-view image capturer.
Abstract:
Provided is an adaptive greenhouse control method that can control a greenhouse to provide an optimized environment by automatically and daily adapting to an external environment and a type of the greenhouse. The method includes performing a P-Band setting operation of setting a P-Band to determine a degree of which a greenhouse window is opened according to a current greenhouse inside temperature based on a predetermined set temperature, performing a greenhouse control operation of controlling the degree of which a greenhouse window is opened according to the P-Band, performing a greenhouse environment parameter measurement operation of measuring a greenhouse environment parameter value applied to set the P-Band, performing a P-Band changing operation of changing the P-Band according to the greenhouse environment parameter value, and performing a greenhouse change control operation of controlling the degree of which a greenhouse window is opened according to the P-Band changed in the P-Band changing operation.