Abstract: A substrate transfer system capable of performing efficient distribution exchange between fabricating facilities is provided. The substrate transfer system includes a lower rail, an upper rail which is located above the lower rail from a ground plane, and extends to be parallel to the lower rail, a conveyor which extends to intersect the lower rail and the upper rail, below the lower rail, a first lower transport unit which transports a first carrier along the lower rail and unloads the first carrier onto the conveyor, and a first upper transport unit which transports a second carrier along the upper rail and unloads the second carrier onto the conveyor, wherein the conveyor includes a linear module which moves the first carrier and the second carrier in a linear direction, and a turning module which turns the first carrier and the second carrier.

Abstract: The present disclosure relates to a method and apparatus for constructing a network adaptable to consecutive/complex domains. An apparatus for constructing a domain adaptive network according to an embodiment of the present disclosure includes a memory configured to store data; and a processor configured to control the memory, wherein the processor is configured to determine a weight to be applied to one or more neural networks based on input data, construct a final neural network by applying the weight to the one or more neural networks, and output result data of the input data using the final neural network, wherein the one or more neural networks are trained using data for each prototype domain.

Abstract: Provided is an apparatus for classifying an attribute of an image object, including: a first memory configured to store target object images that are indexed; a second memory configured to store target object images that are un-indexed; and an object attribute classification module configured to perform learning on the un-indexed target object images to construct a classifier for classifying a detailed attribute of target object, and finely adjust the classifier on the basis of the indexed target object images.

Abstract: Disclosed herein are a method and apparatus for processing feature information based on an artificial neural network. According to an embodiment of the present disclosure, the apparatus for processing feature information based on an artificial neural network may include a memory for storing data and a processor for controlling the memory, and the processor may further be configured to extract a graph, which includes vertices, based on a feature map of an image, to extract a feature vector corresponding to the vertices and to process the graph and the feature vector based on an artificial neural network, and the graph may include positions of the vertices and information on a connection relationship between the vertices.

Abstract: A domain adaptation-based object recognition apparatus includes a memory configured to store a domain adaptation-based object recognition program and a processor configured to execute the program. The processor learns a generative model for generating a feature or an image similar to a gallery image on the basis of domain adaptation in association with an input probe image and learns an object recognition classification model by using a learning database corresponding to the gallery image and the input probe image, thereby performing object recognition using the input probe image.

Abstract: Provided is an apparatus for classifying an attribute of an image object, including: a first memory configured to store target object images that are indexed; a second memory configured to store target object images that are un-indexed; and an object attribute classification module configured to perform learning on the un-indexed target object images to construct a classifier for classifying a detailed attribute of target object, and finely adjust the classifier on the basis of the indexed target object images.

Abstract: A domain adaptation-based object recognition apparatus includes a memory configured to store a domain adaptation-based object recognition program and a processor configured to execute the program. The processor learns a generative model for generating a feature or an image similar to a gallery image on the basis of domain adaptation in association with an input probe image and learns an object recognition classification model by using a learning database corresponding to the gallery image and the input probe image, thereby performing object recognition using the input probe image.

Abstract: Provided is a dynamic object detecting technique, and more specifically, a system and method for determining a state of a motion of a camera on the basis of a local motion estimated on the basis of a video captured by a dynamic camera and a result of analyzing a global motion, flexibly updating a background model according to the state of the motion of the camera, and flexibly detecting a dynamic object according to the state of the motion of the camera.

Abstract: Smart glasses for selectively tracking a target of visual cognition according to the present invention include a first camera configured to capture a first input image that is a first-person view image of a user, a second camera configured to capture a second input image containing sight line information of the user, a display configured to output additional information corresponding to the first input image, a memory configured to store a program for selectively tracking a target of visual cognition on the basis of the first and second input images, and a processor configured to execute the program stored in the memory, wherein upon executing the program, the processor is configured to detect the target of visual cognition from the first input image and determine, from the second input image, whether the user is in an inattentive state with respect to the target of visual cognition.

Abstract: Provided is a dynamic object detecting technique, and more specifically, a system and method for determining a state of a motion of a camera on the basis of a local motion estimated on the basis of a video captured by a dynamic camera and a result of analyzing a global motion, flexibly updating a background model according to the state of the motion of the camera, and flexibly detecting a dynamic object according to the state of the motion of the camera.

Abstract: Image capture data is output from an image capture device, e.g., a CCD, at a commanded frame rate. A succession of fields that occur at a field rate are identified. A commanded frame rate that is less than or equal to the field rate is received. Read signals are applied to the image capture device for selected fields of the succession of fields to thereby output image data at the commanded frame rate. The image capture device is shuttered for a shutter interval based on the commanded frame rate to prevent image capture during the shutter interval. According to aspects of the invention, the commanded frame rate is selected such that the field rate is an integer multiple of the commanded frame rate. An image capture apparatus according to the present invention includes an image capture device configured to receive read signals and operative to output image data responsive thereto. A timing generator is operative to produce a timing signal that define a succession of fields that occur at a field rate.